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librarian-bots

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Librarian Bots 90

Diya-999/Bart12-12V6.0

Diya-999

transformers

text2text-generation

--- license: afl-3.0 ---

huggingtweets/ejazaii

huggingtweets

transformers

text-generation

--- language: en thumbnail: https://www.huggingtweets.com/ejazaii/1626470409321/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1379529428808634376/6XrfvhAA_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Ejaz</div> <div style="text-align: center; font-size: 14px;">@ejazaii</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Ejaz. | Data | Ejaz | | --- | --- | | Tweets downloaded | 1273 | | Retweets | 134 | | Short tweets | 355 | | Tweets kept | 784 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/hci1jrbh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ejazaii's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a5d8p5i) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a5d8p5i/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ejazaii') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

ryuseiken/Ignacio_Copani

ryuseiken

--- license: openrail ---

mindw96/Qwen-2.5-3B-it-dacon-llm

mindw96

transformers

text-generation

--- base_model: Qwen/Qwen2.5-3B-Instruct library_name: transformers model_name: Qwen-2.5-3B-it-dacon-llm tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen-2.5-3B-it-dacon-llm This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="mindw96/Qwen-2.5-3B-it-dacon-llm", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/mindw96/huggingface/runs/mf9het0t) This model was trained with SFT. ### Framework versions - TRL: 0.13.0 - Transformers: 4.48.0 - Pytorch: 2.5.1+cu124 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Thousif/my-cat

Thousif

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Cat Dreambooth model trained by Thousif following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: .jpg) .jpg)

gustavomacedo/Llama3.1-8B-15EP-64BS

gustavomacedo

transformers

--- base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** gustavomacedo - **License:** apache-2.0 - **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ShenaoZhang/0.001_optm_idpo_4iters_iter_1

ShenaoZhang

transformers

text-generation

--- license: mit base_model: ShenaoZhang/0.001_idpo_4iters_iter_1 tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - updated - original model-index: - name: 0.001_optm_idpo_4iters_iter_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 0.001_optm_idpo_4iters_iter_1 This model is a fine-tuned version of [ShenaoZhang/0.001_idpo_4iters_iter_1](https://huggingface.co/ShenaoZhang/0.001_idpo_4iters_iter_1) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

Melodyloves/Melodyooo

Melodyloves

--- license: openrail ---

RichardErkhov/INSAIT-Institute_-_BgGPT-Gemma-2-2.6B-IT-v1.0-awq

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) BgGPT-Gemma-2-2.6B-IT-v1.0 - AWQ - Model creator: https://huggingface.co/INSAIT-Institute/ - Original model: https://huggingface.co/INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0/ Original model description: --- library_name: transformers tags: - gemma2 - instruct - bggpt - insait license: gemma language: - bg - en base_model: - google/gemma-2-2b-it - google/gemma-2-2b pipeline_tag: text-generation --- # INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0  INSAIT introduces **BgGPT-Gemma-2-2.6B-IT-v1.0**, a state-of-the-art Bulgarian language model based on **google/gemma-2-2b** and **google/gemma-2-2b-it**. BgGPT-Gemma-2-2.6B-IT-v1.0 is **free to use** and distributed under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). This model was created by [`INSAIT`](https://insait.ai/), part of Sofia University St. Kliment Ohridski, in Sofia, Bulgaria. # Model description The model was built on top of Google’s Gemma 2 2B open models. It was continuously pre-trained on around 100 billion tokens (85 billion in Bulgarian) using the Branch-and-Merge strategy INSAIT presented at [EMNLP’24](https://aclanthology.org/2024.findings-emnlp.1000/), allowing the model to gain outstanding Bulgarian cultural and linguistic capabilities while retaining its English performance. During the pre-training stage, we use various datasets, including Bulgarian web crawl data, freely available datasets such as Wikipedia, a range of specialized Bulgarian datasets sourced by the INSAIT Institute, and machine translations of popular English datasets. The model was then instruction-fine-tuned on a newly constructed Bulgarian instruction dataset created using real-world conversations. For more information check our [blogpost](https://models.bggpt.ai/blog/). # Benchmarks and Results   We evaluate our models on a set of standard English benchmarks, a translated version of them in Bulgarian, as well as, Bulgarian specific benchmarks we collected: - **Winogrande challenge**: testing world knowledge and understanding - **Hellaswag**: testing sentence completion - **ARC Easy/Challenge**: testing logical reasoning - **TriviaQA**: testing trivia knowledge - **GSM-8k**: solving multiple-choice questions in high-school mathematics - **Exams**: solving high school problems from natural and social sciences - **MON**: contains exams across various subjects for grades 4 to 12 These benchmarks test logical reasoning, mathematics, knowledge, language understanding and other skills of the models and are provided at https://github.com/insait-institute/lm-evaluation-harness-bg. The graphs above show the performance of BgGPT 2.6B compared to other small open language models such as Microsoft's Phi 3.5 and Alibaba's Qwen 2.5 3B. The BgGPT model not only surpasses them, but also **retains English performance** inherited from the original Google Gemma 2 models upon which it is based. # Use in 🤗 Transformers First install the latest version of the transformers library: ``` pip install -U 'transformers[torch]' ``` Then load the model in transformers: ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( "INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0", torch_dtype=torch.bfloat16, attn_implementation="eager", device_map="auto", ) ``` # Recommended Parameters For optimal performance, we recommend the following parameters for text generation, as we have extensively tested our model with them: ```python from transformers import GenerationConfig generation_params = GenerationConfig( max_new_tokens=2048, # Choose maximum generation tokens temperature=0.1, top_k=25, top_p=1, repetition_penalty=1.1, eos_token_id=[1,107], do_sample=True ) ``` In principle, increasing temperature should work adequately as well. # Instruction format In order to leverage instruction fine-tuning, your prompt should begin with a beginning-of-sequence token `<bos>` and be formatted in the Gemma 2 chat template. `<bos>` should only be the first token in a chat sequence. E.g. ``` <bos><start_of_turn>user Кога е основан Софийският университет?<end_of_turn> <start_of_turn>model ``` This format is also available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python tokenizer = AutoTokenizer.from_pretrained( "INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0", use_default_system_prompt=False, ) messages = [ {"role": "user", "content": "Кога е основан Софийският университет?"}, ] input_ids = tokenizer.apply_chat_template( messages, return_tensors="pt", add_generation_prompt=True, return_dict=True ) outputs = model.generate( **input_ids, generation_config=generation_params ) print(tokenizer.decode(outputs[0])) ``` **Important Note:** Models based on Gemma 2 such as BgGPT-Gemma-2-2.6B-IT-v1.0 do not support flash attention. Using it results in degraded performance. # Use with vLLM Example usage with vLLM: ```python from vllm import LLM, SamplingParams from vllm.inputs import TokensPrompt from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0", use_default_system_prompt=False, ) sampling_params = SamplingParams( max_tokens=2048, temperature=0.1, top_k=25, top_p=1, repetition_penalty=1.1, stop_token_ids=[1, 107], ) llm = LLM( model="INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0", dtype="bfloat16", enforce_eager=True ) messages = [ {"role": "user", "content": "Кога е основан Софийският университет?"}, ] formatted_prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) input_ids = tokenizer( formatted_prompt, add_special_tokens=False ).input_ids prompt = TokensPrompt(prompt_token_ids=input_ids) output = llm.generate( prompt, sampling_params ) generated_text = output[0].outputs[0].text print(generated_text) ``` # Use with GGML / llama.cpp The model and instructions for usage in GGUF format are available at [INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0-GGUF](https://huggingface.co/INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0-GGUF). # Community Feedback We welcome feedback from the community to help improve BgGPT. If you have suggestions, encounter any issues, or have ideas for improvements, please: - Share your experience using the model through Hugging Face's community discussion feature or - Contact us at [[email protected]](mailto:[email protected]) Your real-world usage and insights are valuable in helping us optimize the model's performance and behaviour for various use cases. # Summary - **Finetuned from:** [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it); [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b); - **Model type:** Causal decoder-only transformer language model - **Language:** Bulgarian and English - **Contact:** [[email protected]](mailto:[email protected]) - **License:** BgGPT is distributed under [Gemma Terms of Use](https://huggingface.co/INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0/raw/main/LICENSE)

kejian/final-cond-10-0.01

kejian

transformers

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-cond-10-0.01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kejian/final-cond-10-0.01 This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-10-0.01', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/1wgqepja

onnx-community/dinov2-with-registers-giant

onnx-community

transformers.js

image-feature-extraction

--- library_name: transformers.js base_model: facebook/dinov2-with-registers-giant --- https://huggingface.co/facebook/dinov2-with-registers-giant with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

mouadenna/segformer-b0-finetuned-segments-pv_v1_normalized_p100_4batch

mouadenna

transformers

image-segmentation

--- license: other base_model: nvidia/segformer-b0-finetuned-ade-512-512 tags: - vision - image-segmentation - generated_from_trainer metrics: - precision model-index: - name: segformer-b0-finetuned-segments-pv_v1_normalized_p100_4batch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mouadn773/huggingface/runs/g4m4ysqz) # segformer-b0-finetuned-segments-pv_v1_normalized_p100_4batch This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the mouadenna/satellite_PV_dataset_train_test_v1 dataset. It achieves the following results on the evaluation set: - Loss: 0.0074 - Mean Iou: 0.8483 - Precision: 0.9169 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.001 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Precision | |:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:| | 0.0127 | 0.9989 | 229 | 0.0092 | 0.7982 | 0.8641 | | 0.0077 | 1.9978 | 458 | 0.0094 | 0.7871 | 0.8456 | | 0.006 | 2.9967 | 687 | 0.0067 | 0.8140 | 0.9089 | | 0.0051 | 4.0 | 917 | 0.0058 | 0.8358 | 0.8713 | | 0.0045 | 4.9989 | 1146 | 0.0059 | 0.8258 | 0.8761 | | 0.0042 | 5.9978 | 1375 | 0.0058 | 0.8415 | 0.9018 | | 0.0036 | 6.9967 | 1604 | 0.0051 | 0.8513 | 0.9049 | | 0.0038 | 8.0 | 1834 | 0.0062 | 0.8226 | 0.9256 | | 0.004 | 8.9989 | 2063 | 0.0057 | 0.8358 | 0.8913 | | 0.0035 | 9.9978 | 2292 | 0.0053 | 0.8485 | 0.9079 | | 0.0037 | 10.9967 | 2521 | 0.0059 | 0.8192 | 0.9056 | | 0.0038 | 12.0 | 2751 | 0.0054 | 0.8487 | 0.8921 | | 0.0033 | 12.9989 | 2980 | 0.0053 | 0.8541 | 0.9086 | | 0.0028 | 13.9978 | 3209 | 0.0055 | 0.8551 | 0.8985 | | 0.0026 | 14.9967 | 3438 | 0.0060 | 0.8483 | 0.9085 | | 0.0026 | 16.0 | 3668 | 0.0057 | 0.8495 | 0.9076 | | 0.0024 | 16.9989 | 3897 | 0.0058 | 0.8442 | 0.9083 | | 0.0038 | 17.9978 | 4126 | 0.0066 | 0.8113 | 0.8910 | | 0.0031 | 18.9967 | 4355 | 0.0062 | 0.8488 | 0.9108 | | 0.0026 | 20.0 | 4585 | 0.0058 | 0.8575 | 0.9126 | | 0.0024 | 20.9989 | 4814 | 0.0057 | 0.8580 | 0.9119 | | 0.0025 | 21.9978 | 5043 | 0.0059 | 0.8505 | 0.8957 | | 0.0031 | 22.9967 | 5272 | 0.0062 | 0.8472 | 0.9135 | | 0.0022 | 24.0 | 5502 | 0.0055 | 0.8598 | 0.9147 | | 0.0023 | 24.9989 | 5731 | 0.0058 | 0.8621 | 0.9090 | | 0.0023 | 25.9978 | 5960 | 0.0064 | 0.8498 | 0.9094 | | 0.0023 | 26.9967 | 6189 | 0.0067 | 0.8428 | 0.9137 | | 0.0021 | 28.0 | 6419 | 0.0063 | 0.8527 | 0.9076 | | 0.002 | 28.9989 | 6648 | 0.0065 | 0.8509 | 0.9187 | | 0.002 | 29.9978 | 6877 | 0.0074 | 0.8424 | 0.9179 | | 0.002 | 30.9967 | 7106 | 0.0065 | 0.8577 | 0.9116 | | 0.0019 | 32.0 | 7336 | 0.0067 | 0.8547 | 0.9141 | | 0.0019 | 32.9989 | 7565 | 0.0072 | 0.8519 | 0.9168 | | 0.0019 | 33.9978 | 7794 | 0.0067 | 0.8569 | 0.9148 | | 0.0019 | 34.9967 | 8023 | 0.0070 | 0.8544 | 0.9139 | | 0.0017 | 36.0 | 8253 | 0.0072 | 0.8510 | 0.9124 | | 0.0018 | 36.9989 | 8482 | 0.0081 | 0.8425 | 0.9164 | | 0.0017 | 37.9978 | 8711 | 0.0073 | 0.8512 | 0.9155 | | 0.0018 | 38.9967 | 8940 | 0.0073 | 0.8495 | 0.9164 | | 0.0018 | 39.9564 | 9160 | 0.0074 | 0.8483 | 0.9169 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1

Ellight/speecht5_finetuned_voxpopuli_nl

Ellight

transformers

text-to-speech

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] pipeline_tag: text-to-speech --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

pittawat/Reinforce-pixelcopter

pittawat

reinforcement-learning

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 39.30 +/- 29.67 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

avorozhko/ruDialoGpt3-medium-finetuned-context

avorozhko

transformers

text-generation

## Описание модели Этот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта). Окончание обучения - март 2022 года. Чатбот сделан на основе модели [Kirili4ik/ruDialoGpt3-medium-finetuned-telegram](https://huggingface.co/Kirili4ik/ruDialoGpt3-medium-finetuned-telegram) Теперь модель дообучена на основе 27000 анекдотов (14 эпох, скорость обучения в колабе 2-6 часов на эпоху) и умеет понимать контекст разговора. Однако контекст приходится ограничивать несколькими последними сообщениями потому что чем больше контекста тем медленнее модель работает, а контекст растет как снежный ком в процессе разговора. Инференс находится в [spaces](https://huggingface.co/spaces/avorozhko/funbot): Там с ботом можно поговорить. Контекст ограничен 10 последними сообщениями. Шутки бот выдает, но пока скорее случайно, чем намеренно. Однако разговор поддержать способен и даже немного развлечь. Так как это генерация текста, то на одну и ту же фразу бот всегда будет выдавать разные ответы. Также для определения качества данной модели использовалась кастомная метрика - угловое расстояния между эмбеддингами y_train и предикта. То есть мы взяли первый слой эмбеддинга модели и прогоняли предикты и лейблы, получили вектора слов. Потом вектора слов суммировали и получили общие (суммарные) вектора лейблов и предиктов. Чем меньше угол между ними, тем лучше. При рассчетах ориентировались на косинус этого угла, так как cos 0 = 1, то это очень удобно - чем ближе показатель к 1, тем лучше. Вот такое распределение этих значений получилось по эпохам на ПРОВЕРОЧНОЙ выборке (1406 анекдотов): ``` {1: tensor(0.9357, device='cuda:0', grad_fn=<DivBackward0>), 2: tensor(0.9390, device='cuda:0', grad_fn=<DivBackward0>), 3: tensor(0.9417, device='cuda:0', grad_fn=<DivBackward0>), 4: tensor(0.9439, device='cuda:0', grad_fn=<DivBackward0>), 5: tensor(0.9470, device='cuda:0', grad_fn=<DivBackward0>), 6: tensor(0.9537, device='cuda:0', grad_fn=<DivBackward0>), 7: tensor(0.9568, device='cuda:0', grad_fn=<DivBackward0>), 8: tensor(0.9592, device='cuda:0', grad_fn=<DivBackward0>), 9: tensor(0.9610, device='cuda:0', grad_fn=<DivBackward0>), 10: tensor(0.9622, device='cuda:0', grad_fn=<DivBackward0>), 11: tensor(0.9628, device='cuda:0', grad_fn=<DivBackward0>), 12: tensor(0.9632, device='cuda:0', grad_fn=<DivBackward0>), 13: tensor(0.9630, device='cuda:0', grad_fn=<DivBackward0>), 14: tensor(0.9634, device='cuda:0', grad_fn=<DivBackward0>), 15: tensor(0.9634, device='cuda:0', grad_fn=<DivBackward0>)} ``` Для инференса выбрана 14-я эпоха с точностью 0.9634. Далее, судя по всему идет уже переобучение.

Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-4_0bpw_exl2

Zoyd

transformers

text-generation

--- base_model: NousResearch/Hermes-2-Pro-Llama-3-8B tags: - Llama-3 - instruct - finetune - chatml - DPO - RLHF - gpt4 - synthetic data - distillation - function calling - json mode - axolotl - merges model-index: - name: Hermes-2-Pro-Llama-3-Instruct-8B-Merge results: [] language: - en datasets: - teknium/OpenHermes-2.5 widget: - example_title: Hermes 2 Pro Llama-3 Instruct Merge messages: - role: system content: >- You are a sentient, superintelligent artificial general intelligence, here to teach and assist me. - role: user content: >- Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world. license: apache-2.0 --- **Exllamav2** quant (**exl2** / **4.0 bpw**) made with ExLlamaV2 v0.0.21 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |<center>**[2.2](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-2_2bpw_exl2)**</center> | <center>3250 MB</center> | <center>6</center> | |<center>**[2.5](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-2_5bpw_exl2)**</center> | <center>3479 MB</center> | <center>6</center> | |<center>**[3.0](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-3_0bpw_exl2)**</center> | <center>3895 MB</center> | <center>6</center> | |<center>**[3.5](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-3_5bpw_exl2)**</center> | <center>4311 MB</center> | <center>6</center> | |<center>**[3.75](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-3_75bpw_exl2)**</center> | <center>4519 MB</center> | <center>6</center> | |<center>**[4.0](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-4_0bpw_exl2)**</center> | <center>4726 MB</center> | <center>6</center> | |<center>**[4.25](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-4_25bpw_exl2)**</center> | <center>4933 MB</center> | <center>6</center> | |<center>**[5.0](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-5_0bpw_exl2)**</center> | <center>5558 MB</center> | <center>6</center> | |<center>**[6.0](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-6_0bpw_exl2)**</center> | <center>6497 MB</center> | <center>8</center> | |<center>**[6.5](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-6_5bpw_exl2)**</center> | <center>6910 MB</center> | <center>8</center> | |<center>**[8.0](https://huggingface.co/Zoyd/NousResearch_Hermes-2-Theta-Llama-3-8B-8_0bpw_exl2)**</center> | <center>8049 MB</center> | <center>8</center> | # - Hermes-2 Θ Llama-3 8B  ## Model Description Hermes-2 Θ (Theta) is the first experimental merged model released by [Nous Research](https://nousresearch.com/), in collaboration with Charles Goddard at [Arcee](https://www.arcee.ai/), the team behind MergeKit. Hermes-2 Θ is a merged and then further RLHF'ed version our excellent Hermes 2 Pro model and Meta's Llama-3 Instruct model to form a new model, Hermes-2 Θ, combining the best of both worlds of each model. ## Example Outputs ### Create New Mythos:  ### Chat with a Meta-Cognitive Entity  ### Ask for a structured JSON output:  # Prompt Format Hermes 2 Θ uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <|im_start|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|> <|im_start|>user Hello, who are you?<|im_end|> <|im_start|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. ## Prompt Format for Function Calling Our model was trained on specific system prompts and structures for Function Calling. While the system prompt looks complicated, we have created a GitHub repo containing code to easily build these based on real python functions. You should use the system role with this message, followed by a function signature json as this example shows here. ``` <|im_start|>system You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call> {"arguments": <args-dict>, "name": <function-name>} </tool_call><|im_end|> ``` To complete the function call, create a user prompt that follows the above system prompt, like so: ``` <|im_start|>user Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|> ``` The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling): ``` <|im_start|>assistant <tool_call> {"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"} </tool_call><|im_end|> ``` Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so: ``` <|im_start|>tool <tool_response> {"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}} </tool_response> <|im_end|> ``` The assistant will then read in that data from the function's response, and generate a natural language response: ``` <|im_start|>assistant The stock fundamentals data for Tesla (TSLA) are as follows: - **Symbol**: TSLA - **Company Name**: Tesla, Inc. - **Sector**: Consumer Cyclical - **Industry**: Auto Manufacturers - **Market Capitalization**: $566,160,130,480 - **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73 - **Price-to-Book Ratio (PB Ratio)**: 9.04 - **Dividend Yield**: N/A - **Trailing Earnings Per Share (EPS)**: $4.3 - **Beta Value of the Stock**: 2.42 - **52-Week High Price of the Stock**: $299.29 - **52-Week Low Price of the Stock**: $152.37 This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|> ``` ## Prompt Format for JSON Mode / Structured Outputs Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema. Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main ``` <|im_start|>system You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|> ``` Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON. # Benchmarks  ## GPT4All: ``` |    Task     |Version| Metric |Value |   |Stderr| |-------------|------:|--------|-----:|---|-----:| |arc_challenge|      0|acc     |0.5529|±  |0.0145| |             |       |acc_norm|0.5870|±  |0.0144| |arc_easy     |      0|acc     |0.8371|±  |0.0076| |             |       |acc_norm|0.8144|±  |0.0080| |boolq        |      1|acc     |0.8599|±  |0.0061| |hellaswag    |      0|acc     |0.6133|±  |0.0049| |             |       |acc_norm|0.7989|±  |0.0040| |openbookqa   |      0|acc     |0.3940|±  |0.0219| |             |       |acc_norm|0.4680|±  |0.0223| |piqa         |      0|acc     |0.8063|±  |0.0092| |             |       |acc_norm|0.8156|±  |0.0090| |winogrande   |      0|acc     |0.7372|±  |0.0124| ``` Average: 72.59 ## AGIEval: ``` |             Task             |Version| Metric |Value |   |Stderr| |------------------------------|------:|--------|-----:|---|-----:| |agieval_aqua_rat              |      0|acc     |0.2441|±  |0.0270| |                              |       |acc_norm|0.2441|±  |0.0270| |agieval_logiqa_en             |      0|acc     |0.3687|±  |0.0189| |                              |       |acc_norm|0.3840|±  |0.0191| |agieval_lsat_ar               |      0|acc     |0.2304|±  |0.0278| |                              |       |acc_norm|0.2174|±  |0.0273| |agieval_lsat_lr               |      0|acc     |0.5471|±  |0.0221| |                              |       |acc_norm|0.5373|±  |0.0221| |agieval_lsat_rc               |      0|acc     |0.6617|±  |0.0289| |                              |       |acc_norm|0.6357|±  |0.0294| |agieval_sat_en                |      0|acc     |0.7670|±  |0.0295| |                              |       |acc_norm|0.7379|±  |0.0307| |agieval_sat_en_without_passage|      0|acc     |0.4417|±  |0.0347| |                              |       |acc_norm|0.4223|±  |0.0345| |agieval_sat_math              |      0|acc     |0.4000|±  |0.0331| |                              |       |acc_norm|0.3455|±  |0.0321| ``` Average: 44.05 ## BigBench: ``` |                      Task                      |Version|       Metric        |Value |   |Stderr| |------------------------------------------------|------:|---------------------|-----:|---|-----:| |bigbench_causal_judgement                       |      0|multiple_choice_grade|0.6000|±  |0.0356| |bigbench_date_understanding                     |      0|multiple_choice_grade|0.6585|±  |0.0247| |bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3178|±  |0.0290| |bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.2340|±  |0.0224| |                                                |       |exact_str_match      |0.0000|±  |0.0000| |bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2980|±  |0.0205| |bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2057|±  |0.0153| |bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.5367|±  |0.0288| |bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.4040|±  |0.0220| |bigbench_navigate                               |      0|multiple_choice_grade|0.4970|±  |0.0158| |bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.7075|±  |0.0102| |bigbench_ruin_names                             |      0|multiple_choice_grade|0.4821|±  |0.0236| |bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.2295|±  |0.0133| |bigbench_snarks                                 |      0|multiple_choice_grade|0.6906|±  |0.0345| |bigbench_sports_understanding                   |      0|multiple_choice_grade|0.5375|±  |0.0159| |bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.6270|±  |0.0153| |bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2216|±  |0.0118| |bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1594|±  |0.0088| |bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.5367|±  |0.0288| ``` Average: 44.13 **IFEval**: 72.64 **MT_Bench**: Turn 1 - 8.3875, Turn 2 - 8.00625, Average - 8.196875 # Inference Code Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM) Note: To use function calling, you should see the github repo above. ```python # Code to inference Hermes with HF Transformers # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages import torch from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM import bitsandbytes, flash_attn tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Theta-Llama-3-8B', trust_remote_code=True) model = LlamaForCausalLM.from_pretrained( "NousResearch/Hermes-2-Theta-Llama-3-8B", torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, load_in_4bit=True, use_flash_attention_2=True ) prompts = [ """<|im_start|>system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|> <|im_start|>user Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|> <|im_start|>assistant""", ] for chat in prompts: print(chat) input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda") generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id) response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True) print(f"Response: {response}") ``` ## Inference Code for Function Calling: All code for utilizing, parsing, and building function calling templates is available on our github: [https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)  # Chat Interfaces When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane:  ## Quantized Versions: GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF # How to cite: ```bibtext @misc{Hermes-2-Theta-Llama-3-8B, url={[https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B][NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B))}, title={Hermes-2-Theta-Llama-3-8B}, author={"Teknium", Charles Goddard, "interstellarninja", "theemozilla", "karan4d", "huemin_art"} } ```

suprimedev/Suprime_TKB1

suprimedev

--- license: apache-2.0 ---

rusticluftig/700m

rusticluftig

--- license: mit ---

Yuki20/capstone-llama7B-lora

Yuki20

peft

--- library_name: peft base_model: baffo32/decapoda-research-llama-7B-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

DanteJR85/BeppeGrillo

DanteJR85

--- license: openrail ---

pcgarcia/myModel

pcgarcia

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Srijith-rkr/deepseek_base_1e-4_NO_cot_only_failed_samples_300_ckpt

Srijith-rkr

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dimi1357/poca-SoccerTwos

dimi1357

ml-agents

reinforcement-learning

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: dimi1357/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

isaacchung/QwenPhi-7B-slerp

isaacchung

--- tags: - merge - mergekit - lazymergekit - Qwen/Qwen1.5-7B-Chat - microsoft/Phi-3-mini-128k-instruct base_model: - Qwen/Qwen1.5-7B-Chat - microsoft/Phi-3-mini-128k-instruct --- # QwenPhi-7B-slerp QwenPhi-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) * [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ## 🧩 Configuration ```yaml slices: - sources: - model: Qwen/Qwen1.5-7B-Chat layer_range: [0, 32] - model: microsoft/Phi-3-mini-128k-instruct layer_range: [0, 32] merge_method: slerp base_model: microsoft/Phi-3-mini-128k-instruct parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "isaacchung/QwenPhi-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

MoTHer-VTHR/VTHR-LoRA-V-ModelTree_0-Depth_2-Node_AuZXTVeQ

MoTHer-VTHR

transformers

image-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

circulus/on-canvers-anime-v3.9.1

circulus

--- license: gpl-3.0 ---

MenaWANG/translator-en-la

MenaWANG

transformers

text2text-generation

--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: translator-en-la results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # translator-en-la This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.0617 - Bleu: 0.121 - Gen Len: 18.932 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 4.517 | 1.0 | 1242 | 4.1569 | 0.1253 | 18.931 | | 4.3336 | 2.0 | 2484 | 4.0617 | 0.121 | 18.932 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

chansick/distilbert-base-uncased-finetuned-clinc

chansick

transformers

text-classification

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos config: plus split: validation args: plus metrics: - name: Accuracy type: accuracy value: 0.9190322580645162 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7683 - Accuracy: 0.9190 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2971 | 1.0 | 318 | 3.2827 | 0.7152 | | 2.6163 | 2.0 | 636 | 1.8665 | 0.84 | | 1.5389 | 3.0 | 954 | 1.1485 | 0.8981 | | 1.0054 | 4.0 | 1272 | 0.8495 | 0.9135 | | 0.7912 | 5.0 | 1590 | 0.7683 | 0.9190 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

fshfurnitures/Bedfurnituredubai

fshfurnitures

[furniture stores](https://fshfurniture.ae/)

boostcamp-5th-nlp07/koalpaca-polyglot-5.8b-summary-v0.2

boostcamp-5th-nlp07

transformers

text-generation

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: v0.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # v0.2 This model is a fine-tuned version of [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2277 - Accuracy: 0.4969 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2287 | 0.5 | 22 | 2.4602 | 0.4882 | | 2.0297 | 1.0 | 44 | 2.3277 | 0.4913 | | 1.0888 | 1.5 | 66 | 2.4852 | 0.4976 | | 0.894 | 2.0 | 88 | 2.5449 | 0.4961 | | 0.2617 | 2.5 | 110 | 3.0394 | 0.4882 | | 0.2666 | 3.0 | 132 | 3.0984 | 0.4984 | | 0.1301 | 3.5 | 154 | 3.2467 | 0.4858 | | 0.1429 | 4.0 | 176 | 3.1169 | 0.4913 | | 0.092 | 4.5 | 198 | 3.2166 | 0.4961 | | 0.035 | 5.0 | 220 | 3.2277 | 0.4969 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

yhshin1020/smollm-dpo

yhshin1020

transformers

text-generation

--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ABHIiiii1/LaBSE-Fine-Tuned-EN-KHA

ABHIiiii1

sentence-transformers

sentence-similarity

--- base_model: sentence-transformers/LaBSE datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:23999 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Who led thee through that great and terrible wilderness , wherein were fiery serpents , and scorpions , and drought , where there was no water ; who brought thee forth water out of the rock of flint ; sentences: - bad u ai ïa ki ha u Aaron bad ki khun shynrang jong u . - U la ïalam ïa phi lyngba ka ri shyiap kaba ïar bad kaba ishyrkhei eh , ha kaba la don ki bseiñ kiba don bih bad ki ñianglartham . Ha kata ka ri kaba tyrkhong bad ka bym don um , u la pynmih um na u mawsiang na ka bynta jong phi . - Ki paidbah na ki jait ba na shatei ki phah khot ïa u , bad nangta ma ki baroh ki ïaleit lang sha u Rehoboam bad ki ong ha u , - source_sentence: And , behold , Boaz came from Beth–lehem , and said unto the reapers , The Lord be with you . And they answered him , The Lord bless thee . sentences: - Ko ki briew bymïaineh , to wan noh ; phi long ki jong nga . Ngan shim iwei na phi na kawei kawei ka shnong bad ar ngut na kawei kawei ka kur , bad ngan wallam pat ïa phi sha u lum Seïon . - Hadien katto katne por u Boas da lade hi u wan poi na Bethlehem bad u ai khublei ïa ki nongtrei . To U Trai un long ryngkat bad phi ! u ong . U Trai u kyrkhu ïa phi ! ki jubab . - U Trai u la ong ha u , Khreh bad leit sha ‘ Ka Lynti Ba-beit ,’ bad ha ka ïing jong u Judas kylli ïa u briew na Tarsos uba kyrteng u Saul . - source_sentence: Jehovah used the prehuman Jesus as his “master worker” in creating all other things in heaven and on earth . sentences: - Shuwa ba un wan long briew U Jehobah u la pyndonkam ïa u Jisu kum u “rangbah nongtrei” ha kaba thaw ïa kiei kiei baroh kiba don ha bneng bad ha khyndew . - Shisien la don u briew uba la leit ban bet symbai . Katba u dang bet ïa u symbai , katto katne na u , ki la hap ha shi lynter ka lynti ïaid kjat , ha kaba ki la shah ïuh , bad ki sim ki la bam lut . - Ngan ïathuh ïa ka shatei ban shah ïa ki ban leit bad ïa ka shathie ban ym bat noh ïa ki . Ai ba ki briew jong nga ki wan phai na ki ri bajngai , na man la ki bynta baroh jong ka pyrthei . - source_sentence: 'The like figure whereunto even baptism doth also now save us ( not the putting away of the filth of the flesh , but the answer of a good conscience toward God , ) by the resurrection of Jesus Christ :' sentences: - kaba long ka dak kaba kdew sha ka jingpynbaptis , kaba pyllait im ïa phi mynta . Kam dei ka jingsait noh ïa ka jakhlia na ka met , hynrei ka jingkular ba la pynlong sha U Blei na ka jingïatiplem babha . Ka pynim ïa phi da ka jingmihpat jong U Jisu Khrist , - Ki briew kiba sniew kin ïoh ïa kaei kaba ki dei ban ïoh . Ki briew kiba bha kin ïoh bainong na ka bynta ki kam jong ki . - Nangta nga la ïohi ïa ka bneng bathymmai bad ïa ka pyrthei bathymmai . Ka bneng banyngkong bad ka pyrthei banyngkong ki la jah noh , bad ka duriaw kam don shuh . - source_sentence: On that day they read in the book of Moses in the audience of the people ; and therein was found written , that the Ammonite and the Moabite should not come into the congregation of God for ever ; sentences: - U Elisha u la ïap bad la tep ïa u . Man la ka snem ki kynhun jong ki Moab ki ju wan tur thma ïa ka ri Israel . - Katba dang pule jam ïa ka Hukum u Moses ha u paidbah , ki poi ha ka bynta kaba ong ba ym dei ban shah ïa u nong Amon ne u nong Moab ban ïasnohlang bad ki briew jong U Blei . - U angel u la jubab , U Mynsiem Bakhuid un sa wan ha pha , bad ka bor jong U Blei kan shong halor jong pha . Na kane ka daw , ïa i khunlung bakhuid yn khot U Khun U Blei . --- # SentenceTransformer based on sentence-transformers/LaBSE This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision e34fab64a3011d2176c99545a93d5cbddc9a91b7 --> - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("ABHIiiii1/LaBSE-Fine-Tuned-EN-KHA") # Run inference sentences = [ 'On that day they read in the book of Moses in the audience of the people ; and therein was found written , that the Ammonite and the Moabite should not come into the congregation of God for ever ;', 'Katba dang pule jam ïa ka Hukum u Moses ha u paidbah , ki poi ha ka bynta kaba ong ba ym dei ban shah ïa u nong Amon ne u nong Moab ban ïasnohlang bad ki briew jong U Blei .', 'U Elisha u la ïap bad la tep ïa u . Man la ka snem ki kynhun jong ki Moab ki ju wan tur thma ïa ka ri Israel .', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 23,999 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 34.89 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 51.51 tokens</li><li>max: 127 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>And Moses went out from Pharaoh , and entreated the Lord .</code> | <code>U Moses u mihnoh na u Pharaoh , bad u kyrpad ïa U Trai ,</code> | | <code>In the ninth year of Hoshea the king of Assyria took Samaria , and carried Israel away into Assyria , and placed them in Halah and in Habor by the river of Gozan , and in the cities of the Medes .</code> | <code>kaba long ka snem kaba khyndai jong ka jingsynshar u Hoshea , u patsha ka Assyria u kurup ïa ka Samaria , u rah ïa ki Israel sha Assyria kum ki koidi , bad pynsah katto katne ngut na ki ha ka nongbah Halah , katto katne pat hajan ka wah Habor ha ka distrik Gosan , bad katto katne ha ki nongbah jong ka Media .</code> | | <code>And the king said unto Cushi , Is the young man Absalom safe ? And Cushi answered , The enemies of my lord the king , and all that rise against thee to do thee hurt , be as that young man is .</code> | <code>Hato u samla Absalom u dang im ? u syiem u kylli . U mraw u jubab , Ko Kynrad , nga sngew ba kaei kaba la jia ha u kan jin da la jia ha baroh ki nongshun jong ngi , bad ha baroh kiba ïaleh pyrshah ïa phi .</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.3333 | 500 | 0.542 | | 0.6667 | 1000 | 0.135 | | 1.0 | 1500 | 0.0926 | | 1.3333 | 2000 | 0.0535 | | 1.6667 | 2500 | 0.0226 | | 2.0 | 3000 | 0.018 | | 2.3333 | 3500 | 0.0124 | | 2.6667 | 4000 | 0.0057 | | 3.0 | 4500 | 0.0053 | ### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.42.3 - PyTorch: 2.1.2 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->

MTCS34/distilbert-base-uncased-finetuned-squad

MTCS34

transformers

question-answering

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.3505 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2843 | 1.0 | 8235 | 1.3505 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

kxm1k4m1/llama-3.1-5-epoch

kxm1k4m1

transformers

text-generation

--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-aod-cut-oversampling-augmented

hafidikhsan

transformers

audio-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-aod-cut-oversampling-augmented results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-english-pronunciation-evaluation-aod-cut-oversampling-augmented This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0403 - Accuracy: 0.744 - F1: 0.7432 - Precision: 0.7436 - Recall: 0.744 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.8567 | 1.0 | 313 | 0.9539 | 0.5388 | 0.5159 | 0.5387 | 0.5388 | | 0.665 | 2.0 | 626 | 0.7520 | 0.6512 | 0.6545 | 0.6625 | 0.6512 | | 0.629 | 3.0 | 939 | 0.7775 | 0.7008 | 0.6980 | 0.6978 | 0.7008 | | 0.4793 | 4.0 | 1252 | 0.8696 | 0.7268 | 0.7295 | 0.7365 | 0.7268 | | 0.2273 | 5.0 | 1565 | 1.0403 | 0.744 | 0.7432 | 0.7436 | 0.744 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

pmysl/805Na-diffusers

pmysl

diffusers

text-to-image

--- pipeline_tag: text-to-image widget: - text: "A photo of sks tram in the Minecraft style" example_title: "Minecraft" - text: "A photo of sks tram with the Eiffel Tower in the background" example_title: "Eiffel Tower" - text: "A photo of sks tram on the Mars" example_title: "Mars" --- This is a fine-tuned Stable Diffusion model designed to create images of Konstal 805Na. Use `sks tram` in the prompt when you are referring to 805Na

winegarj/distilbert-base-uncased-finetuned-sst2

winegarj

transformers

text-classification

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3009 - Accuracy: 0.9048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 512 - eval_batch_size: 512 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 132 | 0.2494 | 0.8968 | | No log | 2.0 | 264 | 0.2767 | 0.8968 | | No log | 3.0 | 396 | 0.2810 | 0.9002 | | 0.195 | 4.0 | 528 | 0.2920 | 0.9025 | | 0.195 | 5.0 | 660 | 0.3009 | 0.9048 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

jicky/jickytest

jicky

--- license: mit ---

Kang-Seong-Jun/FruitGPT

Kang-Seong-Jun

image-classification

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: FruitGPT results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9666666388511658 --- # FruitGPT Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Apple  #### Banana  #### Grape  #### Orange 

huggingtweets/morgen__shtern

huggingtweets

transformers

text-generation

--- language: en thumbnail: http://www.huggingtweets.com/morgen__shtern/1664826569898/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1567266375026053125/0cyfXyiF_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">MORGENSHTERN</div> <div style="text-align: center; font-size: 14px;">@morgen__shtern</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from MORGENSHTERN. | Data | MORGENSHTERN | | --- | --- | | Tweets downloaded | 3178 | | Retweets | 57 | | Short tweets | 1034 | | Tweets kept | 2087 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3n5yin9a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @morgen__shtern's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2w93y3gk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2w93y3gk/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/morgen__shtern') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

sudopop/output

sudopop

transformers

automatic-speech-recognition

--- tags: - automatic-speech-recognition - kresnik/zeroth_korean - generated_from_trainer datasets: - zeroth_korean metrics: - wer model-index: - name: output results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # output This model is a fine-tuned version of [/home/son/Work/wav2vec2-xls-r-300m/facebook/wav2vec2-xls-r-300m](https://huggingface.co//home/son/Work/wav2vec2-xls-r-300m/facebook/wav2vec2-xls-r-300m) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 2.1666 - Wer: 0.9737 - Cer: 0.5039 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 19.558 | 1.44 | 500 | 19.4094 | 1.0 | 1.0 | | 4.7968 | 2.87 | 1000 | 4.7828 | 1.0 | 1.0 | | 4.5125 | 4.31 | 1500 | 4.4959 | 0.9991 | 0.9540 | | 4.2202 | 5.75 | 2000 | 4.2905 | 0.9923 | 0.8520 | | 3.7774 | 7.18 | 2500 | 3.2846 | 1.0356 | 0.6652 | | 3.1418 | 8.62 | 3000 | 2.3624 | 0.9882 | 0.5429 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1 - Datasets 2.6.1 - Tokenizers 0.11.0

hkivancoral/hushem_1x_beit_base_sgd_00001_fold5

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: hushem_1x_beit_base_sgd_00001_fold5 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.24390243902439024 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hushem_1x_beit_base_sgd_00001_fold5 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.6258 - Accuracy: 0.2439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 6 | 1.6353 | 0.2439 | | 1.5783 | 2.0 | 12 | 1.6348 | 0.2439 | | 1.5783 | 3.0 | 18 | 1.6344 | 0.2439 | | 1.5876 | 4.0 | 24 | 1.6339 | 0.2439 | | 1.5772 | 5.0 | 30 | 1.6335 | 0.2439 | | 1.5772 | 6.0 | 36 | 1.6330 | 0.2439 | | 1.5977 | 7.0 | 42 | 1.6326 | 0.2439 | | 1.5977 | 8.0 | 48 | 1.6322 | 0.2439 | | 1.5317 | 9.0 | 54 | 1.6318 | 0.2439 | | 1.5968 | 10.0 | 60 | 1.6314 | 0.2439 | | 1.5968 | 11.0 | 66 | 1.6311 | 0.2439 | | 1.549 | 12.0 | 72 | 1.6307 | 0.2439 | | 1.549 | 13.0 | 78 | 1.6303 | 0.2439 | | 1.5721 | 14.0 | 84 | 1.6300 | 0.2439 | | 1.5369 | 15.0 | 90 | 1.6297 | 0.2439 | | 1.5369 | 16.0 | 96 | 1.6294 | 0.2439 | | 1.5705 | 17.0 | 102 | 1.6291 | 0.2439 | | 1.5705 | 18.0 | 108 | 1.6288 | 0.2439 | | 1.5679 | 19.0 | 114 | 1.6286 | 0.2439 | | 1.5656 | 20.0 | 120 | 1.6284 | 0.2439 | | 1.5656 | 21.0 | 126 | 1.6281 | 0.2439 | | 1.5685 | 22.0 | 132 | 1.6279 | 0.2439 | | 1.5685 | 23.0 | 138 | 1.6277 | 0.2439 | | 1.5419 | 24.0 | 144 | 1.6275 | 0.2439 | | 1.5718 | 25.0 | 150 | 1.6273 | 0.2439 | | 1.5718 | 26.0 | 156 | 1.6271 | 0.2439 | | 1.5745 | 27.0 | 162 | 1.6269 | 0.2439 | | 1.5745 | 28.0 | 168 | 1.6268 | 0.2439 | | 1.5571 | 29.0 | 174 | 1.6267 | 0.2439 | | 1.5843 | 30.0 | 180 | 1.6265 | 0.2439 | | 1.5843 | 31.0 | 186 | 1.6264 | 0.2439 | | 1.5761 | 32.0 | 192 | 1.6263 | 0.2439 | | 1.5761 | 33.0 | 198 | 1.6262 | 0.2439 | | 1.5292 | 34.0 | 204 | 1.6261 | 0.2439 | | 1.5827 | 35.0 | 210 | 1.6261 | 0.2439 | | 1.5827 | 36.0 | 216 | 1.6260 | 0.2439 | | 1.5796 | 37.0 | 222 | 1.6259 | 0.2439 | | 1.5796 | 38.0 | 228 | 1.6259 | 0.2439 | | 1.5699 | 39.0 | 234 | 1.6259 | 0.2439 | | 1.5472 | 40.0 | 240 | 1.6258 | 0.2439 | | 1.5472 | 41.0 | 246 | 1.6258 | 0.2439 | | 1.5603 | 42.0 | 252 | 1.6258 | 0.2439 | | 1.5603 | 43.0 | 258 | 1.6258 | 0.2439 | | 1.5805 | 44.0 | 264 | 1.6258 | 0.2439 | | 1.5679 | 45.0 | 270 | 1.6258 | 0.2439 | | 1.5679 | 46.0 | 276 | 1.6258 | 0.2439 | | 1.5821 | 47.0 | 282 | 1.6258 | 0.2439 | | 1.5821 | 48.0 | 288 | 1.6258 | 0.2439 | | 1.5058 | 49.0 | 294 | 1.6258 | 0.2439 | | 1.5509 | 50.0 | 300 | 1.6258 | 0.2439 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

INoahGuy77/mistralclone

INoahGuy77

transformers

text-generation

--- license: apache-2.0 pipeline_tag: text-generation tags: - finetuned inference: true widget: - messages: - role: user content: What is your favorite condiment? - role: assistant content: "lkadjfljsd" --- # Model Card for Mistral-7B-Instruct-v0.1 The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets. For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

Ahmad-11/Reported_iGPT_v1.1

Ahmad-11

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

chrohi/meditron-7b-Q8_0-GGUF

chrohi

--- language: - en license: llama2 tags: - llama-cpp - gguf-my-repo base_model: meta-llama/Llama-2-7b datasets: - epfl-llm/guidelines metrics: - accuracy - perplexity --- # chrohi/meditron-7b-Q8_0-GGUF This model was converted to GGUF format from [`epfl-llm/meditron-7b`](https://huggingface.co/epfl-llm/meditron-7b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/epfl-llm/meditron-7b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo chrohi/meditron-7b-Q8_0-GGUF --model meditron-7b-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo chrohi/meditron-7b-Q8_0-GGUF --model meditron-7b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m meditron-7b-q8_0.gguf -n 128 ```

minkhantycc/translation-en-ja

minkhantycc

transformers

translation

--- language: - ja - en license: mit library_name: transformers datasets: - bsd_ja_en metrics: - sacrebleu pipeline_tag: translation widget: - text: おはいよ。 - text: 僕の国で　とても　綺麗な　所が　有ります。 --- This model is the fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on bsd_ja_en dataset. This will translate Japanese sentences to English sentences.

LoneStriker/Marcoroni-7B-v3-8.0bpw-h8-exl2-2

LoneStriker

transformers

text-generation

--- license: apache-2.0 --- # Marcoroni-7B-v3 <img src="https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/20uN0wMu2zTyVGgXV9PIo.png" width = 60%> # Updates December 11, 2023: Marcoroni-7B-v3 has placed **#5** overall and **#1** for 7 billion parameter models on the [Hugging Face Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)! # Model Details * **Trained by**: trained by AIDC AI-Business. * **Model type:** **Marcoroni-7B-v3** is an auto-regressive language model based on mistralai/Mistral-7B-v0.1. * **Language(s)**: English This is a DPO fine tuned model of [Q-bert/MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling). We fine-tuned using 32k data generated by GPT-4 and other models. # Prompting ## Prompt Template for alpaca style ``` ### Instruction: <prompt> (without the <>) ### Response: ```

RapGang23/SchnitzelGang

RapGang23

--- license: openrail ---

Inzamam567/Useless-7pa

Inzamam567

diffusers

text-to-image

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - stable-diffusion - aiartchan duplicated_from: AIARTCHAN/7pa --- # 7pa [원본글](https://arca.live/b/aiart/70729603) [civitai](https://civitai.com/models/13468) # Download - [original 4.27GB](https://civitai.com/api/download/models/15869) - [fp16 2.13GB](https://huggingface.co/AIARTCHAN/7pa/blob/main/7pa-fp16.safetensors) 7th anime v3 + 파스텔 + 어비스오렌지2(sfw)    

usc-isi/sbert-roberta-large-anli-mnli-snli

usc-isi

sentence-transformers

sentence-similarity

--- language: - en pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - anli - multi_nli - snli --- # sbert-roberta-large-anli-mnli-snli This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model is weight initialized by RoBERTa-large and trained on ANLI (Nie et al., 2020), MNLI (Williams et al., 2018), and SNLI (Bowman et al., 2015) using the [`training_nli.py`](https://github.com/UKPLab/sentence-transformers/blob/v0.3.5/examples/training/nli/training_nli.py) example script. Training Details: - Learning rate: 2e-5 - Batch size: 8 - Pooling: Mean - Training time: ~20 hours on one [NVIDIA GeForce RTX 2080 Ti](https://www.nvidia.com/en-us/geforce/graphics-cards/rtx-2080-ti/) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ```bash pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer("usc-isi/sbert-roberta-large-anli-mnli-snli") embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (Hugging Face Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: first, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python import torch from transformers import AutoModel, AutoTokenizer # Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] # First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ["This is an example sentence", "Each sentence is converted"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("usc-isi/sbert-roberta-large-anli-mnli-snli") model = AutoModel.from_pretrained("usc-isi/sbert-roberta-large-anli-mnli-snli") # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt") # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"]) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results See section 4.1 of our paper for evaluation results. ## Full Model Architecture ```text SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors For more information about the project, see our paper: > Ciosici, Manuel, et al. "Machine-Assisted Script Curation." _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations_, Association for Computational Linguistics, 2021, pp. 8–17. _ACLWeb_, <https://www.aclweb.org/anthology/2021.naacl-demos.2>. ## References - Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. [A large annotated corpus for learning natural language inference](https://doi.org/10.18653/v1/D15-1075). In _Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing_, pages 632–642, Lisbon, Portugal. Association for Computational Linguistics. - Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. [AdversarialNLI: A new benchmark for natural language understanding](https://doi.org/10.18653/v1/2020.acl-main.441). In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_, pages 4885–4901, Online. Association for Computational Linguistics. - Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. [A broad-coverage challenge corpus for sentence understanding through inference](https://doi.org/10.18653/v1/N18-1101). In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)_, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics.

yadibolt/unsloth-base-llama3-instruct

yadibolt

transformers

text-generation

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** yadibolt - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

s3nh/ToolBench-ToolLLaMA-2-7b-GGML

s3nh

transformers

text-generation

--- license: openrail language: - en pipeline_tag: text-generation library_name: transformers --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGML Format model files for [This project](https://huggingface.co/ToolBench/ToolLLaMA-2-7b). ### inference ```python import ctransformers from ctransformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, gpu_layers=32, model_type="llama") manual_input: str = "Tell me about your last dream, please." llm(manual_input, max_new_tokens=256, temperature=0.9, top_p= 0.7) ``` # Model Card for Model ID This is ToolLLaMA-2-7b version model introduced in [ToolBench](https://github.com/OpenBMB/ToolBench). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **License:** llama2 - **Finetuned from model [optional]:** LLaMA-2-7b-hf ## Uses Refer to [ToolBench](https://github.com/OpenBMB/ToolBench). ## Training Details Trained with the new version data in ToolBench.

polejowska/swin-tiny-patch4-window7-224-lcbsi-wbc

polejowska

transformers

image-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-lcbsi-wbc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-lcbsi-wbc This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0307 - Accuracy: 0.9933 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3668 | 0.98 | 27 | 0.6977 | 0.76 | | 0.217 | 1.98 | 54 | 0.0992 | 0.972 | | 0.102 | 2.98 | 81 | 0.0573 | 0.9853 | | 0.0762 | 3.98 | 108 | 0.1003 | 0.976 | | 0.0456 | 4.98 | 135 | 0.0307 | 0.9933 | | 0.0219 | 5.98 | 162 | 0.0497 | 0.9907 | | 0.0106 | 6.98 | 189 | 0.0568 | 0.9867 | | 0.0112 | 7.98 | 216 | 0.0532 | 0.9907 | | 0.0067 | 8.98 | 243 | 0.0528 | 0.9907 | | 0.008 | 9.98 | 270 | 0.0482 | 0.992 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

quangtqv/mxbai_rerank_turbo_official_12_8

quangtqv

transformers

text-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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declare-lab/tango-full

declare-lab

transformers

text-to-audio

--- license: cc-by-nc-sa-4.0 datasets: - declare-lab/TangoPromptBank language: - en tags: - music pipeline_tag: text-to-audio --- # TANGO: Text to Audio using iNstruction-Guided diffusiOn **TANGO** is a latent diffusion model for text-to-audio generation. **TANGO** can generate realistic audios including human sounds, animal sounds, natural and artificial sounds and sound effects from textual prompts. We use the frozen instruction-tuned LLM Flan-T5 as the text encoder and train a UNet based diffusion model for audio generation. We outperform current state-of-the-art models for audio generation across both objective and subjective metrics. We release our model, training, inference code and pre-trained checkpoints for the research community. 📣 We recently released **Tango 2**. Access it [here](https://huggingface.co/declare-lab/tango2). 📣 We are releasing **Tango-Full** which was pre-trained on **TangoPromptBank**. ## Code Our code is released here: [https://github.com/declare-lab/tango](https://github.com/declare-lab/tango) We uploaded several **TANGO** generated samples here: [https://tango-web.github.io/](https://tango-web.github.io/) Please follow the instructions in the repository for installation, usage and experiments. ## Quickstart Guide Download the **TANGO** model and generate audio from a text prompt: ```python import IPython import soundfile as sf from tango import Tango tango = Tango("declare-lab/tango-full-ft-audiocaps") prompt = "An audience cheering and clapping" audio = tango.generate(prompt) sf.write(f"{prompt}.wav", audio, samplerate=16000) IPython.display.Audio(data=audio, rate=16000) ``` [An audience cheering and clapping.webm](https://user-images.githubusercontent.com/13917097/233851915-e702524d-cd35-43f7-93e0-86ea579231a7.webm) The model will be automatically downloaded and saved in cache. Subsequent runs will load the model directly from cache. The `generate` function uses 100 steps by default to sample from the latent diffusion model. We recommend using 200 steps for generating better quality audios. This comes at the cost of increased run-time. ```python prompt = "Rolling thunder with lightning strikes" audio = tango.generate(prompt, steps=200) IPython.display.Audio(data=audio, rate=16000) ``` [Rolling thunder with lightning strikes.webm](https://user-images.githubusercontent.com/13917097/233851929-90501e41-911d-453f-a00b-b215743365b4.webm) <!-- [MachineClicking](https://user-images.githubusercontent.com/25340239/233857834-bfda52b4-4fcc-48de-b47a-6a6ddcb3671b.mp4 "sample 1") --> Use the `generate_for_batch` function to generate multiple audio samples for a batch of text prompts: ```python prompts = [ "A car engine revving", "A dog barks and rustles with some clicking", "Water flowing and trickling" ] audios = tango.generate_for_batch(prompts, samples=2) ``` This will generate two samples for each of the three text prompts.

amaai-lab/DisfluencySpeech_BenchmarkB

amaai-lab

text-to-speech

--- license: mit datasets: - amaai-lab/DisfluencySpeech language: - en pipeline_tag: text-to-speech --- # Usage ```python from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub from fairseq.models.text_to_speech.hub_interface import TTSHubInterface import IPython.display as ipd models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "amaai-lab/DisfluencySpeech_BenchmarkB", arg_overrides={"vocoder": "hifigan", "fp16": False, "spec-bwd-max-iter": 32} ) model = models[0] TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg) generator = task.build_generator(models, cfg) text = "Well, that's really funny, isn't it? What a strange world we live in." sample = TTSHubInterface.get_model_input(task, text) sample['net_input']['src_tokens'] = sample['net_input']['src_tokens'].cuda() sample['net_input']['src_lengths'] = sample['net_input']['src_lengths'].cuda() wav, rate = TTSHubInterface.get_prediction(task, model.cuda(), generator, sample) ipd.Audio(wav.cpu(), rate=rate) ```

apbrault/my_awesome_model

apbrault

transformers

text-classification

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2311 - Accuracy: 0.9310 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2258 | 1.0 | 1563 | 0.2041 | 0.9205 | | 0.1469 | 2.0 | 3126 | 0.2311 | 0.9310 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.2 - Tokenizers 0.19.1

hopkins/mbart-finetuned-eng-deu-23

hopkins

transformers

translation

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-23 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbart-finetuned-eng-deu-23 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6523 - Bleu: 20.8871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

etagaca/verifai-detector-roberta

etagaca

transformers

text-classification

--- datasets: - Hello-SimpleAI/HC3 language: - en pipeline_tag: text-classification tags: - chatgpt --- # Model Card for `Hello-SimpleAI/chatgpt-detector-roberta` This model is trained on **the mix of full-text and splitted sentences** of `answer`s from [Hello-SimpleAI/HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3). More details refer to [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597) and Gtihub project [Hello-SimpleAI/chatgpt-comparison-detection](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection). The base checkpoint is [roberta-base](https://huggingface.co/roberta-base). We train it with all [Hello-SimpleAI/HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) data (without held-out) for 1 epoch. (1-epoch is consistent with the experiments in [our paper](https://arxiv.org/abs/2301.07597).) ## Citation Checkout this papaer [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597) ``` @article{guo-etal-2023-hc3, title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection", author = "Guo, Biyang and Zhang, Xin and Wang, Ziyuan and Jiang, Minqi and Nie, Jinran and Ding, Yuxuan and Yue, Jianwei and Wu, Yupeng", journal={arXiv preprint arxiv:2301.07597} year = "2023", } ```

autoevaluate/roberta-base-squad2

autoevaluate

transformers

question-answering

--- language: en datasets: - squad_v2 license: cc-by-4.0 --- # roberta-base for QA > Note: this is a clone of [`roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2) for internal testing. This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. ## Overview **Language model:** roberta-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 96 n_epochs = 2 base_LM_model = "roberta-base" max_seq_len = 386 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` ## Using a distilled model instead Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model. ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") # or reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") ``` For a complete example of ``roberta-base-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/roberta-base-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 79.87029394424324, "f1": 82.91251169582613, "total": 11873, "HasAns_exact": 77.93522267206478, "HasAns_f1": 84.02838248389763, "HasAns_total": 5928, "NoAns_exact": 81.79983179142137, "NoAns_f1": 81.79983179142137, "NoAns_total": 5945 ``` Using the official [question answering notebook](https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb) from `transformers` yields: ``` {'HasAns_exact': 77.93522267206478, 'HasAns_f1': 83.93715663402219, 'HasAns_total': 5928, 'NoAns_exact': 81.90075693860386, 'NoAns_f1': 81.90075693860386, 'NoAns_total': 5945, 'best_exact': 79.92082877116145, 'best_exact_thresh': 0.0, 'best_f1': 82.91749890730902, 'best_f1_thresh': 0.0, 'exact': 79.92082877116145, 'f1': 82.91749890730917, 'total': 11873} ``` which is consistent with the officially reported results. Using the question answering `Evaluator` from `evaluate` gives: ``` {'HasAns_exact': 77.91835357624831, 'HasAns_f1': 84.07820736158186, 'HasAns_total': 5928, 'NoAns_exact': 81.91757779646763, 'NoAns_f1': 81.91757779646763, 'NoAns_total': 5945, 'best_exact': 79.92082877116145, 'best_exact_thresh': 0.996823787689209, 'best_f1': 82.99634576260925, 'best_f1_thresh': 0.996823787689209, 'exact': 79.92082877116145, 'f1': 82.9963457626089, 'latency_in_seconds': 0.016523243643392558, 'samples_per_second': 60.52080460605492, 'total': 11873, 'total_time_in_seconds': 196.18047177799986} ``` which is also consistent with the officially reported results. ## Authors **Branden Chan:** [email protected] **Timo Möller:** [email protected] **Malte Pietsch:** [email protected] **Tanay Soni:** [email protected] ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join"><img alt="slack" class="h-7 inline-block m-0" style="margin: 0" src="https://huggingface.co/spaces/deepset/README/resolve/main/Slack_RGB.png"/>community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)

tommylam/POCA-soccerTwos

tommylam

ml-agents

reinforcement-learning

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: tommylam/POCA-soccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ARG-NCTU/detr-resnet-50-finetuned-100-epochs-real-lifebuoy-dataset

ARG-NCTU

transformers

object-detection

--- library_name: transformers license: apache-2.0 base_model: ARG-NCTU/detr-resnet-50-finetuned-100-epochs-lifebuoy-dataset tags: - generated_from_trainer model-index: - name: detr-resnet-50-finetuned-100-epochs-real-lifebuoy-dataset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-100-epochs-real-lifebuoy-dataset This model is a fine-tuned version of [ARG-NCTU/detr-resnet-50-finetuned-100-epochs-lifebuoy-dataset](https://huggingface.co/ARG-NCTU/detr-resnet-50-finetuned-100-epochs-lifebuoy-dataset) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1

lucnha/convnextv2-base-22k-224-finetuned-eurosat-2

lucnha

transformers

image-classification

--- license: apache-2.0 base_model: facebook/convnextv2-base-22k-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnextv2-base-22k-224-finetuned-eurosat-2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9096045197740112 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnextv2-base-22k-224-finetuned-eurosat-2 This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3801 - Accuracy: 0.9096 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5227 | 1.0 | 99 | 0.5333 | 0.7797 | | 0.4248 | 1.99 | 198 | 0.4145 | 0.8531 | | 0.2998 | 2.99 | 297 | 0.3307 | 0.8757 | | 0.1704 | 4.0 | 397 | 0.2664 | 0.8927 | | 0.0684 | 5.0 | 496 | 0.4353 | 0.8701 | | 0.1546 | 5.99 | 595 | 0.3920 | 0.8870 | | 0.0593 | 6.99 | 694 | 0.3801 | 0.9096 | | 0.0745 | 8.0 | 794 | 0.4030 | 0.8983 | | 0.0877 | 9.0 | 893 | 0.3846 | 0.9040 | | 0.09 | 9.97 | 990 | 0.3816 | 0.9040 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu118 - Datasets 2.17.0 - Tokenizers 0.15.2

sail-rvc/inkling-boy

sail-rvc

transformers

audio-to-audio

--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # inkling-boy ## RVC Model  This model repo was automatically generated. Date: 2023-07-14 07:38:33 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod

fuzzymazoid/ppo-Huggy

fuzzymazoid

ml-agents

reinforcement-learning

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: fuzzymazoid/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

madhutry/yolo-finetuned-98samples

madhutry

transformers

object-detection

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/EleutherAI_-_pythia-410m-v0-4bits

RichardErkhov

transformers

text-generation

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) pythia-410m-v0 - bnb 4bits - Model creator: https://huggingface.co/EleutherAI/ - Original model: https://huggingface.co/EleutherAI/pythia-410m-v0/ Original model description: --- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - the_pile --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-410M ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10^-3 | — | | 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10^-4 | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10^-4 | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10^-4 | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10^-4 | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10^-4 | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10^-4 | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10^-4 | — | <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-410M for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-410M as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-410M has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-410M will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-410M to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-410M may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-410M. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/).<br> The Pile was **not** deduplicated before being used to train Pythia-410M. #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>

myhaaaaaaa/fa991909-b558-4139-a412-9cc1121f19b9

myhaaaaaaa

peft

--- library_name: peft license: other base_model: facebook/opt-125m tags: - axolotl - generated_from_trainer model-index: - name: fa991909-b558-4139-a412-9cc1121f19b9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: facebook/opt-125m bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7fac42cc059a4668_train_data.json ds_type: json format: custom path: /workspace/input_data/7fac42cc059a4668_train_data.json type: field_instruction: label field_output: text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: myhaaaaaaa/fa991909-b558-4139-a412-9cc1121f19b9 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/7fac42cc059a4668_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: da13383f-af60-4b76-b938-060410c0217e wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: da13383f-af60-4b76-b938-060410c0217e warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # fa991909-b558-4139-a412-9cc1121f19b9 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8280 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 16.0588 | 0.3742 | 200 | 3.8280 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

Helsinki-NLP/opus-mt-xh-sv

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-xh-sv * source languages: xh * target languages: sv * OPUS readme: [xh-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/xh-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/xh-sv/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/xh-sv/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/xh-sv/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.xh.sv | 33.1 | 0.522 |

Emperor-WS/ppo-LunarLander-v2-u8

Emperor-WS

reinforcement-learning

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -102.93 +/- 43.17 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 16 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 16 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Emperor-WS/ppo-CartPole-v1' 'batch_size': 2048 'minibatch_size': 128} ```

hlyu/co-condenser-marco-retriever_141011_mean

hlyu

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # hlyu/co-condenser-marco-retriever_141011_mean This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('hlyu/co-condenser-marco-retriever_141011_mean') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('hlyu/co-condenser-marco-retriever_141011_mean') model = AutoModel.from_pretrained('hlyu/co-condenser-marco-retriever_141011_mean') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=hlyu/co-condenser-marco-retriever_141011_mean) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 5055 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MSELoss.MSELoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "eps": 1e-06, "lr": 0.0001 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->

asenella/ms_MoPoE_beta_25_scale_False_seed_1

asenella

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

0x0son0/sl104

0x0son0

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

taoyoung/distilbert-base-uncased-distilled-clinc

taoyoung

transformers

text-classification

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2715 - Accuracy: 0.9465 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2778 | 1.0 | 318 | 1.6183 | 0.7335 | | 1.2551 | 2.0 | 636 | 0.8195 | 0.8681 | | 0.6656 | 3.0 | 954 | 0.4786 | 0.9148 | | 0.4077 | 4.0 | 1272 | 0.3549 | 0.9335 | | 0.3012 | 5.0 | 1590 | 0.3083 | 0.9410 | | 0.2553 | 6.0 | 1908 | 0.2912 | 0.9429 | | 0.2336 | 7.0 | 2226 | 0.2805 | 0.9445 | | 0.2217 | 8.0 | 2544 | 0.2754 | 0.9465 | | 0.2154 | 9.0 | 2862 | 0.2720 | 0.9471 | | 0.2122 | 10.0 | 3180 | 0.2715 | 0.9465 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.2+cu118 - Datasets 2.19.0 - Tokenizers 0.15.2

sail-rvc/SHUHUAGIDLE

sail-rvc

transformers

audio-to-audio

--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # SHUHUAGIDLE ## RVC Model  This model repo was automatically generated. Date: 2023-07-14 07:31:02 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod

nat-hunt/b1833ca1-6f8c-498a-83c2-9373d3a4d8b7

nat-hunt

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/Mistral-Nemo-Instruct-2407 tags: - axolotl - generated_from_trainer model-index: - name: b1833ca1-6f8c-498a-83c2-9373d3a4d8b7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) # b1833ca1-6f8c-498a-83c2-9373d3a4d8b7 This model is a fine-tuned version of [unsloth/Mistral-Nemo-Instruct-2407](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6335 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

shi-labs/dinat-tiny-in1k-224

shi-labs

transformers

image-classification

--- license: mit tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # DiNAT (tiny variant) DiNAT-Tiny trained on ImageNet-1K at 224x224 resolution. It was introduced in the paper [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001) by Hassani et al. and first released in [this repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). ## Model description DiNAT is a hierarchical vision transformer based on Neighborhood Attention (NA) and its dilated variant (DiNA). Neighborhood Attention is a restricted self attention pattern in which each token's receptive field is limited to its nearest neighboring pixels. NA and DiNA are therefore sliding-window attention patterns, and as a result are highly flexible and maintain translational equivariance. They come with PyTorch implementations through the [NATTEN](https://github.com/SHI-Labs/NATTEN/) package.  [Source](https://paperswithcode.com/paper/dilated-neighborhood-attention-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=dinat) to look for fine-tuned versions on a task that interests you. ### Example Here is how to use this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, DinatForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = AutoImageProcessor.from_pretrained("shi-labs/dinat-tiny-in1k-224") model = DinatForImageClassification.from_pretrained("shi-labs/dinat-tiny-in1k-224") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more examples, please refer to the [documentation](https://huggingface.co/transformers/model_doc/dinat.html#). ### Requirements Other than transformers, this model requires the [NATTEN](https://shi-labs.com/natten) package. If you're on Linux, you can refer to [shi-labs.com/natten](https://shi-labs.com/natten) for instructions on installing with pre-compiled binaries (just select your torch build to get the correct wheel URL). You can alternatively use `pip install natten` to compile on your device, which may take up to a few minutes. Mac users only have the latter option (no pre-compiled binaries). Refer to [NATTEN's GitHub](https://github.com/SHI-Labs/NATTEN/) for more information. ### BibTeX entry and citation info ```bibtex @article{hassani2022dilated, title = {Dilated Neighborhood Attention Transformer}, author = {Ali Hassani and Humphrey Shi}, year = 2022, url = {https://arxiv.org/abs/2209.15001}, eprint = {2209.15001}, archiveprefix = {arXiv}, primaryclass = {cs.CV} } ```

nielsr/segformer-finetuned-sidewalk-10k-steps

nielsr

transformers

image-segmentation

--- license: apache-2.0 tags: - image-segmentation - vision - generated_from_trainer base_model: nvidia/mit-b0 model-index: - name: segformer-finetuned-sidewalk-50-epochs results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-finetuned-sidewalk-50-epochs This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.6350 - Mean Iou: 0.3022 - Mean Accuracy: 0.3724 - Overall Accuracy: 0.8117 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.8240 - Accuracy Flat-sidewalk: 0.8308 - Accuracy Flat-crosswalk: 0.7789 - Accuracy Flat-cyclinglane: 0.9052 - Accuracy Flat-parkingdriveway: 0.3152 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.4703 - Accuracy Human-person: 0.6444 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9424 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.7116 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.8716 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.4736 - Accuracy Construction-fenceguardrail: 0.5408 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: nan - Accuracy Construction-stairs: 0.0048 - Accuracy Object-pole: 0.4202 - Accuracy Object-trafficsign: 0.0754 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9437 - Accuracy Nature-terrain: 0.8196 - Accuracy Sky: 0.9525 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.1041 - Accuracy Void-static: 0.2872 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.7413 - Iou Flat-sidewalk: 0.7520 - Iou Flat-crosswalk: 0.7629 - Iou Flat-cyclinglane: 0.4453 - Iou Flat-parkingdriveway: 0.2976 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.3701 - Iou Human-person: 0.4953 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.7962 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.4152 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.6712 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.3749 - Iou Construction-fenceguardrail: 0.4613 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: nan - Iou Construction-stairs: 0.0048 - Iou Object-pole: 0.2337 - Iou Object-trafficsign: 0.0753 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.8324 - Iou Nature-terrain: 0.7277 - Iou Sky: 0.9234 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0913 - Iou Void-static: 0.1997 - Iou Void-unclear: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | |:-------------:|:------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:| | 2.4745 | 1.85 | 100 | 1.7861 | 0.1056 | 0.1555 | 0.6397 | nan | 0.2287 | 0.9278 | 0.0 | 0.1406 | 0.0032 | nan | 0.0 | 0.0 | 0.0 | 0.7757 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8764 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8387 | 0.8794 | 0.3057 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.1931 | 0.6432 | 0.0 | 0.1380 | 0.0031 | nan | 0.0 | 0.0 | 0.0 | 0.5312 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4482 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6323 | 0.4860 | 0.3053 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.7294 | 3.7 | 200 | 1.3129 | 0.1517 | 0.1996 | 0.7410 | nan | 0.7928 | 0.8830 | 0.0 | 0.6053 | 0.0089 | nan | 0.0 | 0.0 | 0.0 | 0.7837 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8530 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9138 | 0.7742 | 0.7740 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5519 | 0.7788 | 0.0 | 0.5131 | 0.0088 | nan | 0.0 | 0.0 | 0.0 | 0.5804 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5005 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6747 | 0.5247 | 0.7209 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4479 | 5.56 | 300 | 1.1309 | 0.1608 | 0.2113 | 0.7588 | nan | 0.7973 | 0.9008 | 0.0 | 0.7721 | 0.0269 | nan | 0.0 | 0.0 | 0.0 | 0.8744 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8581 | 0.0 | 0.0007 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8622 | 0.8707 | 0.7985 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5861 | 0.7816 | 0.0 | 0.5877 | 0.0261 | nan | 0.0 | 0.0 | 0.0 | 0.6119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5582 | 0.0 | 0.0007 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7024 | 0.5206 | 0.7706 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2348 | 7.41 | 400 | 0.9644 | 0.1707 | 0.2170 | 0.7736 | nan | 0.8125 | 0.9218 | 0.0 | 0.7596 | 0.1081 | nan | 0.0000 | 0.0 | 0.0 | 0.9080 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8280 | 0.0 | 0.0334 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8856 | 0.8260 | 0.8612 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6003 | 0.7937 | 0.0 | 0.6538 | 0.0997 | nan | 0.0000 | 0.0 | 0.0 | 0.6189 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5731 | 0.0 | 0.0330 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7147 | 0.5601 | 0.8139 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0762 | 9.26 | 500 | 0.8819 | 0.1722 | 0.2159 | 0.7748 | nan | 0.7512 | 0.9353 | 0.0 | 0.7565 | 0.1204 | nan | 0.0016 | 0.0 | 0.0 | 0.9115 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8689 | 0.0 | 0.0565 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9098 | 0.7664 | 0.8303 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5993 | 0.7850 | 0.0 | 0.6536 | 0.1052 | nan | 0.0016 | 0.0 | 0.0 | 0.6377 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5767 | 0.0 | 0.0547 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7285 | 0.5709 | 0.7984 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9933 | 11.11 | 600 | 0.8347 | 0.1814 | 0.2263 | 0.7822 | nan | 0.8064 | 0.9111 | 0.0 | 0.7880 | 0.1443 | nan | 0.0436 | 0.0 | 0.0 | 0.8944 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8970 | 0.0 | 0.1914 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9053 | 0.8080 | 0.8526 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6088 | 0.8045 | 0.0 | 0.6845 | 0.1255 | nan | 0.0419 | 0.0 | 0.0 | 0.6594 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5548 | 0.0 | 0.1585 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7440 | 0.6068 | 0.8176 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9424 | 12.96 | 700 | 0.8428 | 0.1824 | 0.2271 | 0.7704 | nan | 0.6767 | 0.9270 | 0.0475 | 0.7655 | 0.1322 | nan | 0.2020 | 0.0189 | 0.0 | 0.8410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9205 | 0.0 | 0.2568 | 0.0 | 0.0 | nan | 0.0 | 0.0023 | 0.0 | 0.0 | 0.8994 | 0.7347 | 0.8413 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5838 | 0.7914 | 0.0475 | 0.6091 | 0.1095 | nan | 0.1597 | 0.0185 | 0.0 | 0.6706 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5131 | 0.0 | 0.1872 | 0.0 | 0.0 | nan | 0.0 | 0.0023 | 0.0 | 0.0 | 0.7525 | 0.5837 | 0.8077 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.8673 | 14.81 | 800 | 0.7934 | 0.2089 | 0.2509 | 0.7818 | nan | 0.6854 | 0.9394 | 0.7072 | 0.7240 | 0.1504 | nan | 0.2013 | 0.0186 | 0.0 | 0.9071 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9037 | 0.0 | 0.3110 | 0.0 | 0.0 | nan | 0.0 | 0.0108 | 0.0 | 0.0 | 0.8990 | 0.7171 | 0.8513 | 0.0 | 0.0 | 0.0013 | 0.0 | nan | 0.5914 | 0.7755 | 0.6900 | 0.6673 | 0.1340 | nan | 0.1542 | 0.0183 | 0.0 | 0.6792 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5639 | 0.0 | 0.2172 | 0.0 | 0.0 | nan | 0.0 | 0.0100 | 0.0 | 0.0 | 0.7615 | 0.6014 | 0.8192 | 0.0 | 0.0 | 0.0013 | 0.0 | | 0.8126 | 16.67 | 900 | 0.7484 | 0.2268 | 0.2784 | 0.7940 | nan | 0.6791 | 0.9397 | 0.7812 | 0.8009 | 0.1532 | nan | 0.3244 | 0.2962 | 0.0 | 0.9018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8567 | 0.0 | 0.4772 | 0.0002 | 0.0 | nan | 0.0 | 0.0834 | 0.0 | 0.0 | 0.8992 | 0.8280 | 0.8837 | 0.0 | 0.0 | 0.0032 | 0.0 | nan | 0.6303 | 0.7968 | 0.7079 | 0.6095 | 0.1396 | nan | 0.2196 | 0.2638 | 0.0 | 0.7100 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6016 | 0.0 | 0.2860 | 0.0002 | 0.0 | nan | 0.0 | 0.0570 | 0.0 | 0.0 | 0.7678 | 0.6211 | 0.8416 | 0.0 | 0.0 | 0.0032 | 0.0 | | 0.7989 | 18.52 | 1000 | 0.7241 | 0.2279 | 0.2803 | 0.8018 | nan | 0.7224 | 0.9402 | 0.7875 | 0.8234 | 0.1793 | nan | 0.3763 | 0.1974 | 0.0 | 0.9259 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8911 | 0.0 | 0.3994 | 0.0029 | 0.0 | nan | 0.0 | 0.0758 | 0.0 | 0.0 | 0.8619 | 0.8774 | 0.8854 | 0.0 | 0.0 | 0.0225 | 0.0 | nan | 0.6579 | 0.8292 | 0.7198 | 0.6924 | 0.1660 | nan | 0.2392 | 0.1794 | 0.0 | 0.6748 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5766 | 0.0 | 0.2654 | 0.0029 | 0.0 | nan | 0.0 | 0.0636 | 0.0 | 0.0 | 0.7582 | 0.5994 | 0.8455 | 0.0 | 0.0 | 0.0220 | 0.0 | | 0.7429 | 20.37 | 1100 | 0.7321 | 0.2276 | 0.2862 | 0.7876 | nan | 0.8321 | 0.8491 | 0.7958 | 0.8572 | 0.2216 | nan | 0.3030 | 0.2864 | 0.0 | 0.9456 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8668 | 0.0 | 0.3757 | 0.0040 | 0.0 | nan | 0.0 | 0.1140 | 0.0 | 0.0 | 0.8839 | 0.8499 | 0.9228 | 0.0 | 0.0 | 0.0505 | 0.0 | nan | 0.6678 | 0.7848 | 0.7342 | 0.5048 | 0.1995 | nan | 0.2316 | 0.2463 | 0.0 | 0.6379 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5916 | 0.0 | 0.2668 | 0.0040 | 0.0 | nan | 0.0 | 0.0820 | 0.0 | 0.0 | 0.7827 | 0.6428 | 0.8583 | 0.0 | 0.0 | 0.0465 | 0.0 | | 0.7131 | 22.22 | 1200 | 0.7231 | 0.2377 | 0.2995 | 0.7870 | nan | 0.8306 | 0.8458 | 0.7952 | 0.8505 | 0.2218 | nan | 0.3614 | 0.5001 | 0.0 | 0.9504 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7598 | 0.0 | 0.5317 | 0.0405 | 0.0 | nan | 0.0 | 0.1381 | 0.0 | 0.0 | 0.9284 | 0.7938 | 0.9110 | 0.0 | 0.0 | 0.1262 | 0.0 | nan | 0.7038 | 0.7740 | 0.7537 | 0.4538 | 0.1996 | nan | 0.2521 | 0.3853 | 0.0 | 0.6576 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6157 | 0.0 | 0.3046 | 0.0404 | 0.0 | nan | 0.0 | 0.0921 | 0.0 | 0.0 | 0.7846 | 0.6383 | 0.8588 | 0.0 | 0.0 | 0.0911 | 0.0 | | 0.6919 | 24.07 | 1300 | 0.6775 | 0.2361 | 0.2885 | 0.8013 | nan | 0.7728 | 0.9073 | 0.8010 | 0.8366 | 0.1547 | nan | 0.3070 | 0.3428 | 0.0 | 0.9272 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8568 | 0.0 | 0.5009 | 0.0736 | 0.0 | nan | 0.0 | 0.0975 | 0.0 | 0.0 | 0.9297 | 0.7567 | 0.8978 | 0.0 | 0.0 | 0.0682 | 0.0 | nan | 0.6564 | 0.7929 | 0.6932 | 0.6396 | 0.1438 | nan | 0.2385 | 0.2888 | 0.0 | 0.6807 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6085 | 0.0 | 0.3114 | 0.0729 | 0.0 | nan | 0.0 | 0.0803 | 0.0 | 0.0 | 0.7857 | 0.6403 | 0.8601 | 0.0 | 0.0 | 0.0610 | 0.0 | | 0.68 | 25.93 | 1400 | 0.6321 | 0.2575 | 0.3109 | 0.8181 | nan | 0.7851 | 0.9362 | 0.8041 | 0.8438 | 0.1694 | nan | 0.3956 | 0.5626 | 0.0 | 0.9306 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8313 | 0.0 | 0.5073 | 0.2728 | 0.0 | nan | 0.0 | 0.1741 | 0.0 | 0.0 | 0.9221 | 0.7899 | 0.9071 | 0.0 | 0.0 | 0.1157 | 0.0 | nan | 0.6781 | 0.8336 | 0.7386 | 0.7047 | 0.1564 | nan | 0.2789 | 0.4291 | 0.0 | 0.6934 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6062 | 0.0 | 0.3305 | 0.2579 | 0.0 | nan | 0.0 | 0.1228 | 0.0 | 0.0 | 0.7952 | 0.6651 | 0.8631 | 0.0 | 0.0 | 0.0865 | 0.0 | | 0.6644 | 27.78 | 1500 | 0.6568 | 0.2555 | 0.3132 | 0.8074 | nan | 0.7687 | 0.9014 | 0.7631 | 0.8302 | 0.1869 | nan | 0.4841 | 0.4880 | 0.0 | 0.9294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.8139 | 0.0 | 0.5482 | 0.3042 | 0.0 | nan | 0.0 | 0.1974 | 0.0 | 0.0 | 0.9225 | 0.8543 | 0.9042 | 0.0 | 0.0 | 0.1259 | 0.0 | nan | 0.6723 | 0.8030 | 0.7443 | 0.5873 | 0.1742 | nan | 0.3013 | 0.3813 | 0.0 | 0.7117 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6159 | 0.0 | 0.3289 | 0.2810 | 0.0 | nan | 0.0 | 0.1295 | 0.0 | 0.0 | 0.8015 | 0.6848 | 0.8665 | 0.0 | 0.0 | 0.0931 | 0.0 | | 0.6153 | 29.63 | 1600 | 0.6157 | 0.2586 | 0.3131 | 0.8188 | nan | 0.8000 | 0.9242 | 0.7980 | 0.8445 | 0.1758 | nan | 0.4143 | 0.6256 | 0.0 | 0.9155 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.8792 | 0.0 | 0.4465 | 0.2182 | 0.0 | nan | 0.0 | 0.1970 | 0.0 | 0.0 | 0.9111 | 0.8171 | 0.9368 | 0.0 | 0.0 | 0.1136 | 0.0 | nan | 0.6844 | 0.8212 | 0.7565 | 0.6537 | 0.1636 | nan | 0.2857 | 0.4354 | 0.0 | 0.7222 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.6274 | 0.0 | 0.3217 | 0.2147 | 0.0 | nan | 0.0 | 0.1313 | 0.0 | 0.0 | 0.8082 | 0.6809 | 0.8737 | 0.0 | 0.0 | 0.0926 | 0.0 | | 0.6154 | 31.48 | 1700 | 0.6397 | 0.2621 | 0.3204 | 0.8117 | nan | 0.8357 | 0.8840 | 0.7908 | 0.8465 | 0.2590 | nan | 0.4050 | 0.5401 | 0.0 | 0.9393 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0105 | 0.0 | 0.0 | 0.8169 | 0.0 | 0.4733 | 0.3188 | 0.0 | nan | 0.0 | 0.2505 | 0.0 | 0.0 | 0.9181 | 0.8473 | 0.9287 | 0.0 | 0.0 | 0.1890 | 0.0 | nan | 0.6774 | 0.8042 | 0.7524 | 0.5662 | 0.2300 | nan | 0.2971 | 0.4050 | 0.0 | 0.6970 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0105 | 0.0 | 0.0 | 0.6489 | 0.0 | 0.3454 | 0.3058 | 0.0 | nan | 0.0 | 0.1441 | 0.0 | 0.0 | 0.8074 | 0.6913 | 0.8820 | 0.0 | 0.0 | 0.1224 | 0.0 | | 0.6305 | 33.33 | 1800 | 0.6131 | 0.2641 | 0.3212 | 0.8194 | nan | 0.8171 | 0.8984 | 0.8212 | 0.8462 | 0.2582 | nan | 0.5051 | 0.5504 | 0.0 | 0.9421 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0221 | 0.0 | 0.0 | 0.8777 | 0.0 | 0.3528 | 0.3169 | 0.0 | nan | 0.0 | 0.2249 | 0.0 | 0.0 | 0.9203 | 0.8499 | 0.9175 | 0.0 | 0.0 | 0.1587 | 0.0 | nan | 0.7209 | 0.8195 | 0.7546 | 0.6166 | 0.2267 | nan | 0.3408 | 0.4000 | 0.0 | 0.6906 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0221 | 0.0 | 0.0 | 0.6055 | 0.0 | 0.2823 | 0.3044 | 0.0 | nan | 0.0 | 0.1545 | 0.0 | 0.0 | 0.8124 | 0.6994 | 0.8799 | 0.0 | 0.0 | 0.1204 | 0.0 | | 0.6083 | 35.19 | 1900 | 0.6224 | 0.2646 | 0.3182 | 0.8171 | nan | 0.7473 | 0.9297 | 0.7826 | 0.8269 | 0.2162 | nan | 0.4556 | 0.4982 | 0.0 | 0.9169 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0865 | 0.0 | 0.0 | 0.9031 | 0.0 | 0.3618 | 0.3583 | 0.0 | nan | 0.0 | 0.2603 | 0.0 | 0.0 | 0.8966 | 0.8828 | 0.9016 | 0.0 | 0.0 | 0.1587 | 0.0 | nan | 0.6824 | 0.8210 | 0.7645 | 0.5950 | 0.2019 | nan | 0.3166 | 0.3895 | 0.0 | 0.7307 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0853 | 0.0 | 0.0 | 0.6063 | 0.0 | 0.2860 | 0.3200 | 0.0 | nan | 0.0 | 0.1659 | 0.0 | 0.0 | 0.8188 | 0.7017 | 0.8695 | 0.0 | 0.0 | 0.1113 | 0.0 | | 0.5847 | 37.04 | 2000 | 0.5906 | 0.2713 | 0.3209 | 0.8281 | nan | 0.7374 | 0.9612 | 0.7764 | 0.8195 | 0.2033 | nan | 0.4219 | 0.4950 | 0.0 | 0.9339 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0960 | 0.0 | 0.0 | 0.8434 | 0.0 | 0.4552 | 0.4437 | 0.0 | nan | 0.0 | 0.2250 | 0.0 | 0.0 | 0.9315 | 0.8612 | 0.9071 | 0.0 | 0.0 | 0.1567 | 0.0 | nan | 0.6883 | 0.8311 | 0.7525 | 0.6838 | 0.1851 | nan | 0.3228 | 0.3780 | 0.0 | 0.7236 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0944 | 0.0 | 0.0 | 0.6338 | 0.0 | 0.3408 | 0.3853 | 0.0 | nan | 0.0 | 0.1586 | 0.0 | 0.0 | 0.8104 | 0.6978 | 0.8800 | 0.0 | 0.0 | 0.1162 | 0.0 | | 0.5764 | 38.89 | 2100 | 0.6088 | 0.2752 | 0.3225 | 0.8255 | nan | 0.7525 | 0.9472 | 0.7709 | 0.8441 | 0.2134 | nan | 0.3932 | 0.5383 | 0.0 | 0.9030 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3470 | 0.0 | 0.0 | 0.9195 | 0.0 | 0.3310 | 0.3215 | 0.0 | nan | 0.0 | 0.2234 | 0.0 | 0.0 | 0.9289 | 0.7964 | 0.9280 | 0.0 | 0.0 | 0.1604 | 0.0 | nan | 0.6993 | 0.8276 | 0.7546 | 0.7234 | 0.1997 | nan | 0.3005 | 0.4222 | 0.0 | 0.7348 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3123 | 0.0 | 0.0 | 0.5918 | 0.0 | 0.2787 | 0.3037 | 0.0 | nan | 0.0 | 0.1585 | 0.0 | 0.0 | 0.8124 | 0.6781 | 0.8844 | 0.0 | 0.0 | 0.1247 | 0.0 | | 0.5787 | 40.74 | 2200 | 0.5706 | 0.2824 | 0.3351 | 0.8347 | nan | 0.8178 | 0.9369 | 0.8003 | 0.8511 | 0.2352 | nan | 0.4838 | 0.5417 | 0.0 | 0.9025 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3689 | 0.0 | 0.0 | 0.8739 | 0.0 | 0.4493 | 0.4040 | 0.0 | nan | 0.0 | 0.2524 | 0.0 | 0.0 | 0.9422 | 0.8182 | 0.9183 | 0.0 | 0.0 | 0.1276 | 0.0 | nan | 0.7292 | 0.8432 | 0.7669 | 0.6897 | 0.2161 | nan | 0.3484 | 0.4230 | 0.0 | 0.7519 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3045 | 0.0 | 0.0 | 0.6407 | 0.0 | 0.3373 | 0.3491 | 0.0 | nan | 0.0 | 0.1557 | 0.0 | 0.0 | 0.8080 | 0.6803 | 0.8850 | 0.0 | 0.0 | 0.1068 | 0.0 | | 0.5724 | 42.59 | 2300 | 0.7562 | 0.2740 | 0.3479 | 0.7662 | nan | 0.8734 | 0.7169 | 0.7809 | 0.8847 | 0.2838 | nan | 0.3742 | 0.6758 | 0.0 | 0.9339 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6048 | 0.0 | 0.0 | 0.8535 | 0.0 | 0.4435 | 0.4729 | 0.0 | nan | 0.0 | 0.2817 | 0.0 | 0.0 | 0.9149 | 0.8765 | 0.9329 | 0.0 | 0.0 | 0.2292 | 0.0 | nan | 0.7041 | 0.6683 | 0.7628 | 0.3371 | 0.2575 | nan | 0.2878 | 0.4639 | 0.0 | 0.7454 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4190 | 0.0 | 0.0 | 0.6387 | 0.0 | 0.3357 | 0.3997 | 0.0 | nan | 0.0 | 0.1776 | 0.0 | 0.0 | 0.8183 | 0.7106 | 0.8911 | 0.0 | 0.0 | 0.1516 | 0.0 | | 0.556 | 44.44 | 2400 | 0.7350 | 0.2665 | 0.3366 | 0.7813 | nan | 0.7897 | 0.7888 | 0.8022 | 0.8878 | 0.2389 | nan | 0.4270 | 0.4859 | 0.0 | 0.9401 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4618 | 0.0 | 0.0 | 0.8866 | 0.0 | 0.3979 | 0.5050 | 0.0 | nan | 0.0 | 0.2580 | 0.0 | 0.0 | 0.9097 | 0.8627 | 0.9337 | 0.0 | 0.0 | 0.1948 | 0.0 | nan | 0.6902 | 0.7286 | 0.7779 | 0.3964 | 0.2231 | nan | 0.3011 | 0.3626 | 0.0 | 0.7078 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3485 | 0.0 | 0.0 | 0.6171 | 0.0 | 0.3044 | 0.3372 | 0.0 | nan | 0.0 | 0.1812 | 0.0 | 0.0 | 0.8195 | 0.7011 | 0.8947 | 0.0 | 0.0 | 0.1378 | 0.0 | | 0.5599 | 46.3 | 2500 | 0.5949 | 0.2846 | 0.3464 | 0.8215 | nan | 0.7919 | 0.9145 | 0.7935 | 0.8679 | 0.2189 | nan | 0.3795 | 0.5589 | 0.0 | 0.9334 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5627 | 0.0 | 0.0 | 0.8536 | 0.0 | 0.4394 | 0.4730 | 0.0 | nan | 0.0 | 0.3260 | 0.0 | 0.0 | 0.9098 | 0.8344 | 0.9487 | 0.0 | 0.0 | 0.2801 | 0.0 | nan | 0.6901 | 0.8199 | 0.7749 | 0.5729 | 0.2084 | nan | 0.3034 | 0.4321 | 0.0 | 0.7422 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4230 | 0.0 | 0.0 | 0.6491 | 0.0 | 0.3237 | 0.3989 | 0.0 | nan | 0.0 | 0.1963 | 0.0 | 0.0 | 0.8232 | 0.7048 | 0.8949 | 0.0 | 0.0 | 0.1489 | 0.0 | | 0.5368 | 48.15 | 2600 | 0.6125 | 0.2829 | 0.3502 | 0.8211 | nan | 0.7798 | 0.9034 | 0.7913 | 0.9079 | 0.2587 | nan | 0.3407 | 0.6423 | 0.0 | 0.9351 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6794 | 0.0 | 0.0 | 0.8554 | 0.0 | 0.3996 | 0.4884 | 0.0 | nan | 0.0 | 0.2870 | 0.0 | 0.0 | 0.9271 | 0.8698 | 0.9424 | 0.0 | 0.0 | 0.1992 | 0.0 | nan | 0.6878 | 0.8122 | 0.7578 | 0.5597 | 0.2427 | nan | 0.2680 | 0.4737 | 0.0 | 0.7517 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3649 | 0.0 | 0.0 | 0.6557 | 0.0 | 0.3130 | 0.4117 | 0.0 | nan | 0.0 | 0.1847 | 0.0 | 0.0 | 0.8236 | 0.7137 | 0.8969 | 0.0 | 0.0 | 0.1361 | 0.0 | | 0.5391 | 50.0 | 2700 | 0.5993 | 0.2877 | 0.3507 | 0.8242 | nan | 0.8174 | 0.8948 | 0.8094 | 0.8896 | 0.2730 | nan | 0.4105 | 0.5570 | 0.0 | 0.9164 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5439 | 0.0 | 0.0 | 0.8772 | 0.0 | 0.5070 | 0.5443 | 0.0 | nan | 0.0 | 0.2691 | 0.0 | 0.0 | 0.9205 | 0.8660 | 0.8975 | 0.0 | 0.0 | 0.2294 | 0.0 | nan | 0.7059 | 0.8214 | 0.7578 | 0.5803 | 0.2537 | nan | 0.2892 | 0.4308 | 0.0 | 0.7548 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4363 | 0.0 | 0.0 | 0.6490 | 0.0 | 0.3579 | 0.4224 | 0.0 | nan | 0.0 | 0.1927 | 0.0 | 0.0 | 0.8239 | 0.7040 | 0.8748 | 0.0 | 0.0 | 0.1516 | 0.0 | | 0.5041 | 51.85 | 2800 | 0.5912 | 0.2859 | 0.3493 | 0.8264 | nan | 0.7593 | 0.9248 | 0.8029 | 0.8780 | 0.2945 | nan | 0.3718 | 0.6308 | 0.0 | 0.9078 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6667 | 0.0 | 0.0 | 0.8945 | 0.0 | 0.3362 | 0.4834 | 0.0 | nan | 0.0 | 0.3167 | 0.0 | 0.0 | 0.9255 | 0.8641 | 0.9382 | 0.0 | 0.0 | 0.1836 | 0.0 | nan | 0.6993 | 0.8205 | 0.7232 | 0.5789 | 0.2712 | nan | 0.2852 | 0.4872 | 0.0 | 0.7747 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3825 | 0.0 | 0.0 | 0.6382 | 0.0 | 0.2862 | 0.4138 | 0.0 | nan | 0.0 | 0.2019 | 0.0 | 0.0 | 0.8284 | 0.7271 | 0.8984 | 0.0 | 0.0 | 0.1316 | 0.0 | | 0.5007 | 53.7 | 2900 | 0.6220 | 0.2839 | 0.3577 | 0.8134 | nan | 0.7302 | 0.8903 | 0.8180 | 0.9098 | 0.3134 | nan | 0.3521 | 0.6870 | 0.0 | 0.9429 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7288 | 0.0 | 0.0 | 0.8340 | 0.0 | 0.5169 | 0.4700 | 0.0 | nan | 0.0 | 0.3105 | 0.0 | 0.0 | 0.9356 | 0.8318 | 0.9437 | 0.0 | 0.0003 | 0.2298 | 0.0 | nan | 0.6722 | 0.8034 | 0.7257 | 0.4922 | 0.2900 | nan | 0.2639 | 0.4741 | 0.0 | 0.7434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4082 | 0.0 | 0.0 | 0.6635 | 0.0 | 0.3690 | 0.4172 | 0.0 | nan | 0.0 | 0.1981 | 0.0 | 0.0 | 0.8205 | 0.6936 | 0.9015 | 0.0 | 0.0003 | 0.1483 | 0.0 | | 0.4992 | 55.56 | 3000 | 0.5669 | 0.2928 | 0.3647 | 0.8317 | nan | 0.7826 | 0.9171 | 0.8018 | 0.9165 | 0.2758 | nan | 0.5273 | 0.6986 | 0.0 | 0.9410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6836 | 0.0 | 0.0 | 0.8296 | 0.0 | 0.4717 | 0.4595 | 0.0 | nan | 0.0 | 0.3613 | 0.0 | 0.0 | 0.9272 | 0.8671 | 0.9424 | 0.0 | 0.0017 | 0.2669 | 0.0 | nan | 0.7196 | 0.8377 | 0.7464 | 0.6016 | 0.2573 | nan | 0.3367 | 0.4767 | 0.0 | 0.7565 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4237 | 0.0 | 0.0 | 0.6653 | 0.0 | 0.3438 | 0.4034 | 0.0 | nan | 0.0 | 0.1974 | 0.0 | 0.0 | 0.8287 | 0.7120 | 0.9031 | 0.0 | 0.0017 | 0.1565 | 0.0 | | 0.5151 | 57.41 | 3100 | 0.6131 | 0.2864 | 0.3598 | 0.8169 | nan | 0.7793 | 0.9005 | 0.7894 | 0.8762 | 0.2508 | nan | 0.3852 | 0.6197 | 0.0 | 0.9316 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6506 | 0.0 | 0.0 | 0.7819 | 0.0 | 0.5348 | 0.5782 | 0.0 | nan | 0.0 | 0.3853 | 0.0 | 0.0 | 0.9211 | 0.8624 | 0.9390 | 0.0 | 0.0 | 0.3278 | 0.0 | nan | 0.6967 | 0.8145 | 0.7436 | 0.5453 | 0.2362 | nan | 0.2992 | 0.4656 | 0.0 | 0.7549 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4221 | 0.0 | 0.0 | 0.6246 | 0.0 | 0.3873 | 0.3923 | 0.0 | nan | 0.0 | 0.1937 | 0.0 | 0.0 | 0.8257 | 0.7204 | 0.8994 | 0.0 | 0.0 | 0.1417 | 0.0 | | 0.4688 | 59.26 | 3200 | 0.7342 | 0.2674 | 0.3425 | 0.7758 | nan | 0.6724 | 0.8138 | 0.8211 | 0.8881 | 0.2106 | nan | 0.3435 | 0.4240 | 0.0 | 0.9345 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6881 | 0.0 | 0.0 | 0.8684 | 0.0 | 0.4808 | 0.5494 | 0.0 | nan | 0.0 | 0.2968 | 0.0 | 0.0 | 0.9269 | 0.8322 | 0.9291 | 0.0 | 0.0 | 0.2817 | 0.0 | nan | 0.6227 | 0.7395 | 0.7654 | 0.4008 | 0.1990 | nan | 0.2434 | 0.3473 | 0.0 | 0.7526 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3733 | 0.0 | 0.0 | 0.5567 | 0.0 | 0.3425 | 0.4056 | 0.0 | nan | 0.0 | 0.2033 | 0.0 | 0.0 | 0.8238 | 0.7088 | 0.8978 | 0.0 | 0.0 | 0.1748 | 0.0 | | 0.4657 | 61.11 | 3300 | 0.7162 | 0.2737 | 0.3487 | 0.7884 | nan | 0.6859 | 0.8395 | 0.7919 | 0.8974 | 0.2306 | nan | 0.4086 | 0.6012 | 0.0 | 0.9212 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7186 | 0.0 | 0.0 | 0.8738 | 0.0 | 0.4323 | 0.5271 | 0.0 | nan | 0.0 | 0.3163 | 0.0 | 0.0 | 0.9373 | 0.8107 | 0.9381 | 0.0 | 0.0 | 0.2280 | 0.0 | nan | 0.6253 | 0.7668 | 0.7584 | 0.4350 | 0.2180 | nan | 0.2835 | 0.4646 | 0.0 | 0.7649 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3505 | 0.0 | 0.0 | 0.5817 | 0.0 | 0.3184 | 0.4275 | 0.0 | nan | 0.0 | 0.1989 | 0.0 | 0.0 | 0.8181 | 0.6916 | 0.9021 | 0.0 | 0.0 | 0.1529 | 0.0 | | 0.4789 | 62.96 | 3400 | 0.6510 | 0.2824 | 0.3535 | 0.8065 | nan | 0.7245 | 0.8835 | 0.7760 | 0.8886 | 0.2720 | nan | 0.3709 | 0.6675 | 0.0 | 0.9351 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6668 | 0.0 | 0.0 | 0.8450 | 0.0 | 0.4917 | 0.5508 | 0.0 | nan | 0.0 | 0.3585 | 0.0 | 0.0 | 0.9367 | 0.7684 | 0.9321 | 0.0 | 0.0022 | 0.2404 | 0.0 | nan | 0.6754 | 0.7938 | 0.7682 | 0.4856 | 0.2514 | nan | 0.2841 | 0.4779 | 0.0 | 0.7566 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3801 | 0.0 | 0.0 | 0.6118 | 0.0 | 0.3623 | 0.4464 | 0.0 | nan | 0.0 | 0.1990 | 0.0 | 0.0 | 0.8150 | 0.6727 | 0.9029 | 0.0 | 0.0022 | 0.1516 | 0.0 | | 0.4718 | 64.81 | 3500 | 0.7369 | 0.2741 | 0.3491 | 0.7687 | nan | 0.7886 | 0.7455 | 0.8159 | 0.8865 | 0.2585 | nan | 0.3583 | 0.6014 | 0.0 | 0.9362 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6741 | 0.0 | 0.0 | 0.8728 | 0.0 | 0.4488 | 0.5138 | 0.0 | nan | 0.0 | 0.3533 | 0.0 | 0.0 | 0.9343 | 0.8363 | 0.9345 | 0.0 | 0.0002 | 0.2111 | 0.0 | nan | 0.6800 | 0.6730 | 0.7173 | 0.3412 | 0.2406 | nan | 0.2736 | 0.4651 | 0.0 | 0.7688 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3688 | 0.0 | 0.0 | 0.6494 | 0.0 | 0.3507 | 0.4403 | 0.0 | nan | 0.0 | 0.1950 | 0.0 | 0.0 | 0.8287 | 0.7216 | 0.9039 | 0.0 | 0.0002 | 0.1536 | 0.0 | | 0.4586 | 66.67 | 3600 | 0.7463 | 0.2799 | 0.3515 | 0.7620 | nan | 0.8497 | 0.6965 | 0.7931 | 0.9041 | 0.2737 | nan | 0.3983 | 0.5616 | 0.0 | 0.9365 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5892 | 0.0 | 0.0 | 0.8439 | 0.0 | 0.5213 | 0.4720 | 0.0 | nan | 0.0 | 0.3429 | 0.0 | 0.0 | 0.9332 | 0.8690 | 0.9431 | 0.0 | 0.0 | 0.3213 | 0.0 | nan | 0.7435 | 0.6450 | 0.7808 | 0.3120 | 0.2517 | nan | 0.3134 | 0.4378 | 0.0 | 0.7305 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4349 | 0.0 | 0.0 | 0.6399 | 0.0 | 0.3813 | 0.4243 | 0.0 | nan | 0.0 | 0.2097 | 0.0 | 0.0 | 0.8287 | 0.7225 | 0.9085 | 0.0 | 0.0 | 0.1926 | 0.0 | | 0.4506 | 68.52 | 3700 | 0.6409 | 0.2859 | 0.3587 | 0.8030 | nan | 0.7887 | 0.8394 | 0.8054 | 0.8912 | 0.2518 | nan | 0.3799 | 0.6292 | 0.0 | 0.9273 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7090 | 0.0 | 0.0 | 0.8655 | 0.0 | 0.4989 | 0.5447 | 0.0 | nan | 0.0 | 0.3519 | 0.0 | 0.0 | 0.9335 | 0.8362 | 0.9278 | 0.0 | 0.0 | 0.2975 | 0.0 | nan | 0.7248 | 0.7574 | 0.7649 | 0.4118 | 0.2326 | nan | 0.2996 | 0.4840 | 0.0 | 0.7856 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3424 | 0.0 | 0.0 | 0.6639 | 0.0 | 0.3766 | 0.4576 | 0.0 | nan | 0.0 | 0.2055 | 0.0 | 0.0 | 0.8284 | 0.7274 | 0.9032 | 0.0 | 0.0 | 0.1823 | 0.0 | | 0.4659 | 70.37 | 3800 | 0.6466 | 0.2884 | 0.3577 | 0.8081 | nan | 0.8256 | 0.8420 | 0.7982 | 0.8692 | 0.3484 | nan | 0.4035 | 0.4964 | 0.0 | 0.9489 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6461 | 0.0 | 0.0 | 0.8281 | 0.0 | 0.5593 | 0.5404 | 0.0 | nan | 0.0 | 0.3533 | 0.0 | 0.0 | 0.9345 | 0.7861 | 0.9426 | 0.0 | 0.0 | 0.3225 | 0.0 | nan | 0.7403 | 0.7665 | 0.7649 | 0.4456 | 0.2991 | nan | 0.3198 | 0.3976 | 0.0 | 0.7512 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4217 | 0.0 | 0.0 | 0.6537 | 0.0 | 0.3859 | 0.4470 | 0.0 | nan | 0.0 | 0.2219 | 0.0 | 0.0 | 0.8223 | 0.6908 | 0.9109 | 0.0 | 0.0 | 0.1898 | 0.0 | | 0.4416 | 72.22 | 3900 | 0.6944 | 0.2824 | 0.3648 | 0.7953 | nan | 0.8073 | 0.8044 | 0.8200 | 0.9039 | 0.2713 | nan | 0.4385 | 0.6632 | 0.0 | 0.9435 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7130 | 0.0 | 0.0 | 0.8448 | 0.0 | 0.5050 | 0.5552 | 0.0 | nan | 0.0 | 0.3791 | 0.0 | 0.0 | 0.9316 | 0.8332 | 0.9378 | 0.0 | 0.0047 | 0.3183 | 0.0 | nan | 0.7045 | 0.7445 | 0.6571 | 0.4107 | 0.2536 | nan | 0.3089 | 0.4711 | 0.0 | 0.7504 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3814 | 0.0 | 0.0 | 0.6468 | 0.0 | 0.3800 | 0.4413 | 0.0 | nan | 0.0 | 0.2243 | 0.0 | 0.0 | 0.8294 | 0.7257 | 0.9078 | 0.0 | 0.0047 | 0.1964 | 0.0 | | 0.4347 | 74.07 | 4000 | 0.5742 | 0.2960 | 0.3615 | 0.8319 | nan | 0.8135 | 0.9088 | 0.8067 | 0.8959 | 0.3006 | nan | 0.3611 | 0.6055 | 0.0 | 0.9354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6851 | 0.0 | 0.0 | 0.8692 | 0.0 | 0.4956 | 0.5065 | 0.0 | nan | 0.0 | 0.3493 | 0.0 | 0.0 | 0.9264 | 0.8500 | 0.9368 | 0.0 | 0.0018 | 0.3210 | 0.0 | nan | 0.7436 | 0.8254 | 0.7615 | 0.5609 | 0.2797 | nan | 0.3045 | 0.4733 | 0.0 | 0.7745 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4006 | 0.0 | 0.0 | 0.6424 | 0.0 | 0.3800 | 0.4600 | 0.0 | nan | 0.0 | 0.2126 | 0.0 | 0.0 | 0.8296 | 0.7251 | 0.9085 | 0.0 | 0.0018 | 0.1876 | 0.0 | | 0.4191 | 75.93 | 4100 | 0.6454 | 0.2879 | 0.3671 | 0.8068 | nan | 0.7757 | 0.8432 | 0.8171 | 0.8803 | 0.3169 | nan | 0.4971 | 0.6474 | 0.0 | 0.9274 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7272 | 0.0 | 0.0 | 0.8520 | 0.0 | 0.4847 | 0.5414 | 0.0 | nan | 0.0 | 0.4113 | 0.0 | 0.0 | 0.9400 | 0.8335 | 0.9348 | 0.0 | 0.0167 | 0.3000 | 0.0 | nan | 0.7112 | 0.7615 | 0.6876 | 0.4533 | 0.2904 | nan | 0.3375 | 0.4768 | 0.0 | 0.7857 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3483 | 0.0 | 0.0 | 0.6544 | 0.0 | 0.3636 | 0.4546 | 0.0 | nan | 0.0 | 0.2086 | 0.0 | 0.0 | 0.8293 | 0.7293 | 0.9093 | 0.0 | 0.0165 | 0.1938 | 0.0 | | 0.4355 | 77.78 | 4200 | 0.5871 | 0.2915 | 0.3601 | 0.8236 | nan | 0.6673 | 0.9324 | 0.8063 | 0.8730 | 0.2988 | nan | 0.5014 | 0.5734 | 0.0 | 0.9480 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6629 | 0.0 | 0.0 | 0.8653 | 0.0 | 0.4649 | 0.5559 | 0.0 | nan | 0.0 | 0.3890 | 0.0 | 0.0 | 0.9183 | 0.8681 | 0.9537 | 0.0 | 0.0088 | 0.2359 | 0.0 | nan | 0.6266 | 0.8175 | 0.7309 | 0.5730 | 0.2746 | nan | 0.3471 | 0.4465 | 0.0 | 0.7567 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4103 | 0.0 | 0.0 | 0.6684 | 0.0 | 0.3482 | 0.4615 | 0.0 | nan | 0.0 | 0.2062 | 0.0 | 0.0 | 0.8356 | 0.7347 | 0.9131 | 0.0 | 0.0088 | 0.1686 | 0.0 | | 0.431 | 79.63 | 4300 | 0.5778 | 0.2902 | 0.3540 | 0.8266 | nan | 0.8325 | 0.9042 | 0.7971 | 0.8575 | 0.2707 | nan | 0.4318 | 0.5731 | 0.0 | 0.9428 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6701 | 0.0 | 0.0 | 0.8781 | 0.0 | 0.4081 | 0.5480 | 0.0 | nan | 0.0 | 0.3573 | 0.0 | 0.0 | 0.9299 | 0.7480 | 0.9397 | 0.0 | 0.0343 | 0.2046 | 0.0 | nan | 0.7428 | 0.8112 | 0.7719 | 0.5907 | 0.2545 | nan | 0.3259 | 0.4272 | 0.0 | 0.7505 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4255 | 0.0 | 0.0 | 0.6496 | 0.0 | 0.3209 | 0.4384 | 0.0 | nan | 0.0 | 0.2061 | 0.0 | 0.0 | 0.8142 | 0.6646 | 0.9118 | 0.0 | 0.0338 | 0.1477 | 0.0 | | 0.4105 | 81.48 | 4400 | 0.7355 | 0.2837 | 0.3547 | 0.7802 | nan | 0.8194 | 0.7548 | 0.8125 | 0.9004 | 0.2421 | nan | 0.4411 | 0.5260 | 0.0 | 0.9344 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6628 | 0.0 | 0.0 | 0.9003 | 0.0 | 0.4114 | 0.5457 | 0.0 | nan | 0.0 | 0.3720 | 0.0 | 0.0 | 0.9386 | 0.8336 | 0.9269 | 0.0 | 0.0905 | 0.2364 | 0.0 | nan | 0.7295 | 0.6964 | 0.7754 | 0.3477 | 0.2325 | nan | 0.3336 | 0.4069 | 0.0 | 0.7641 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4284 | 0.0 | 0.0 | 0.6483 | 0.0 | 0.3512 | 0.4444 | 0.0 | nan | 0.0 | 0.2140 | 0.0 | 0.0 | 0.8260 | 0.7200 | 0.9047 | 0.0 | 0.0883 | 0.1667 | 0.0 | | 0.4102 | 83.33 | 4500 | 0.6431 | 0.2832 | 0.3550 | 0.8023 | nan | 0.6173 | 0.8926 | 0.8233 | 0.8684 | 0.3015 | nan | 0.4774 | 0.5853 | 0.0 | 0.9435 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7118 | 0.0 | 0.0 | 0.8678 | 0.0 | 0.4544 | 0.5288 | 0.0 | nan | 0.0 | 0.3435 | 0.0 | 0.0 | 0.9438 | 0.7934 | 0.9323 | 0.0 | 0.0264 | 0.2495 | 0.0 | nan | 0.5793 | 0.7784 | 0.7849 | 0.5220 | 0.2750 | nan | 0.3433 | 0.4263 | 0.0 | 0.7478 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3651 | 0.0 | 0.0 | 0.6236 | 0.0 | 0.3489 | 0.4347 | 0.0 | nan | 0.0 | 0.2243 | 0.0 | 0.0 | 0.8184 | 0.6879 | 0.9082 | 0.0 | 0.0258 | 0.1674 | 0.0 | | 0.4172 | 85.19 | 4600 | 0.6988 | 0.2875 | 0.3537 | 0.7940 | nan | 0.7505 | 0.8194 | 0.8168 | 0.9128 | 0.2640 | nan | 0.4022 | 0.4961 | 0.0 | 0.9391 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6453 | 0.0 | 0.0 | 0.8769 | 0.0 | 0.4600 | 0.5182 | 0.0 | nan | 0.0 | 0.3740 | 0.0 | 0.0 | 0.9378 | 0.8263 | 0.9455 | 0.0 | 0.0900 | 0.2436 | 0.0 | nan | 0.7048 | 0.7401 | 0.7654 | 0.3938 | 0.2454 | nan | 0.2874 | 0.3973 | 0.0 | 0.7572 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4779 | 0.0 | 0.0 | 0.6427 | 0.0 | 0.3531 | 0.4565 | 0.0 | nan | 0.0 | 0.2402 | 0.0 | 0.0 | 0.8333 | 0.7320 | 0.9149 | 0.0 | 0.0880 | 0.1706 | 0.0 | | 0.3885 | 87.04 | 4700 | 0.5978 | 0.2953 | 0.3647 | 0.8175 | nan | 0.8142 | 0.8718 | 0.8027 | 0.8554 | 0.3059 | nan | 0.3787 | 0.5867 | 0.0 | 0.9403 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6845 | 0.0 | 0.0 | 0.8471 | 0.0 | 0.5315 | 0.5788 | 0.0 | nan | 0.0 | 0.3874 | 0.0 | 0.0 | 0.9354 | 0.8156 | 0.9494 | 0.0 | 0.1221 | 0.2636 | 0.0 | nan | 0.7263 | 0.7825 | 0.7874 | 0.4784 | 0.2859 | nan | 0.2981 | 0.4480 | 0.0 | 0.7604 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3820 | 0.0 | 0.0 | 0.6694 | 0.0 | 0.3781 | 0.4545 | 0.0 | nan | 0.0 | 0.2385 | 0.0 | 0.0 | 0.8301 | 0.7216 | 0.9144 | 0.0 | 0.1131 | 0.1798 | 0.0 | | 0.3949 | 88.89 | 4800 | 0.5747 | 0.2961 | 0.3643 | 0.8282 | nan | 0.8129 | 0.8976 | 0.8121 | 0.8713 | 0.2894 | nan | 0.4694 | 0.5562 | 0.0 | 0.9391 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6947 | 0.0 | 0.0 | 0.8395 | 0.0 | 0.5260 | 0.5481 | 0.0 | nan | 0.0 | 0.3852 | 0.0 | 0.0 | 0.9428 | 0.8221 | 0.9365 | 0.0 | 0.0559 | 0.2580 | 0.0 | nan | 0.7394 | 0.8130 | 0.7924 | 0.5533 | 0.2658 | nan | 0.3447 | 0.4378 | 0.0 | 0.7620 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3851 | 0.0 | 0.0 | 0.6633 | 0.0 | 0.3722 | 0.4533 | 0.0 | nan | 0.0 | 0.2184 | 0.0 | 0.0 | 0.8217 | 0.7122 | 0.9124 | 0.0 | 0.0534 | 0.1742 | 0.0 | | 0.4158 | 90.74 | 4900 | 0.6449 | 0.2916 | 0.3657 | 0.8070 | nan | 0.8043 | 0.8271 | 0.8157 | 0.9192 | 0.3073 | nan | 0.4380 | 0.6344 | 0.0 | 0.9340 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7171 | 0.0 | 0.0 | 0.8572 | 0.0 | 0.5188 | 0.5406 | 0.0 | nan | 0.0 | 0.3852 | 0.0 | 0.0 | 0.9420 | 0.8552 | 0.9459 | 0.0 | 0.0450 | 0.2148 | 0.0 | nan | 0.6975 | 0.7564 | 0.7902 | 0.4563 | 0.2853 | nan | 0.3171 | 0.4654 | 0.0 | 0.7879 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3571 | 0.0 | 0.0 | 0.6623 | 0.0 | 0.3819 | 0.4583 | 0.0 | nan | 0.0 | 0.2243 | 0.0 | 0.0 | 0.8302 | 0.7431 | 0.9150 | 0.0 | 0.0421 | 0.1602 | 0.0 | | 0.3856 | 92.59 | 5000 | 0.7492 | 0.2796 | 0.3559 | 0.7680 | nan | 0.8020 | 0.7250 | 0.8248 | 0.9139 | 0.2500 | nan | 0.3621 | 0.5930 | 0.0 | 0.9411 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6964 | 0.0 | 0.0 | 0.9036 | 0.0 | 0.3460 | 0.5234 | 0.0 | nan | 0.0 | 0.4271 | 0.0 | 0.0 | 0.9255 | 0.8871 | 0.9524 | 0.0 | 0.0666 | 0.2471 | 0.0 | nan | 0.6954 | 0.6697 | 0.7878 | 0.3256 | 0.2365 | nan | 0.2864 | 0.4452 | 0.0 | 0.7724 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3838 | 0.0 | 0.0 | 0.6413 | 0.0 | 0.2968 | 0.4239 | 0.0 | nan | 0.0 | 0.2271 | 0.0 | 0.0 | 0.8382 | 0.7554 | 0.9171 | 0.0 | 0.0624 | 0.1808 | 0.0 | | 0.3915 | 94.44 | 5100 | 0.6402 | 0.2893 | 0.3608 | 0.8012 | nan | 0.7614 | 0.8406 | 0.7898 | 0.9029 | 0.3080 | nan | 0.3857 | 0.6328 | 0.0 | 0.9373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7010 | 0.0 | 0.0 | 0.8626 | 0.0 | 0.5045 | 0.5235 | 0.0 | nan | 0.0 | 0.3802 | 0.0 | 0.0 | 0.9442 | 0.7561 | 0.9401 | 0.0 | 0.1133 | 0.2603 | 0.0 | nan | 0.6850 | 0.7546 | 0.7750 | 0.4451 | 0.2827 | nan | 0.3049 | 0.4715 | 0.0 | 0.7694 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3810 | 0.0 | 0.0 | 0.6626 | 0.0 | 0.3832 | 0.4394 | 0.0 | nan | 0.0 | 0.2214 | 0.0 | 0.0 | 0.8125 | 0.6725 | 0.9138 | 0.0 | 0.1034 | 0.1797 | 0.0 | | 0.3732 | 96.3 | 5200 | 0.7308 | 0.2840 | 0.3598 | 0.7795 | nan | 0.7534 | 0.7741 | 0.8137 | 0.9035 | 0.2614 | nan | 0.4308 | 0.6431 | 0.0 | 0.9315 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7293 | 0.0 | 0.0 | 0.8884 | 0.0 | 0.4166 | 0.5225 | 0.0 | nan | 0.0 | 0.3992 | 0.0 | 0.0 | 0.9329 | 0.8517 | 0.9519 | 0.0 | 0.0756 | 0.2354 | 0.0 | nan | 0.6723 | 0.6942 | 0.7836 | 0.3665 | 0.2474 | nan | 0.3333 | 0.4669 | 0.0 | 0.7857 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3545 | 0.0 | 0.0 | 0.6375 | 0.0 | 0.3443 | 0.4311 | 0.0 | nan | 0.0 | 0.2377 | 0.0 | 0.0 | 0.8346 | 0.7428 | 0.9173 | 0.0 | 0.0659 | 0.1722 | 0.0 | | 0.3843 | 98.15 | 5300 | 0.6580 | 0.2864 | 0.3556 | 0.7962 | nan | 0.7254 | 0.8440 | 0.7996 | 0.8889 | 0.2696 | nan | 0.4320 | 0.6399 | 0.0 | 0.9285 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6708 | 0.0 | 0.0 | 0.8872 | 0.0 | 0.4070 | 0.5262 | 0.0 | nan | 0.0 | 0.3791 | 0.0 | 0.0 | 0.9423 | 0.7462 | 0.9487 | 0.0 | 0.1269 | 0.2159 | 0.0 | nan | 0.6660 | 0.7540 | 0.7836 | 0.4484 | 0.2521 | nan | 0.3307 | 0.4691 | 0.0 | 0.7963 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3896 | 0.0 | 0.0 | 0.6071 | 0.0 | 0.3185 | 0.4568 | 0.0 | nan | 0.0 | 0.2206 | 0.0 | 0.0 | 0.8138 | 0.6608 | 0.9170 | 0.0 | 0.1163 | 0.1644 | 0.0 | | 0.3903 | 100.0 | 5400 | 0.6288 | 0.2881 | 0.3541 | 0.8086 | nan | 0.7763 | 0.8567 | 0.8240 | 0.8951 | 0.2446 | nan | 0.4334 | 0.5553 | 0.0 | 0.9354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6738 | 0.0 | 0.0 | 0.8901 | 0.0 | 0.4777 | 0.5458 | 0.0 | nan | 0.0 | 0.3297 | 0.0 | 0.0 | 0.9417 | 0.7702 | 0.9457 | 0.0 | 0.0457 | 0.1907 | 0.0 | nan | 0.6906 | 0.7727 | 0.7923 | 0.4705 | 0.2358 | nan | 0.3295 | 0.4509 | 0.0 | 0.7755 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3981 | 0.0 | 0.0 | 0.6528 | 0.0 | 0.3644 | 0.4573 | 0.0 | nan | 0.0 | 0.2197 | 0.0 | 0.0 | 0.8176 | 0.6797 | 0.9157 | 0.0 | 0.0444 | 0.1500 | 0.0 | | 0.355 | 101.85 | 5500 | 0.7112 | 0.2860 | 0.3563 | 0.7844 | nan | 0.7834 | 0.7947 | 0.8123 | 0.8807 | 0.2262 | nan | 0.3408 | 0.6020 | 0.0 | 0.9382 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6759 | 0.0 | 0.0 | 0.8838 | 0.0 | 0.4491 | 0.5845 | 0.0 | nan | 0.0 | 0.4029 | 0.0 | 0.0 | 0.9295 | 0.7890 | 0.9477 | 0.0 | 0.1045 | 0.2564 | 0.0 | nan | 0.7086 | 0.7078 | 0.7825 | 0.3607 | 0.2168 | nan | 0.2792 | 0.4624 | 0.0 | 0.7767 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4366 | 0.0 | 0.0 | 0.6667 | 0.0 | 0.3443 | 0.4351 | 0.0 | nan | 0.0 | 0.2386 | 0.0 | 0.0 | 0.8283 | 0.7060 | 0.9167 | 0.0 | 0.1000 | 0.1847 | 0.0 | | 0.3729 | 103.7 | 5600 | 0.6849 | 0.2835 | 0.3591 | 0.7887 | nan | 0.8150 | 0.7790 | 0.8122 | 0.8834 | 0.2787 | nan | 0.4506 | 0.6270 | 0.0 | 0.9253 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7408 | 0.0 | 0.0 | 0.9180 | 0.0 | 0.3273 | 0.5197 | 0.0 | nan | 0.0 | 0.4167 | 0.0 | 0.0 | 0.9358 | 0.8379 | 0.9406 | 0.0 | 0.0480 | 0.2345 | 0.0 | nan | 0.6989 | 0.7189 | 0.7862 | 0.3939 | 0.2648 | nan | 0.3292 | 0.4851 | 0.0 | 0.7976 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3286 | 0.0 | 0.0 | 0.6202 | 0.0 | 0.2779 | 0.4371 | 0.0 | nan | 0.0 | 0.2402 | 0.0 | 0.0 | 0.8321 | 0.7297 | 0.9140 | 0.0 | 0.0437 | 0.1749 | 0.0 | | 0.3895 | 105.56 | 5700 | 0.6917 | 0.2909 | 0.3669 | 0.7881 | nan | 0.8520 | 0.7575 | 0.8037 | 0.9006 | 0.2858 | nan | 0.4909 | 0.6331 | 0.0 | 0.9365 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6811 | 0.0 | 0.0 | 0.8525 | 0.0 | 0.5087 | 0.5374 | 0.0 | nan | 0.0 | 0.3766 | 0.0 | 0.0 | 0.9432 | 0.8426 | 0.9479 | 0.0 | 0.0982 | 0.2931 | 0.0 | nan | 0.7338 | 0.7000 | 0.7834 | 0.3764 | 0.2683 | nan | 0.3430 | 0.4719 | 0.0 | 0.7841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3792 | 0.0 | 0.0 | 0.6627 | 0.0 | 0.3815 | 0.4454 | 0.0 | nan | 0.0 | 0.2245 | 0.0 | 0.0 | 0.8273 | 0.7311 | 0.9183 | 0.0 | 0.0894 | 0.1885 | 0.0 | | 0.3602 | 107.41 | 5800 | 0.5475 | 0.3042 | 0.3685 | 0.8353 | nan | 0.7641 | 0.9319 | 0.8055 | 0.8737 | 0.3132 | nan | 0.4868 | 0.6244 | 0.0 | 0.9407 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6873 | 0.0 | 0.0 | 0.8810 | 0.0 | 0.4631 | 0.5387 | 0.0 | nan | 0.0 | 0.4382 | 0.0 | 0.0 | 0.9298 | 0.7866 | 0.9486 | 0.0 | 0.1344 | 0.2454 | 0.0 | nan | 0.7121 | 0.8270 | 0.7806 | 0.6491 | 0.2900 | nan | 0.3497 | 0.4700 | 0.0 | 0.7753 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4480 | 0.0 | 0.0 | 0.6577 | 0.0 | 0.3509 | 0.4582 | 0.0 | nan | 0.0 | 0.2281 | 0.0 | 0.0 | 0.8267 | 0.6946 | 0.9179 | 0.0 | 0.1213 | 0.1782 | 0.0 | | 0.3674 | 109.26 | 5900 | 0.6421 | 0.2919 | 0.3540 | 0.8016 | nan | 0.6932 | 0.8577 | 0.8144 | 0.9018 | 0.3136 | nan | 0.3961 | 0.5655 | 0.0 | 0.9370 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6563 | 0.0 | 0.0 | 0.9140 | 0.0 | 0.3656 | 0.4891 | 0.0 | nan | 0.0 | 0.3775 | 0.0 | 0.0 | 0.9373 | 0.8204 | 0.9427 | 0.0 | 0.1378 | 0.2090 | 0.0 | nan | 0.6366 | 0.7503 | 0.7829 | 0.4541 | 0.2884 | nan | 0.3050 | 0.4442 | 0.0 | 0.7727 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4780 | 0.0 | 0.0 | 0.6644 | 0.0 | 0.3163 | 0.4511 | 0.0 | nan | 0.0 | 0.2316 | 0.0 | 0.0 | 0.8321 | 0.7257 | 0.9157 | 0.0 | 0.1268 | 0.1636 | 0.0 | | 0.3657 | 111.11 | 6000 | 0.5813 | 0.2955 | 0.3637 | 0.8277 | nan | 0.7870 | 0.8975 | 0.7014 | 0.8566 | 0.3741 | nan | 0.4469 | 0.6219 | 0.0 | 0.9403 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7185 | 0.0 | 0.0 | 0.8827 | 0.0 | 0.4503 | 0.5681 | 0.0 | nan | 0.0 | 0.3815 | 0.0 | 0.0 | 0.9397 | 0.8275 | 0.9484 | 0.0 | 0.0968 | 0.1999 | 0.0 | nan | 0.7203 | 0.8097 | 0.6881 | 0.5693 | 0.3405 | nan | 0.3293 | 0.4754 | 0.0 | 0.7846 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3863 | 0.0 | 0.0 | 0.6346 | 0.0 | 0.3557 | 0.4385 | 0.0 | nan | 0.0 | 0.2181 | 0.0 | 0.0 | 0.8287 | 0.7172 | 0.9189 | 0.0 | 0.0846 | 0.1578 | 0.0 | | 0.367 | 112.96 | 6100 | 0.6609 | 0.2897 | 0.3661 | 0.7984 | nan | 0.7903 | 0.8284 | 0.8039 | 0.9016 | 0.2212 | nan | 0.4163 | 0.6816 | 0.0 | 0.9453 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7209 | 0.0 | 0.0 | 0.8372 | 0.0 | 0.4577 | 0.5511 | 0.0 | nan | 0.0 | 0.4283 | 0.0 | 0.0 | 0.9390 | 0.7875 | 0.9493 | 0.0 | 0.1399 | 0.3157 | 0.0 | nan | 0.7203 | 0.7408 | 0.7738 | 0.4105 | 0.2117 | nan | 0.3182 | 0.4784 | 0.0 | 0.7828 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3859 | 0.0 | 0.0 | 0.6672 | 0.0 | 0.3588 | 0.4378 | 0.0 | nan | 0.0 | 0.2244 | 0.0 | 0.0 | 0.8282 | 0.7032 | 0.9187 | 0.0 | 0.1137 | 0.1958 | 0.0 | | 0.3638 | 114.81 | 6200 | 0.7997 | 0.2803 | 0.3592 | 0.7547 | nan | 0.8092 | 0.6782 | 0.8102 | 0.9284 | 0.2905 | nan | 0.3691 | 0.6185 | 0.0 | 0.9403 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7520 | 0.0 | 0.0 | 0.8609 | 0.0 | 0.4178 | 0.5567 | 0.0 | nan | 0.0 | 0.3931 | 0.0 | 0.0 | 0.9474 | 0.8770 | 0.9435 | 0.0000 | 0.0667 | 0.2347 | 0.0 | nan | 0.7091 | 0.6261 | 0.7837 | 0.2942 | 0.2753 | nan | 0.2928 | 0.4552 | 0.0 | 0.7808 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3801 | 0.0 | 0.0 | 0.6648 | 0.0 | 0.3421 | 0.4315 | 0.0 | nan | 0.0 | 0.2152 | 0.0 | 0.0 | 0.8297 | 0.7448 | 0.9168 | 0.0000 | 0.0595 | 0.1680 | 0.0 | | 0.3654 | 116.67 | 6300 | 0.6019 | 0.2956 | 0.3645 | 0.8175 | nan | 0.8244 | 0.8533 | 0.6788 | 0.8927 | 0.3058 | nan | 0.4950 | 0.6003 | 0.0 | 0.9396 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6930 | 0.0 | 0.0 | 0.8964 | 0.0 | 0.3647 | 0.5196 | 0.0 | nan | 0.0 | 0.4113 | 0.0 | 0.0 | 0.9257 | 0.8551 | 0.9594 | 0.0 | 0.1310 | 0.3167 | 0.0 | nan | 0.7337 | 0.7732 | 0.6601 | 0.4748 | 0.2853 | nan | 0.3520 | 0.4685 | 0.0 | 0.7868 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4121 | 0.0 | 0.0 | 0.6708 | 0.0 | 0.3117 | 0.4434 | 0.0 | nan | 0.0 | 0.2326 | 0.0 | 0.0 | 0.8405 | 0.7541 | 0.9187 | 0.0 | 0.1205 | 0.2201 | 0.0 | | 0.3652 | 118.52 | 6400 | 0.5981 | 0.2967 | 0.3649 | 0.8205 | nan | 0.7551 | 0.8909 | 0.6342 | 0.9054 | 0.3093 | nan | 0.4234 | 0.6313 | 0.0 | 0.9387 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6751 | 0.0 | 0.0 | 0.8700 | 0.0 | 0.4187 | 0.5633 | 0.0 | nan | 0.0 | 0.4465 | 0.0 | 0.0 | 0.9262 | 0.8528 | 0.9534 | 0.0002 | 0.1437 | 0.3398 | 0.0 | nan | 0.6956 | 0.7948 | 0.6246 | 0.4963 | 0.2861 | nan | 0.3171 | 0.4870 | 0.0 | 0.7941 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4467 | 0.0 | 0.0 | 0.6719 | 0.0 | 0.3338 | 0.4473 | 0.0 | nan | 0.0 | 0.2377 | 0.0 | 0.0 | 0.8417 | 0.7531 | 0.9198 | 0.0002 | 0.1302 | 0.2180 | 0.0 | | 0.3559 | 120.37 | 6500 | 0.5780 | 0.3026 | 0.3668 | 0.8256 | nan | 0.7517 | 0.9024 | 0.8103 | 0.8905 | 0.3788 | nan | 0.3990 | 0.5648 | 0.0 | 0.9522 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6491 | 0.0 | 0.0 | 0.8623 | 0.0 | 0.5208 | 0.5227 | 0.0 | nan | 0.0 | 0.4095 | 0.0 | 0.0 | 0.9315 | 0.8073 | 0.9531 | 0.0 | 0.1367 | 0.2937 | 0.0 | nan | 0.6917 | 0.8084 | 0.7831 | 0.5645 | 0.3365 | nan | 0.3195 | 0.4446 | 0.0 | 0.7603 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4620 | 0.0 | 0.0 | 0.6310 | 0.0 | 0.3859 | 0.4599 | 0.0 | nan | 0.0 | 0.2286 | 0.0 | 0.0 | 0.8329 | 0.7236 | 0.9192 | 0.0 | 0.1259 | 0.2064 | 0.0 | | 0.3348 | 122.22 | 6600 | 0.5522 | 0.3023 | 0.3735 | 0.8379 | nan | 0.8289 | 0.9088 | 0.6882 | 0.8947 | 0.3594 | nan | 0.4373 | 0.6918 | 0.0 | 0.9448 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7098 | 0.0 | 0.0 | 0.8356 | 0.0 | 0.5156 | 0.5832 | 0.0 | nan | 0.0 | 0.4059 | 0.0 | 0.0 | 0.9417 | 0.8359 | 0.9578 | 0.0009 | 0.1308 | 0.2812 | 0.0 | nan | 0.7433 | 0.8257 | 0.6716 | 0.5930 | 0.3306 | nan | 0.3517 | 0.4956 | 0.0 | 0.7897 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3747 | 0.0 | 0.0 | 0.6736 | 0.0 | 0.3802 | 0.4271 | 0.0 | nan | 0.0 | 0.2180 | 0.0 | 0.0 | 0.8323 | 0.7373 | 0.9200 | 0.0008 | 0.1171 | 0.1906 | 0.0 | | 0.3653 | 124.07 | 6700 | 0.6070 | 0.2986 | 0.3679 | 0.8216 | nan | 0.6919 | 0.9133 | 0.8114 | 0.8786 | 0.3306 | nan | 0.4558 | 0.6517 | 0.0 | 0.9455 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7183 | 0.0 | 0.0 | 0.8672 | 0.0 | 0.5019 | 0.5472 | 0.0 | nan | 0.0 | 0.4162 | 0.0 | 0.0 | 0.9390 | 0.8019 | 0.9414 | 0.0 | 0.0957 | 0.2664 | 0.0 | nan | 0.6394 | 0.8000 | 0.7821 | 0.6011 | 0.3025 | nan | 0.3359 | 0.4969 | 0.0 | 0.7887 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3803 | 0.0 | 0.0 | 0.6386 | 0.0 | 0.3855 | 0.4427 | 0.0 | nan | 0.0 | 0.2268 | 0.0 | 0.0 | 0.8298 | 0.7136 | 0.9170 | 0.0 | 0.0886 | 0.1861 | 0.0 | | 0.3216 | 125.93 | 6800 | 0.6091 | 0.3003 | 0.3729 | 0.8176 | nan | 0.8300 | 0.8429 | 0.8233 | 0.9193 | 0.3587 | nan | 0.4900 | 0.6837 | 0.0 | 0.9439 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7272 | 0.0 | 0.0 | 0.8781 | 0.0 | 0.4143 | 0.5307 | 0.0 | nan | 0.0 | 0.4051 | 0.0116 | 0.0 | 0.9314 | 0.8400 | 0.9539 | 0.0 | 0.0921 | 0.2558 | 0.0 | nan | 0.7584 | 0.7706 | 0.7892 | 0.4626 | 0.3268 | nan | 0.3678 | 0.5054 | 0.0 | 0.7811 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3947 | 0.0 | 0.0 | 0.6604 | 0.0 | 0.3306 | 0.4515 | 0.0 | nan | 0.0 | 0.2265 | 0.0116 | 0.0 | 0.8386 | 0.7409 | 0.9204 | 0.0 | 0.0850 | 0.1887 | 0.0 | | 0.358 | 127.78 | 6900 | 0.5287 | 0.3110 | 0.3729 | 0.8465 | nan | 0.8062 | 0.9359 | 0.8173 | 0.8927 | 0.3346 | nan | 0.4527 | 0.6392 | 0.0 | 0.9354 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6945 | 0.0 | 0.0 | 0.8722 | 0.0 | 0.4896 | 0.5317 | 0.0 | nan | 0.0 | 0.4070 | 0.0 | 0.0 | 0.9436 | 0.8467 | 0.9449 | 0.0 | 0.1243 | 0.2646 | 0.0 | nan | 0.7567 | 0.8356 | 0.7873 | 0.6388 | 0.3087 | nan | 0.3575 | 0.4948 | 0.0 | 0.7958 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4146 | 0.0 | 0.0 | 0.6798 | 0.0 | 0.3797 | 0.4630 | 0.0 | nan | 0.0 | 0.2283 | 0.0 | 0.0 | 0.8356 | 0.7467 | 0.9182 | 0.0 | 0.1175 | 0.1940 | 0.0 | | 0.3402 | 129.63 | 7000 | 0.6208 | 0.2946 | 0.3637 | 0.8141 | nan | 0.7658 | 0.8754 | 0.8158 | 0.9118 | 0.2322 | nan | 0.4017 | 0.6637 | 0.0 | 0.9438 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6933 | 0.0 | 0.0 | 0.8763 | 0.0 | 0.3895 | 0.5601 | 0.0 | nan | 0.0 | 0.4252 | 0.0043 | 0.0 | 0.9423 | 0.7810 | 0.9448 | 0.0000 | 0.1253 | 0.2865 | 0.0 | nan | 0.7060 | 0.7779 | 0.7885 | 0.4813 | 0.2236 | nan | 0.3133 | 0.4921 | 0.0 | 0.7863 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4236 | 0.0 | 0.0 | 0.6817 | 0.0 | 0.3292 | 0.4440 | 0.0 | nan | 0.0 | 0.2236 | 0.0043 | 0.0 | 0.8247 | 0.6964 | 0.9178 | 0.0000 | 0.1163 | 0.1976 | 0.0 | | 0.3218 | 131.48 | 7100 | 0.5444 | 0.3108 | 0.3748 | 0.8443 | nan | 0.8296 | 0.9244 | 0.8276 | 0.8878 | 0.2774 | nan | 0.4782 | 0.6750 | 0.0 | 0.9366 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6983 | 0.0 | 0.0 | 0.8664 | 0.0 | 0.4743 | 0.5451 | 0.0 | nan | 0.0 | 0.4187 | 0.0113 | 0.0 | 0.9391 | 0.8642 | 0.9558 | 0.0 | 0.1166 | 0.2684 | 0.0 | nan | 0.7636 | 0.8260 | 0.7984 | 0.6281 | 0.2647 | nan | 0.3705 | 0.5066 | 0.0 | 0.8001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4217 | 0.0 | 0.0 | 0.6783 | 0.0 | 0.3686 | 0.4581 | 0.0 | nan | 0.0 | 0.2178 | 0.0113 | 0.0 | 0.8396 | 0.7666 | 0.9213 | 0.0 | 0.1113 | 0.1943 | 0.0 | | 0.3413 | 133.33 | 7200 | 0.5473 | 0.3063 | 0.3680 | 0.8412 | nan | 0.8038 | 0.9272 | 0.7396 | 0.8885 | 0.2742 | nan | 0.4489 | 0.5761 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6970 | 0.0 | 0.0 | 0.8722 | 0.0 | 0.5185 | 0.5545 | 0.0 | nan | 0.0 | 0.4060 | 0.0241 | 0.0 | 0.9384 | 0.8611 | 0.9453 | 0.0 | 0.1082 | 0.2489 | 0.0 | nan | 0.7450 | 0.8245 | 0.7280 | 0.6104 | 0.2595 | nan | 0.3532 | 0.4660 | 0.0 | 0.7846 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4313 | 0.0 | 0.0 | 0.6807 | 0.0 | 0.3896 | 0.4684 | 0.0 | nan | 0.0 | 0.2284 | 0.0241 | 0.0 | 0.8397 | 0.7610 | 0.9186 | 0.0 | 0.1022 | 0.1871 | 0.0 | | 0.3463 | 135.19 | 7300 | 0.6341 | 0.2922 | 0.3603 | 0.8106 | nan | 0.8087 | 0.8519 | 0.8052 | 0.9145 | 0.2425 | nan | 0.3711 | 0.5676 | 0.0 | 0.9336 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7046 | 0.0 | 0.0 | 0.8888 | 0.0 | 0.3923 | 0.5815 | 0.0 | nan | 0.0 | 0.4055 | 0.0319 | 0.0 | 0.9344 | 0.8036 | 0.9503 | 0.0 | 0.1152 | 0.2276 | 0.0 | nan | 0.7410 | 0.7674 | 0.7870 | 0.4522 | 0.2330 | nan | 0.3152 | 0.4495 | 0.0 | 0.7851 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4247 | 0.0 | 0.0 | 0.6553 | 0.0 | 0.3108 | 0.4330 | 0.0 | nan | 0.0 | 0.2290 | 0.0319 | 0.0 | 0.8273 | 0.7106 | 0.9198 | 0.0 | 0.1051 | 0.1720 | 0.0 | | 0.317 | 137.04 | 7400 | 0.5689 | 0.2996 | 0.3673 | 0.8346 | nan | 0.8380 | 0.9048 | 0.7202 | 0.8874 | 0.2300 | nan | 0.4682 | 0.6001 | 0.0 | 0.9282 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7278 | 0.0 | 0.0 | 0.8811 | 0.0 | 0.4430 | 0.5714 | 0.0 | nan | 0.0 | 0.4115 | 0.0148 | 0.0 | 0.9311 | 0.8477 | 0.9517 | 0.0 | 0.1019 | 0.2961 | 0.0 | nan | 0.7600 | 0.8107 | 0.7092 | 0.5843 | 0.2243 | nan | 0.3634 | 0.4741 | 0.0 | 0.7839 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3683 | 0.0 | 0.0 | 0.6667 | 0.0 | 0.3433 | 0.4519 | 0.0 | nan | 0.0 | 0.2331 | 0.0148 | 0.0 | 0.8387 | 0.7448 | 0.9201 | 0.0 | 0.0930 | 0.2020 | 0.0 | | 0.3241 | 138.89 | 7500 | 0.5921 | 0.3030 | 0.3698 | 0.8264 | nan | 0.7560 | 0.9038 | 0.8054 | 0.8993 | 0.2921 | nan | 0.4358 | 0.6497 | 0.0 | 0.9426 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6843 | 0.0 | 0.0 | 0.8596 | 0.0 | 0.4666 | 0.5531 | 0.0 | nan | 0.0014 | 0.4125 | 0.0280 | 0.0 | 0.9419 | 0.8345 | 0.9468 | 0.0005 | 0.1478 | 0.2726 | 0.0 | nan | 0.6935 | 0.8021 | 0.7869 | 0.5437 | 0.2719 | nan | 0.3428 | 0.4933 | 0.0 | 0.7917 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4134 | 0.0 | 0.0 | 0.6707 | 0.0 | 0.3632 | 0.4528 | 0.0 | nan | 0.0014 | 0.2150 | 0.0280 | 0.0 | 0.8367 | 0.7422 | 0.9203 | 0.0005 | 0.1346 | 0.1914 | 0.0 | | 0.3341 | 140.74 | 7600 | 0.5641 | 0.3038 | 0.3702 | 0.8325 | nan | 0.7624 | 0.9172 | 0.8114 | 0.8959 | 0.2940 | nan | 0.5063 | 0.6105 | 0.0 | 0.9434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7179 | 0.0 | 0.0 | 0.8732 | 0.0 | 0.5230 | 0.5420 | 0.0 | nan | 0.0 | 0.4148 | 0.0425 | 0.0 | 0.9411 | 0.7719 | 0.9528 | 0.0 | 0.0840 | 0.2431 | 0.0 | nan | 0.7064 | 0.8174 | 0.7877 | 0.6132 | 0.2760 | nan | 0.3594 | 0.4823 | 0.0 | 0.7859 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4116 | 0.0 | 0.0 | 0.6715 | 0.0 | 0.3953 | 0.4613 | 0.0 | nan | 0.0 | 0.2236 | 0.0425 | 0.0 | 0.8241 | 0.6840 | 0.9219 | 0.0 | 0.0790 | 0.1794 | 0.0 | | 0.3135 | 142.59 | 7700 | 0.5712 | 0.3062 | 0.3709 | 0.8300 | nan | 0.7952 | 0.8986 | 0.8100 | 0.8619 | 0.3084 | nan | 0.4715 | 0.6006 | 0.0 | 0.9439 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6837 | 0.0 | 0.0 | 0.8669 | 0.0 | 0.5083 | 0.5475 | 0.0 | nan | 0.0 | 0.4053 | 0.0384 | 0.0 | 0.9443 | 0.8124 | 0.9524 | 0.0 | 0.1181 | 0.3029 | 0.0 | nan | 0.7270 | 0.8042 | 0.7907 | 0.5385 | 0.2877 | nan | 0.3610 | 0.4689 | 0.0 | 0.7784 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4431 | 0.0 | 0.0 | 0.6764 | 0.0 | 0.3905 | 0.4659 | 0.0 | nan | 0.0 | 0.2280 | 0.0384 | 0.0 | 0.8312 | 0.7224 | 0.9227 | 0.0 | 0.1114 | 0.2117 | 0.0 | | 0.2985 | 144.44 | 7800 | 0.5705 | 0.3063 | 0.3739 | 0.8331 | nan | 0.7844 | 0.9061 | 0.8011 | 0.8987 | 0.3105 | nan | 0.4674 | 0.6336 | 0.0 | 0.9448 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7174 | 0.0 | 0.0 | 0.8645 | 0.0 | 0.4836 | 0.5414 | 0.0 | nan | 0.0 | 0.4277 | 0.0445 | 0.0 | 0.9390 | 0.8448 | 0.9518 | 0.0003 | 0.1004 | 0.3014 | 0.0 | nan | 0.7238 | 0.8110 | 0.7871 | 0.5506 | 0.2869 | nan | 0.3545 | 0.4901 | 0.0 | 0.7879 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4047 | 0.0 | 0.0 | 0.6872 | 0.0 | 0.3776 | 0.4572 | 0.0 | nan | 0.0 | 0.2263 | 0.0445 | 0.0 | 0.8392 | 0.7464 | 0.9226 | 0.0003 | 0.0950 | 0.2101 | 0.0 | | 0.3083 | 146.3 | 7900 | 0.6255 | 0.3029 | 0.3735 | 0.8173 | nan | 0.7919 | 0.8576 | 0.8118 | 0.9101 | 0.3017 | nan | 0.4374 | 0.6462 | 0.0 | 0.9461 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7137 | 0.0 | 0.0 | 0.8706 | 0.0 | 0.5111 | 0.5445 | 0.0 | nan | 0.0001 | 0.4282 | 0.0589 | 0.0 | 0.9317 | 0.8537 | 0.9628 | 0.0000 | 0.1030 | 0.2713 | 0.0 | nan | 0.7389 | 0.7675 | 0.7857 | 0.4623 | 0.2774 | nan | 0.3477 | 0.4815 | 0.0 | 0.7777 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4220 | 0.0 | 0.0 | 0.6797 | 0.0 | 0.3926 | 0.4652 | 0.0 | nan | 0.0001 | 0.2292 | 0.0588 | 0.0 | 0.8421 | 0.7549 | 0.9219 | 0.0000 | 0.0939 | 0.1926 | 0.0 | | 0.3132 | 148.15 | 8000 | 0.6407 | 0.2987 | 0.3697 | 0.8084 | nan | 0.8056 | 0.8366 | 0.8045 | 0.9187 | 0.2881 | nan | 0.3901 | 0.6494 | 0.0 | 0.9456 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7065 | 0.0 | 0.0 | 0.8674 | 0.0 | 0.4835 | 0.5578 | 0.0 | nan | 0.0 | 0.4107 | 0.0690 | 0.0 | 0.9364 | 0.8069 | 0.9579 | 0.0 | 0.1392 | 0.2549 | 0.0 | nan | 0.7400 | 0.7511 | 0.7860 | 0.4288 | 0.2705 | nan | 0.3211 | 0.4907 | 0.0 | 0.7845 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4064 | 0.0 | 0.0 | 0.6776 | 0.0 | 0.3750 | 0.4463 | 0.0 | nan | 0.0 | 0.2323 | 0.0689 | 0.0 | 0.8346 | 0.7221 | 0.9215 | 0.0 | 0.1189 | 0.1827 | 0.0 | | 0.3227 | 150.0 | 8100 | 0.6215 | 0.3010 | 0.3747 | 0.8154 | nan | 0.8072 | 0.8523 | 0.7987 | 0.9122 | 0.3387 | nan | 0.4049 | 0.6521 | 0.0 | 0.9464 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7268 | 0.0 | 0.0 | 0.8526 | 0.0 | 0.5301 | 0.5632 | 0.0 | nan | 0.0015 | 0.4353 | 0.0597 | 0.0 | 0.9352 | 0.8036 | 0.9574 | 0.0 | 0.1202 | 0.2916 | 0.0 | nan | 0.7319 | 0.7712 | 0.7839 | 0.4639 | 0.3115 | nan | 0.3235 | 0.4815 | 0.0 | 0.7813 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3954 | 0.0 | 0.0 | 0.6800 | 0.0 | 0.3930 | 0.4522 | 0.0 | nan | 0.0015 | 0.2349 | 0.0596 | 0.0 | 0.8319 | 0.7106 | 0.9225 | 0.0 | 0.1071 | 0.1947 | 0.0 | | 0.3041 | 151.85 | 8200 | 0.6365 | 0.2982 | 0.3695 | 0.8091 | nan | 0.7813 | 0.8516 | 0.8100 | 0.9057 | 0.2989 | nan | 0.4138 | 0.6557 | 0.0 | 0.9422 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7155 | 0.0 | 0.0 | 0.8717 | 0.0 | 0.5273 | 0.5454 | 0.0 | nan | 0.0 | 0.4293 | 0.0595 | 0.0 | 0.9354 | 0.7484 | 0.9557 | 0.0 | 0.1301 | 0.2483 | 0.0 | nan | 0.7117 | 0.7612 | 0.7891 | 0.4543 | 0.2787 | nan | 0.3305 | 0.4950 | 0.0 | 0.7874 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4007 | 0.0 | 0.0 | 0.6772 | 0.0 | 0.3923 | 0.4632 | 0.0 | nan | 0.0 | 0.2342 | 0.0594 | 0.0 | 0.8230 | 0.6691 | 0.9227 | 0.0 | 0.1142 | 0.1800 | 0.0 | | 0.3295 | 153.7 | 8300 | 0.5763 | 0.3064 | 0.3745 | 0.8319 | nan | 0.8091 | 0.9000 | 0.8155 | 0.8927 | 0.3048 | nan | 0.4385 | 0.6734 | 0.0 | 0.9391 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7114 | 0.0 | 0.0 | 0.8707 | 0.0 | 0.4884 | 0.5694 | 0.0 | nan | 0.0032 | 0.4179 | 0.0581 | 0.0 | 0.9385 | 0.8107 | 0.9552 | 0.0006 | 0.1316 | 0.2550 | 0.0 | nan | 0.7460 | 0.8059 | 0.7926 | 0.5582 | 0.2844 | nan | 0.3545 | 0.5009 | 0.0 | 0.7892 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4184 | 0.0 | 0.0 | 0.6741 | 0.0 | 0.3769 | 0.4455 | 0.0 | nan | 0.0032 | 0.2317 | 0.0581 | 0.0 | 0.8317 | 0.7120 | 0.9232 | 0.0005 | 0.1162 | 0.1807 | 0.0 | | 0.3057 | 155.56 | 8400 | 0.6602 | 0.2967 | 0.3669 | 0.8053 | nan | 0.7862 | 0.8400 | 0.8012 | 0.9083 | 0.2761 | nan | 0.3977 | 0.6548 | 0.0 | 0.9399 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7262 | 0.0 | 0.0 | 0.8830 | 0.0 | 0.4582 | 0.5390 | 0.0 | nan | 0.0 | 0.4382 | 0.0696 | 0.0 | 0.9380 | 0.7676 | 0.9517 | 0.0 | 0.1204 | 0.2454 | 0.0 | nan | 0.7257 | 0.7493 | 0.7832 | 0.4331 | 0.2603 | nan | 0.3344 | 0.4909 | 0.0 | 0.7899 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4164 | 0.0 | 0.0 | 0.6631 | 0.0 | 0.3619 | 0.4610 | 0.0 | nan | 0.0 | 0.2358 | 0.0695 | 0.0 | 0.8268 | 0.6858 | 0.9224 | 0.0 | 0.1038 | 0.1798 | 0.0 | | 0.3152 | 157.41 | 8500 | 0.6195 | 0.2986 | 0.3661 | 0.8115 | nan | 0.7876 | 0.8570 | 0.7994 | 0.8920 | 0.2891 | nan | 0.4035 | 0.6056 | 0.0 | 0.9417 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7090 | 0.0 | 0.0 | 0.8719 | 0.0 | 0.4959 | 0.5413 | 0.0 | nan | 0.0 | 0.4136 | 0.0566 | 0.0 | 0.9414 | 0.7717 | 0.9517 | 0.0 | 0.1198 | 0.2672 | 0.0 | nan | 0.7263 | 0.7633 | 0.7814 | 0.4550 | 0.2715 | nan | 0.3352 | 0.4721 | 0.0 | 0.7820 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4233 | 0.0 | 0.0 | 0.6671 | 0.0 | 0.3757 | 0.4677 | 0.0 | nan | 0.0 | 0.2407 | 0.0565 | 0.0 | 0.8255 | 0.6891 | 0.9216 | 0.0 | 0.1083 | 0.1912 | 0.0 | | 0.3041 | 159.26 | 8600 | 0.5761 | 0.3071 | 0.3735 | 0.8297 | nan | 0.8077 | 0.8910 | 0.8053 | 0.8839 | 0.3353 | nan | 0.4603 | 0.6015 | 0.0 | 0.9489 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6966 | 0.0 | 0.0 | 0.8701 | 0.0 | 0.4933 | 0.5427 | 0.0 | nan | 0.0082 | 0.4481 | 0.0761 | 0.0 | 0.9301 | 0.8454 | 0.9544 | 0.0005 | 0.1062 | 0.2469 | 0.0 | nan | 0.7406 | 0.7982 | 0.7855 | 0.5184 | 0.3024 | nan | 0.3652 | 0.4669 | 0.0 | 0.7807 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4413 | 0.0 | 0.0 | 0.6853 | 0.0 | 0.3815 | 0.4553 | 0.0 | nan | 0.0082 | 0.2312 | 0.0759 | 0.0 | 0.8414 | 0.7507 | 0.9229 | 0.0005 | 0.0961 | 0.1775 | 0.0 | | 0.3185 | 161.11 | 8700 | 0.5760 | 0.3058 | 0.3698 | 0.8296 | nan | 0.8094 | 0.8946 | 0.7956 | 0.8887 | 0.2897 | nan | 0.4223 | 0.5895 | 0.0 | 0.9357 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6889 | 0.0 | 0.0 | 0.8908 | 0.0 | 0.4640 | 0.5538 | 0.0 | nan | 0.0 | 0.4239 | 0.0692 | 0.0 | 0.9305 | 0.8418 | 0.9519 | 0.0001 | 0.1431 | 0.2510 | 0.0 | nan | 0.7455 | 0.7997 | 0.7789 | 0.5321 | 0.2717 | nan | 0.3473 | 0.4756 | 0.0 | 0.8013 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4311 | 0.0 | 0.0 | 0.6576 | 0.0 | 0.3605 | 0.4511 | 0.0 | nan | 0.0 | 0.2412 | 0.0691 | 0.0 | 0.8410 | 0.7459 | 0.9223 | 0.0001 | 0.1284 | 0.1839 | 0.0 | | 0.2908 | 162.96 | 8800 | 0.5655 | 0.3075 | 0.3717 | 0.8316 | nan | 0.8548 | 0.8841 | 0.7997 | 0.8745 | 0.3118 | nan | 0.4610 | 0.6024 | 0.0 | 0.9410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6931 | 0.0 | 0.0 | 0.8861 | 0.0 | 0.4534 | 0.5383 | 0.0 | nan | 0.0015 | 0.4266 | 0.0689 | 0.0 | 0.9366 | 0.8053 | 0.9554 | 0.0 | 0.1346 | 0.2641 | 0.0 | nan | 0.7595 | 0.8021 | 0.7817 | 0.5396 | 0.2919 | nan | 0.3717 | 0.4720 | 0.0 | 0.7905 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4462 | 0.0 | 0.0 | 0.6634 | 0.0 | 0.3562 | 0.4639 | 0.0 | nan | 0.0015 | 0.2393 | 0.0688 | 0.0 | 0.8346 | 0.7212 | 0.9232 | 0.0 | 0.1193 | 0.1923 | 0.0 | | 0.3137 | 164.81 | 8900 | 0.5829 | 0.3094 | 0.3784 | 0.8279 | nan | 0.8476 | 0.8674 | 0.8118 | 0.9018 | 0.3237 | nan | 0.4801 | 0.6610 | 0.0 | 0.9387 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6851 | 0.0 | 0.0 | 0.8696 | 0.0 | 0.5109 | 0.5681 | 0.0 | nan | 0.0260 | 0.4276 | 0.0709 | 0.0 | 0.9330 | 0.8416 | 0.9554 | 0.0012 | 0.1333 | 0.2547 | 0.0 | nan | 0.7562 | 0.7893 | 0.7902 | 0.5123 | 0.3055 | nan | 0.3768 | 0.4921 | 0.0 | 0.7978 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4472 | 0.0 | 0.0 | 0.6754 | 0.0 | 0.3867 | 0.4408 | 0.0 | nan | 0.0260 | 0.2316 | 0.0708 | 0.0 | 0.8396 | 0.7418 | 0.9237 | 0.0010 | 0.1173 | 0.1797 | 0.0 | | 0.3219 | 166.67 | 9000 | 0.5812 | 0.3065 | 0.3750 | 0.8278 | nan | 0.8354 | 0.8788 | 0.8041 | 0.8834 | 0.2990 | nan | 0.4594 | 0.6655 | 0.0 | 0.9395 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6980 | 0.0 | 0.0 | 0.8601 | 0.0 | 0.5069 | 0.5685 | 0.0 | nan | 0.0113 | 0.4156 | 0.0664 | 0.0 | 0.9440 | 0.8108 | 0.9521 | 0.0001 | 0.1291 | 0.2716 | 0.0 | nan | 0.7565 | 0.7902 | 0.7828 | 0.5219 | 0.2845 | nan | 0.3688 | 0.4922 | 0.0 | 0.7966 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4240 | 0.0 | 0.0 | 0.6768 | 0.0 | 0.3877 | 0.4481 | 0.0 | nan | 0.0113 | 0.2327 | 0.0664 | 0.0 | 0.8308 | 0.7154 | 0.9230 | 0.0001 | 0.1124 | 0.1869 | 0.0 | | 0.3181 | 168.52 | 9100 | 0.5632 | 0.3112 | 0.3765 | 0.8367 | nan | 0.8125 | 0.9072 | 0.8124 | 0.8963 | 0.3044 | nan | 0.4647 | 0.6697 | 0.0 | 0.9359 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6879 | 0.0 | 0.0 | 0.8771 | 0.0 | 0.5085 | 0.5560 | 0.0 | nan | 0.0039 | 0.4244 | 0.0703 | 0.0 | 0.9367 | 0.8280 | 0.9532 | 0.0 | 0.1309 | 0.2672 | 0.0 | nan | 0.7474 | 0.8113 | 0.7892 | 0.5707 | 0.2882 | nan | 0.3704 | 0.5031 | 0.0 | 0.7988 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4314 | 0.0 | 0.0 | 0.6778 | 0.0 | 0.3900 | 0.4604 | 0.0 | nan | 0.0039 | 0.2372 | 0.0702 | 0.0 | 0.8390 | 0.7407 | 0.9234 | 0.0 | 0.1173 | 0.1872 | 0.0 | | 0.3009 | 170.37 | 9200 | 0.5671 | 0.3095 | 0.3743 | 0.8326 | nan | 0.7939 | 0.9018 | 0.7926 | 0.8902 | 0.3160 | nan | 0.4603 | 0.6415 | 0.0 | 0.9414 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6804 | 0.0 | 0.0 | 0.8815 | 0.0 | 0.4974 | 0.5528 | 0.0 | nan | 0.0000 | 0.4233 | 0.0749 | 0.0 | 0.9339 | 0.8322 | 0.9566 | 0.0 | 0.1296 | 0.2770 | 0.0 | nan | 0.7279 | 0.8041 | 0.7736 | 0.5652 | 0.2951 | nan | 0.3698 | 0.4960 | 0.0 | 0.7938 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4395 | 0.0 | 0.0 | 0.6714 | 0.0 | 0.3837 | 0.4627 | 0.0 | nan | 0.0000 | 0.2368 | 0.0747 | 0.0 | 0.8379 | 0.7389 | 0.9235 | 0.0 | 0.1161 | 0.1946 | 0.0 | | 0.2873 | 172.22 | 9300 | 0.6113 | 0.3047 | 0.3720 | 0.8176 | nan | 0.8107 | 0.8536 | 0.7603 | 0.8949 | 0.3232 | nan | 0.4761 | 0.6422 | 0.0 | 0.9415 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6799 | 0.0 | 0.0 | 0.8720 | 0.0 | 0.5023 | 0.5457 | 0.0 | nan | 0.0034 | 0.4146 | 0.0717 | 0.0 | 0.9439 | 0.8035 | 0.9521 | 0.0 | 0.1299 | 0.2839 | 0.0 | nan | 0.7355 | 0.7675 | 0.7422 | 0.4826 | 0.3027 | nan | 0.3715 | 0.4933 | 0.0 | 0.7896 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4421 | 0.0 | 0.0 | 0.6666 | 0.0 | 0.3881 | 0.4723 | 0.0 | nan | 0.0034 | 0.2350 | 0.0716 | 0.0 | 0.8305 | 0.7183 | 0.9229 | 0.0 | 0.1152 | 0.1992 | 0.0 | | 0.2856 | 174.07 | 9400 | 0.6091 | 0.3045 | 0.3713 | 0.8183 | nan | 0.8177 | 0.8508 | 0.7884 | 0.9070 | 0.3274 | nan | 0.4412 | 0.5971 | 0.0 | 0.9437 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6904 | 0.0 | 0.0 | 0.8760 | 0.0 | 0.5037 | 0.5471 | 0.0 | nan | 0.0023 | 0.4093 | 0.0729 | 0.0 | 0.9395 | 0.8289 | 0.9513 | 0.0000 | 0.1123 | 0.2745 | 0.0 | nan | 0.7401 | 0.7694 | 0.7705 | 0.4745 | 0.3070 | nan | 0.3570 | 0.4797 | 0.0 | 0.7901 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4370 | 0.0 | 0.0 | 0.6642 | 0.0 | 0.3879 | 0.4663 | 0.0 | nan | 0.0023 | 0.2356 | 0.0728 | 0.0 | 0.8358 | 0.7333 | 0.9230 | 0.0000 | 0.1034 | 0.1937 | 0.0 | | 0.2803 | 175.93 | 9500 | 0.6404 | 0.3009 | 0.3704 | 0.8084 | nan | 0.8365 | 0.8208 | 0.7833 | 0.9062 | 0.3050 | nan | 0.4405 | 0.6203 | 0.0 | 0.9443 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6940 | 0.0 | 0.0 | 0.8667 | 0.0 | 0.5055 | 0.5494 | 0.0 | nan | 0.0084 | 0.4148 | 0.0772 | 0.0 | 0.9424 | 0.8074 | 0.9551 | 0.0001 | 0.1077 | 0.2664 | 0.0 | nan | 0.7454 | 0.7459 | 0.7680 | 0.4316 | 0.2897 | nan | 0.3571 | 0.4866 | 0.0 | 0.7930 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4255 | 0.0 | 0.0 | 0.6652 | 0.0 | 0.3877 | 0.4601 | 0.0 | nan | 0.0084 | 0.2306 | 0.0771 | 0.0 | 0.8314 | 0.7178 | 0.9235 | 0.0001 | 0.0969 | 0.1889 | 0.0 | | 0.2924 | 177.78 | 9600 | 0.6156 | 0.3045 | 0.3723 | 0.8156 | nan | 0.8293 | 0.8420 | 0.8051 | 0.8964 | 0.3365 | nan | 0.4651 | 0.6281 | 0.0 | 0.9443 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6806 | 0.0 | 0.0 | 0.8777 | 0.0 | 0.4957 | 0.5434 | 0.0 | nan | 0.0043 | 0.4293 | 0.0774 | 0.0 | 0.9387 | 0.7942 | 0.9562 | 0.0 | 0.1178 | 0.2514 | 0.0 | nan | 0.7508 | 0.7606 | 0.7848 | 0.4617 | 0.3134 | nan | 0.3712 | 0.4903 | 0.0 | 0.7912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4384 | 0.0 | 0.0 | 0.6666 | 0.0 | 0.3850 | 0.4648 | 0.0 | nan | 0.0043 | 0.2308 | 0.0773 | 0.0 | 0.8320 | 0.7126 | 0.9232 | 0.0 | 0.1028 | 0.1836 | 0.0 | | 0.2911 | 179.63 | 9700 | 0.6039 | 0.3051 | 0.3743 | 0.8197 | nan | 0.8161 | 0.8573 | 0.8009 | 0.9013 | 0.3091 | nan | 0.4597 | 0.6407 | 0.0 | 0.9406 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7191 | 0.0 | 0.0 | 0.8787 | 0.0 | 0.5007 | 0.5561 | 0.0 | nan | 0.0046 | 0.4187 | 0.0825 | 0.0 | 0.9325 | 0.8335 | 0.9578 | 0.0000 | 0.1036 | 0.2642 | 0.0 | nan | 0.7434 | 0.7687 | 0.7825 | 0.4751 | 0.2917 | nan | 0.3667 | 0.4994 | 0.0 | 0.7998 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4127 | 0.0 | 0.0 | 0.6761 | 0.0 | 0.3878 | 0.4561 | 0.0 | nan | 0.0046 | 0.2352 | 0.0823 | 0.0 | 0.8393 | 0.7401 | 0.9235 | 0.0000 | 0.0883 | 0.1885 | 0.0 | | 0.3093 | 181.48 | 9800 | 0.6244 | 0.3021 | 0.3707 | 0.8132 | nan | 0.8240 | 0.8367 | 0.7819 | 0.9031 | 0.3158 | nan | 0.4523 | 0.6336 | 0.0 | 0.9419 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7047 | 0.0 | 0.0 | 0.8782 | 0.0 | 0.5024 | 0.5478 | 0.0 | nan | 0.0 | 0.4039 | 0.0761 | 0.0 | 0.9422 | 0.8036 | 0.9524 | 0.0 | 0.0992 | 0.2629 | 0.0 | nan | 0.7414 | 0.7575 | 0.7666 | 0.4537 | 0.2990 | nan | 0.3642 | 0.4913 | 0.0 | 0.7906 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4261 | 0.0 | 0.0 | 0.6655 | 0.0 | 0.3892 | 0.4639 | 0.0 | nan | 0.0 | 0.2339 | 0.0760 | 0.0 | 0.8311 | 0.7168 | 0.9226 | 0.0 | 0.0873 | 0.1892 | 0.0 | | 0.3194 | 183.33 | 9900 | 0.6384 | 0.3015 | 0.3707 | 0.8106 | nan | 0.8269 | 0.8295 | 0.7809 | 0.9036 | 0.3169 | nan | 0.4373 | 0.6407 | 0.0 | 0.9394 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7004 | 0.0 | 0.0 | 0.8774 | 0.0 | 0.4936 | 0.5511 | 0.0 | nan | 0.0004 | 0.4210 | 0.0726 | 0.0 | 0.9434 | 0.8072 | 0.9462 | 0.0 | 0.1149 | 0.2605 | 0.0 | nan | 0.7423 | 0.7508 | 0.7639 | 0.4418 | 0.2988 | nan | 0.3584 | 0.4963 | 0.0 | 0.7976 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4212 | 0.0 | 0.0 | 0.6662 | 0.0 | 0.3830 | 0.4618 | 0.0 | nan | 0.0004 | 0.2347 | 0.0725 | 0.0 | 0.8311 | 0.7208 | 0.9214 | 0.0 | 0.0993 | 0.1875 | 0.0 | | 0.3174 | 185.19 | 10000 | 0.6350 | 0.3022 | 0.3724 | 0.8117 | nan | 0.8240 | 0.8308 | 0.7789 | 0.9052 | 0.3152 | nan | 0.4703 | 0.6444 | 0.0 | 0.9424 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7116 | 0.0 | 0.0 | 0.8716 | 0.0 | 0.4736 | 0.5408 | 0.0 | nan | 0.0048 | 0.4202 | 0.0754 | 0.0 | 0.9437 | 0.8196 | 0.9525 | 0.0 | 0.1041 | 0.2872 | 0.0 | nan | 0.7413 | 0.7520 | 0.7629 | 0.4453 | 0.2976 | nan | 0.3701 | 0.4953 | 0.0 | 0.7962 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4152 | 0.0 | 0.0 | 0.6712 | 0.0 | 0.3749 | 0.4613 | 0.0 | nan | 0.0048 | 0.2337 | 0.0753 | 0.0 | 0.8324 | 0.7277 | 0.9234 | 0.0 | 0.0913 | 0.1997 | 0.0 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

atk1432/Taxi-v3

atk1432

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.36 +/- 2.68 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage model = load_from_hub(repo_id="atk1432/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])

askardada/recvischallenge_11_fold_1

askardada

peft

--- library_name: peft base_model: google/vit-base-patch16-224 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

PassbyGrocer/hreb-weibo

PassbyGrocer

transformers

token-classification

--- library_name: transformers license: apache-2.0 base_model: hfl/chinese-roberta-wwm-ext-large tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert_bilstm_mega_crf-ner-weibo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_bilstm_mega_crf-ner-weibo This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2341 - Precision: 0.6657 - Recall: 0.7075 - F1: 0.6860 - Accuracy: 0.9683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.7329 | 1.0 | 11 | 0.4907 | 0.0 | 0.0 | 0.0 | 0.9274 | | 0.4493 | 2.0 | 22 | 0.3486 | 0.0 | 0.0 | 0.0 | 0.9274 | | 0.3203 | 3.0 | 33 | 0.2384 | 0.2941 | 0.0629 | 0.1036 | 0.9354 | | 0.2259 | 4.0 | 44 | 0.1618 | 0.5219 | 0.4874 | 0.5041 | 0.9586 | | 0.1617 | 5.0 | 55 | 0.1318 | 0.5476 | 0.5975 | 0.5714 | 0.9642 | | 0.1171 | 6.0 | 66 | 0.1202 | 0.5718 | 0.6509 | 0.6088 | 0.9676 | | 0.0956 | 7.0 | 77 | 0.1210 | 0.6022 | 0.6855 | 0.6412 | 0.9692 | | 0.0666 | 8.0 | 88 | 0.1208 | 0.5951 | 0.6887 | 0.6385 | 0.9690 | | 0.0567 | 9.0 | 99 | 0.1205 | 0.5963 | 0.7107 | 0.6485 | 0.9687 | | 0.0433 | 10.0 | 110 | 0.1219 | 0.6230 | 0.7170 | 0.6667 | 0.9699 | | 0.0333 | 11.0 | 121 | 0.1365 | 0.6375 | 0.6635 | 0.6502 | 0.9687 | | 0.0309 | 12.0 | 132 | 0.1421 | 0.6011 | 0.6918 | 0.6433 | 0.9672 | | 0.0239 | 13.0 | 143 | 0.1460 | 0.6398 | 0.6981 | 0.6677 | 0.9687 | | 0.0235 | 14.0 | 154 | 0.1539 | 0.6518 | 0.6887 | 0.6697 | 0.9687 | | 0.0188 | 15.0 | 165 | 0.1604 | 0.6656 | 0.6824 | 0.6739 | 0.9694 | | 0.0193 | 16.0 | 176 | 0.1625 | 0.6471 | 0.6918 | 0.6687 | 0.9687 | | 0.0155 | 17.0 | 187 | 0.1758 | 0.6770 | 0.6855 | 0.6813 | 0.9683 | | 0.0148 | 18.0 | 198 | 0.1714 | 0.6506 | 0.6792 | 0.6646 | 0.9688 | | 0.014 | 19.0 | 209 | 0.1626 | 0.6391 | 0.7296 | 0.6814 | 0.9674 | | 0.0116 | 20.0 | 220 | 0.1718 | 0.6459 | 0.7170 | 0.6796 | 0.9687 | | 0.0111 | 21.0 | 231 | 0.1840 | 0.6718 | 0.6824 | 0.6771 | 0.9694 | | 0.0097 | 22.0 | 242 | 0.1807 | 0.6479 | 0.6887 | 0.6677 | 0.9677 | | 0.0098 | 23.0 | 253 | 0.1787 | 0.6391 | 0.7296 | 0.6814 | 0.9664 | | 0.0089 | 24.0 | 264 | 0.1877 | 0.6518 | 0.6887 | 0.6697 | 0.9688 | | 0.0077 | 25.0 | 275 | 0.1896 | 0.6519 | 0.6950 | 0.6728 | 0.9693 | | 0.008 | 26.0 | 286 | 0.1915 | 0.6608 | 0.7107 | 0.6848 | 0.9690 | | 0.0079 | 27.0 | 297 | 0.2008 | 0.6606 | 0.6792 | 0.6698 | 0.9687 | | 0.0072 | 28.0 | 308 | 0.1961 | 0.6486 | 0.7138 | 0.6796 | 0.9681 | | 0.0067 | 29.0 | 319 | 0.2040 | 0.6617 | 0.7013 | 0.6809 | 0.9691 | | 0.0063 | 30.0 | 330 | 0.2028 | 0.6725 | 0.7296 | 0.6998 | 0.9688 | | 0.0056 | 31.0 | 341 | 0.2053 | 0.6716 | 0.7201 | 0.6950 | 0.9689 | | 0.0073 | 32.0 | 352 | 0.2088 | 0.6465 | 0.6730 | 0.6595 | 0.9674 | | 0.0061 | 33.0 | 363 | 0.1936 | 0.6138 | 0.7296 | 0.6667 | 0.9673 | | 0.0057 | 34.0 | 374 | 0.2061 | 0.6596 | 0.6824 | 0.6708 | 0.9683 | | 0.0062 | 35.0 | 385 | 0.2077 | 0.6627 | 0.7044 | 0.6829 | 0.9680 | | 0.0046 | 36.0 | 396 | 0.2133 | 0.6738 | 0.6950 | 0.6842 | 0.9689 | | 0.0062 | 37.0 | 407 | 0.2029 | 0.6696 | 0.7201 | 0.6939 | 0.9680 | | 0.0058 | 38.0 | 418 | 0.2039 | 0.6707 | 0.7044 | 0.6871 | 0.9678 | | 0.0047 | 39.0 | 429 | 0.2055 | 0.6667 | 0.7233 | 0.6938 | 0.9685 | | 0.0049 | 40.0 | 440 | 0.2105 | 0.6757 | 0.7075 | 0.6912 | 0.9692 | | 0.0048 | 41.0 | 451 | 0.2052 | 0.6667 | 0.7107 | 0.6880 | 0.9683 | | 0.0049 | 42.0 | 462 | 0.2081 | 0.6590 | 0.7170 | 0.6867 | 0.9687 | | 0.0063 | 43.0 | 473 | 0.2011 | 0.6552 | 0.7170 | 0.6847 | 0.9683 | | 0.0046 | 44.0 | 484 | 0.1994 | 0.6477 | 0.7170 | 0.6806 | 0.9676 | | 0.0047 | 45.0 | 495 | 0.2122 | 0.6790 | 0.6918 | 0.6854 | 0.9693 | | 0.0048 | 46.0 | 506 | 0.2082 | 0.6609 | 0.7233 | 0.6907 | 0.9687 | | 0.0042 | 47.0 | 517 | 0.2140 | 0.6769 | 0.6918 | 0.6843 | 0.9695 | | 0.0054 | 48.0 | 528 | 0.2054 | 0.6514 | 0.7170 | 0.6826 | 0.9681 | | 0.0037 | 49.0 | 539 | 0.2070 | 0.6686 | 0.7107 | 0.6890 | 0.9689 | | 0.0045 | 50.0 | 550 | 0.2093 | 0.6514 | 0.7170 | 0.6826 | 0.9686 | | 0.004 | 51.0 | 561 | 0.2163 | 0.6787 | 0.7107 | 0.6943 | 0.9698 | | 0.0038 | 52.0 | 572 | 0.2173 | 0.6706 | 0.7107 | 0.6901 | 0.9694 | | 0.0042 | 53.0 | 583 | 0.2156 | 0.6745 | 0.7233 | 0.6980 | 0.9694 | | 0.0039 | 54.0 | 594 | 0.2190 | 0.6727 | 0.6981 | 0.6852 | 0.9689 | | 0.0037 | 55.0 | 605 | 0.2213 | 0.6767 | 0.7044 | 0.6903 | 0.9687 | | 0.0043 | 56.0 | 616 | 0.2247 | 0.6829 | 0.7044 | 0.6935 | 0.9690 | | 0.0034 | 57.0 | 627 | 0.2291 | 0.6789 | 0.6981 | 0.6884 | 0.9689 | | 0.0046 | 58.0 | 638 | 0.2258 | 0.6737 | 0.7075 | 0.6902 | 0.9686 | | 0.0033 | 59.0 | 649 | 0.2254 | 0.6736 | 0.7138 | 0.6931 | 0.9689 | | 0.0036 | 60.0 | 660 | 0.2255 | 0.6758 | 0.7013 | 0.6883 | 0.9690 | | 0.0038 | 61.0 | 671 | 0.2200 | 0.6580 | 0.7138 | 0.6848 | 0.9682 | | 0.0036 | 62.0 | 682 | 0.2210 | 0.6657 | 0.7075 | 0.6860 | 0.9687 | | 0.0039 | 63.0 | 693 | 0.2237 | 0.6647 | 0.7107 | 0.6869 | 0.9682 | | 0.0039 | 64.0 | 704 | 0.2295 | 0.6727 | 0.6981 | 0.6852 | 0.9688 | | 0.0032 | 65.0 | 715 | 0.2271 | 0.6707 | 0.7044 | 0.6871 | 0.9687 | | 0.0038 | 66.0 | 726 | 0.2290 | 0.6677 | 0.7013 | 0.6840 | 0.9687 | | 0.0033 | 67.0 | 737 | 0.2260 | 0.6617 | 0.7013 | 0.6809 | 0.9682 | | 0.0038 | 68.0 | 748 | 0.2250 | 0.6676 | 0.7138 | 0.6900 | 0.9686 | | 0.0037 | 69.0 | 759 | 0.2254 | 0.6618 | 0.7075 | 0.6839 | 0.9684 | | 0.0039 | 70.0 | 770 | 0.2281 | 0.6687 | 0.6981 | 0.6831 | 0.9687 | | 0.0036 | 71.0 | 781 | 0.2317 | 0.6687 | 0.6981 | 0.6831 | 0.9687 | | 0.0034 | 72.0 | 792 | 0.2272 | 0.6609 | 0.7170 | 0.6878 | 0.9686 | | 0.0036 | 73.0 | 803 | 0.2278 | 0.6756 | 0.7138 | 0.6942 | 0.9687 | | 0.0035 | 74.0 | 814 | 0.2287 | 0.6677 | 0.7075 | 0.6870 | 0.9683 | | 0.0034 | 75.0 | 825 | 0.2283 | 0.6686 | 0.7107 | 0.6890 | 0.9681 | | 0.0032 | 76.0 | 836 | 0.2331 | 0.6657 | 0.7075 | 0.6860 | 0.9672 | | 0.0041 | 77.0 | 847 | 0.2357 | 0.6598 | 0.7075 | 0.6829 | 0.9675 | | 0.0033 | 78.0 | 858 | 0.2352 | 0.6706 | 0.7170 | 0.6930 | 0.9676 | | 0.0039 | 79.0 | 869 | 0.2363 | 0.6696 | 0.7075 | 0.6881 | 0.9689 | | 0.0036 | 80.0 | 880 | 0.2367 | 0.6627 | 0.6918 | 0.6769 | 0.9685 | | 0.0032 | 81.0 | 891 | 0.2369 | 0.6607 | 0.6981 | 0.6789 | 0.9683 | | 0.0036 | 82.0 | 902 | 0.2331 | 0.6696 | 0.7201 | 0.6939 | 0.9687 | | 0.0036 | 83.0 | 913 | 0.2286 | 0.6599 | 0.7138 | 0.6858 | 0.9682 | | 0.0034 | 84.0 | 924 | 0.2276 | 0.6637 | 0.7138 | 0.6879 | 0.9687 | | 0.0035 | 85.0 | 935 | 0.2286 | 0.6647 | 0.7107 | 0.6869 | 0.9687 | | 0.0031 | 86.0 | 946 | 0.2296 | 0.6667 | 0.7044 | 0.6850 | 0.9689 | | 0.0036 | 87.0 | 957 | 0.2296 | 0.6677 | 0.7075 | 0.6870 | 0.9687 | | 0.0033 | 88.0 | 968 | 0.2299 | 0.6706 | 0.7170 | 0.6930 | 0.9688 | | 0.0033 | 89.0 | 979 | 0.2301 | 0.6618 | 0.7138 | 0.6868 | 0.9683 | | 0.0034 | 90.0 | 990 | 0.2320 | 0.6766 | 0.7170 | 0.6962 | 0.9687 | | 0.0031 | 91.0 | 1001 | 0.2309 | 0.6766 | 0.7170 | 0.6962 | 0.9686 | | 0.0033 | 92.0 | 1012 | 0.2315 | 0.6736 | 0.7138 | 0.6931 | 0.9685 | | 0.0037 | 93.0 | 1023 | 0.2333 | 0.6696 | 0.7075 | 0.6881 | 0.9684 | | 0.0031 | 94.0 | 1034 | 0.2342 | 0.6696 | 0.7075 | 0.6881 | 0.9684 | | 0.0029 | 95.0 | 1045 | 0.2351 | 0.6687 | 0.7044 | 0.6861 | 0.9683 | | 0.004 | 96.0 | 1056 | 0.2347 | 0.6667 | 0.7044 | 0.6850 | 0.9683 | | 0.0032 | 97.0 | 1067 | 0.2346 | 0.6667 | 0.7044 | 0.6850 | 0.9683 | | 0.0033 | 98.0 | 1078 | 0.2343 | 0.6667 | 0.7044 | 0.6850 | 0.9683 | | 0.0032 | 99.0 | 1089 | 0.2341 | 0.6647 | 0.7044 | 0.6840 | 0.9682 | | 0.0034 | 100.0 | 1100 | 0.2341 | 0.6657 | 0.7075 | 0.6860 | 0.9683 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.4.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

lukaspetersson/gemma2b-refuse-addition

lukaspetersson

peft

--- license: other library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: google/gemma-2b model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 999 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ISEGURA/distilbert-base-multilingual-cased-autext2024_05-12-2024_detection

ISEGURA

transformers

text-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BhabhaAI/Mistral-translation-classify

BhabhaAI

transformers

text-generation

--- library_name: transformers license: apache-2.0 datasets: - BhabhaAI/translation-classify language: - en --- # Mistral Translation Classify This is a fine tuned model on the [translation-classify dataset](https://huggingface.co/datasets/BhabhaAI/translation-classify) to classify whether we should translate an example. It achieves 94% accuracy on validation dataset. ## Examples Some question when translated does not remain meaningful/correct. The goal is to avoid such examples. This includes coding, word-count, spelling error detection etc. Take a look at [dataset](https://huggingface.co/datasets/BhabhaAI/translation-classify) for examples

kaimclone1/falcon-7b-instruct-ft-adapters

kaimclone1

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0

RachidAR/Qwen2.5-Coder-1.5B-Q5_K_M-GGUF

RachidAR

transformers

text-generation

--- base_model: Qwen/Qwen2.5-Coder-1.5B language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B/blob/main/LICENSE pipeline_tag: text-generation tags: - code - qwen - qwen-coder - codeqwen - llama-cpp - gguf-my-repo --- # RachidAR/Qwen2.5-Coder-1.5B-Q5_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-Coder-1.5B`](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo RachidAR/Qwen2.5-Coder-1.5B-Q5_K_M-GGUF --hf-file qwen2.5-coder-1.5b-q5_k_m-imat.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo RachidAR/Qwen2.5-Coder-1.5B-Q5_K_M-GGUF --hf-file qwen2.5-coder-1.5b-q5_k_m-imat.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo RachidAR/Qwen2.5-Coder-1.5B-Q5_K_M-GGUF --hf-file qwen2.5-coder-1.5b-q5_k_m-imat.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo RachidAR/Qwen2.5-Coder-1.5B-Q5_K_M-GGUF --hf-file qwen2.5-coder-1.5b-q5_k_m-imat.gguf -c 2048 ```

MonishSoundarRaj/environment_around_alumni_pavilion_building_uncc

MonishSoundarRaj

diffusers

text-to-image

--- tags: - autotrain - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks ground license: openrail++ --- # AutoTrain SDXL LoRA DreamBooth - leonickson1/environment_around_alumni_pavilion_building_uncc <Gallery /> ## Model description These are leonickson1/environment_around_alumni_pavilion_building_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: None. ## Trigger words You should use photo of a sks ground to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](leonickson1/environment_around_alumni_pavilion_building_uncc/tree/main) them in the Files & versions tab.

frankjoshua/stable-diffusion-3.5-large

frankjoshua

diffusers

text-to-image

--- license: other license_name: stabilityai-ai-community license_link: LICENSE.md tags: - text-to-image - stable-diffusion - diffusers inference: true extra_gated_prompt: >- By clicking "Agree", you agree to the [License Agreement](https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md) and acknowledge Stability AI's [Privacy Policy](https://stability.ai/privacy-policy). extra_gated_fields: Name: text Email: text Country: country Organization or Affiliation: text Receive email updates and promotions on Stability AI products, services, and research?: type: select options: - 'Yes' - 'No' What do you intend to use the model for?: type: select options: - Research - Personal use - Creative Professional - Startup - Enterprise I agree to the License Agreement and acknowledge Stability AI's Privacy Policy: checkbox language: - en pipeline_tag: text-to-image --- # Stable Diffusion 3.5 Large  ## Model  [Stable Diffusion 3.5 Large](https://stability.ai/news/introducing-stable-diffusion-3-5) is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency. Please note: This model is released under the [Stability Community License](https://stability.ai/community-license-agreement). Visit [Stability AI](https://stability.ai/license) to learn or [contact us](https://stability.ai/enterprise) for commercial licensing details. ### Model Description - **Developed by:** Stability AI - **Model type:** MMDiT text-to-image generative model - **Model Description:** This model generates images based on text prompts. It is a [Multimodal Diffusion Transformer](https://arxiv.org/abs/2403.03206) that use three fixed, pretrained text encoders, and with QK-normalization to improve training stability. ### License - **Community License:** Free for research, non-commercial, and commercial use for organizations or individuals with less than $1M in total annual revenue. More details can be found in the [Community License Agreement](https://stability.ai/community-license-agreement). Read more at https://stability.ai/license. - **For individuals and organizations with annual revenue above $1M**: please [contact us](https://stability.ai/enterprise) to get an Enterprise License. ### Model Sources For local or self-hosted use, we recommend [ComfyUI](https://github.com/comfyanonymous/ComfyUI) for node-based UI inference, or [diffusers](https://github.com/huggingface/diffusers) or [GitHub](https://github.com/Stability-AI/sd3.5) for programmatic use. - **ComfyUI:** [Github](https://github.com/comfyanonymous/ComfyUI), [Example Workflow](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) - **Huggingface Space:** [Space](https://huggingface.co/spaces/stabilityai/stable-diffusion-3.5-large) - **Diffusers**: [See below](#using-with-diffusers). - **GitHub**: [GitHub](https://github.com/Stability-AI/sd3.5). - **API Endpoints:** - [Stability AI API](https://platform.stability.ai/docs/api-reference#tag/Generate/paths/~1v2beta~1stable-image~1generate~1sd3/post) - [Replicate](https://replicate.com/stability-ai/stable-diffusion-3.5-large) - [Deepinfra](https://deepinfra.com/stabilityai/sd3.5) ### Implementation Details - **QK Normalization:** Implements the QK normalization technique to improve training Stability. - **Text Encoders：** - CLIPs: [OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip), [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main), context length 77 tokens - T5: [T5-xxl](https://huggingface.co/google/t5-v1_1-xxl), context length 77/256 tokens at different stages of training - **Training Data and Strategy:** This model was trained on a wide variety of data, including synthetic data and filtered publicly available data. For more technical details of the original MMDiT architecture, please refer to the [Research paper](https://stability.ai/news/stable-diffusion-3-research-paper). ### Model Performance See [blog](https://stability.ai/news/introducing-stable-diffusion-3-5) for our study about comparative performance in prompt adherence and aesthetic quality. ## File Structure Click here to access the [Files and versions tab](https://huggingface.co/stabilityai/stable-diffusion-3.5-large/tree/main) ```│ ├── text_encoders/ │ ├── README.md │ ├── clip_g.safetensors │ ├── clip_l.safetensors │ ├── t5xxl_fp16.safetensors │ └── t5xxl_fp8_e4m3fn.safetensors │ ├── README.md ├── LICENSE ├── sd3_large.safetensors ├── SD3.5L_example_workflow.json └── sd3_large_demo.png ** File structure below is for diffusers integration** ├── scheduler/ ├── text_encoder/ ├── text_encoder_2/ ├── text_encoder_3/ ├── tokenizer/ ├── tokenizer_2/ ├── tokenizer_3/ ├── transformer/ ├── vae/ └── model_index.json ``` ## Using with Diffusers Upgrade to the latest version of the [🧨 diffusers library](https://github.com/huggingface/diffusers) ``` pip install -U diffusers ``` and then you can run ```py import torch from diffusers import StableDiffusion3Pipeline pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16) pipe = pipe.to("cuda") image = pipe( "A capybara holding a sign that reads Hello World", num_inference_steps=28, guidance_scale=3.5, ).images[0] image.save("capybara.png") ``` ### Quantizing the model with diffusers Reduce your VRAM usage and have the model fit on 🤏 VRAM GPUs ``` pip install bitsandbytes ``` ```py from diffusers import BitsAndBytesConfig, SD3Transformer2DModel from diffusers import StableDiffusion3Pipeline import torch model_id = "stabilityai/stable-diffusion-3.5-large" nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) model_nf4 = SD3Transformer2DModel.from_pretrained( model_id, subfolder="transformer", quantization_config=nf4_config, torch_dtype=torch.bfloat16 ) pipeline = StableDiffusion3Pipeline.from_pretrained( model_id, transformer=model_nf4, torch_dtype=torch.bfloat16 ) pipeline.enable_model_cpu_offload() prompt = "A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus, basking in a river of melted butter amidst a breakfast-themed landscape. It features the distinctive, bulky body shape of a hippo. However, instead of the usual grey skin, the creature's body resembles a golden-brown, crispy waffle fresh off the griddle. The skin is textured with the familiar grid pattern of a waffle, each square filled with a glistening sheen of syrup. The environment combines the natural habitat of a hippo with elements of a breakfast table setting, a river of warm, melted butter, with oversized utensils or plates peeking out from the lush, pancake-like foliage in the background, a towering pepper mill standing in for a tree. As the sun rises in this fantastical world, it casts a warm, buttery glow over the scene. The creature, content in its butter river, lets out a yawn. Nearby, a flock of birds take flight" image = pipeline( prompt=prompt, num_inference_steps=28, guidance_scale=4.5, max_sequence_length=512, ).images[0] image.save("whimsical.png") ``` ### Fine-tuning Please see the fine-tuning guide [here](https://stabilityai.notion.site/Stable-Diffusion-3-5-Large-Fine-tuning-Tutorial-11a61cdcd1968027a15bdbd7c40be8c6). ## Uses ### Intended Uses Intended uses include the following: * Generation of artworks and use in design and other artistic processes. * Applications in educational or creative tools. * Research on generative models, including understanding the limitations of generative models. All uses of the model must be in accordance with our [Acceptable Use Policy](https://stability.ai/use-policy). ### Out-of-Scope Uses The model was not trained to be factual or true representations of people or events. As such, using the model to generate such content is out-of-scope of the abilities of this model. ## Safety As part of our safety-by-design and responsible AI deployment approach, we take deliberate measures to ensure Integrity starts at the early stages of development. We implement safety measures throughout the development of our models. We have implemented safety mitigations that are intended to reduce the risk of certain harms, however we recommend that developers conduct their own testing and apply additional mitigations based on their specific use cases. For more about our approach to Safety, please visit our [Safety page](https://stability.ai/safety). ### Integrity Evaluation Our integrity evaluation methods include structured evaluations and red-teaming testing for certain harms. Testing was conducted primarily in English and may not cover all possible harms. ### Risks identified and mitigations: * Harmful content: We have used filtered data sets when training our models and implemented safeguards that attempt to strike the right balance between usefulness and preventing harm. However, this does not guarantee that all possible harmful content has been removed. TAll developers and deployers should exercise caution and implement content safety guardrails based on their specific product policies and application use cases. * Misuse: Technical limitations and developer and end-user education can help mitigate against malicious applications of models. All users are required to adhere to our [Acceptable Use Policy](https://stability.ai/use-policy), including when applying fine-tuning and prompt engineering mechanisms. Please reference the Stability AI Acceptable Use Policy for information on violative uses of our products. * Privacy violations: Developers and deployers are encouraged to adhere to privacy regulations with techniques that respect data privacy. ### Contact Please report any issues with the model or contact us: * Safety issues: [email protected] * Security issues: [email protected] * Privacy issues: [email protected] * License and general: https://stability.ai/license * Enterprise license: https://stability.ai/enterprise

asenella/mmnist_MVAEconfig_resnet_seed_0_ratio_05_c

asenella

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

asenella/mmnist_MVAEconfig2_seed_0_ratio_00_c

asenella

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

mci29/sn29_w1m3_gf2z

mci29

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

xu1998hz/43_dpo_lora_ucb_rand

xu1998hz

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

iczaw/prompt-diffusion-diffusers

iczaw

diffusers

image-to-text

--- library_name: diffusers base_models: - runwayml/stable-diffusion-v1-5 - lllyasviel/ControlNet pipeline_tag: image-to-text --- [Prompt diffusion](https://huggingface.co/zhendongw/prompt-diffusion) converted to Diffusers.

DBangshu/V4_Base_GPT2_e5_4_3

DBangshu

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

AugustoReies/vit-base-patch16-224-mascotas-DA

AugustoReies

transformers

image-classification

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-patch16-224-mascotas-DA results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-mascotas-DA This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1356 - Accuracy: 0.9625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00035 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.3161 | 0.9849 | 49 | 0.1356 | 0.9625 | | 0.157 | 1.9899 | 99 | 0.1231 | 0.95 | | 0.1355 | 2.9950 | 149 | 0.1380 | 0.9625 | | 0.0979 | 4.0 | 199 | 0.2714 | 0.925 | | 0.0788 | 4.9849 | 248 | 0.2664 | 0.9375 | | 0.0584 | 5.9095 | 294 | 0.2223 | 0.9375 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.19.1

cleanrl/Pitfall-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2

cleanrl

cleanrl

reinforcement-learning

--- tags: - Pitfall-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pitfall-v5 type: Pitfall-v5 metrics: - type: mean_reward value: -24.80 +/- 50.75 name: mean_reward verified: false --- # (CleanRL) **PPO** Agent Playing **Pitfall-v5** This is a trained model of a PPO agent playing Pitfall-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Pitfall-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/cleanrl/Pitfall-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Pitfall-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Pitfall-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Pitfall-v5 --seed 2 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Pitfall-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 2, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

m3hrdadfi/wav2vec2-large-xlsr-persian-v3

m3hrdadfi

transformers

automatic-speech-recognition

--- language: fa datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week widget: - example_title: Common Voice sample 1 src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3/resolve/main/sample1.flac - example_title: Common Voice sample 2978 src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3/resolve/main/sample2978.flac - example_title: Common Voice sample 5168 src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3/resolve/main/sample5168.flac model-index: - name: XLSR Wav2Vec2 Persian (Farsi) V3 by Mehrdad Farahani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice fa type: common_voice args: fa metrics: - name: Test WER type: wer value: 10.36 --- # Wav2Vec2-Large-XLSR-53-Persian V3 ## Usage Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. **Requirements** ```bash # requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa !pip install jiwer !pip install parsivar !pip install num2fawords ``` **Normalizer** ```bash # Normalizer !wget -O normalizer.py https://huggingface.co/m3hrdadfi/"wav2vec2-large-xlsr-persian-v3/raw/main/dictionary.py !wget -O normalizer.py https://huggingface.co/m3hrdadfi/"wav2vec2-large-xlsr-persian-v3/raw/main/normalizer.py ``` **Downloading data** ```bash wget https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/fa.tar.gz tar -xzf fa.tar.gz rm -rf fa.tar.gz ``` **Cleaning** ```python from normalizer import normalizer def cleaning(text): if not isinstance(text, str): return None return normalizer({"sentence": text}, return_dict=False) data_dir = "/content/cv-corpus-6.1-2020-12-11/fa" test = pd.read_csv(f"{data_dir}/test.tsv", sep=" ") test["path"] = data_dir + "/clips/" + test["path"] print(f"Step 0: {len(test)}") test["status"] = test["path"].apply(lambda path: True if os.path.exists(path) else None) test = test.dropna(subset=["path"]) test = test.drop("status", 1) print(f"Step 1: {len(test)}") test["sentence"] = test["sentence"].apply(lambda t: cleaning(t)) test = test.dropna(subset=["sentence"]) print(f"Step 2: {len(test)}") test = test.reset_index(drop=True) print(test.head()) test = test[["path", "sentence"]] test.to_csv("/content/test.csv", sep=" ", encoding="utf-8", index=False) ``` **Prediction** ```python import numpy as np import pandas as pd import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset, load_metric import IPython.display as ipd model_name_or_path = "m3hrdadfi/wav2vec2-large-xlsr-persian-v3" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(model_name_or_path, device) processor = Wav2Vec2Processor.from_pretrained(model_name_or_path) model = Wav2Vec2ForCTC.from_pretrained(model_name_or_path).to(device) def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array = speech_array.squeeze().numpy() speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, processor.feature_extractor.sampling_rate) batch["speech"] = speech_array return batch def predict(batch): features = processor( batch["speech"], sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt", padding=True ) input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids) return batch dataset = load_dataset("csv", data_files={"test": "/content/test.csv"}, delimiter=" ")["test"] dataset = dataset.map(speech_file_to_array_fn) result = dataset.map(predict, batched=True, batch_size=4) ``` **WER Score** ```python wer = load_metric("wer") print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"]))) ``` **Output** ```python max_items = np.random.randint(0, len(result), 20).tolist() for i in max_items: reference, predicted = result["sentence"][i], result["predicted"][i] print("reference:", reference) print("predicted:", predicted) print('---') ``` ```text reference: ماجرا رو براش تعریف کردم اون گفت مریم اگه میدونی پسر خوبیه خب چه اشکالی داره با‌هاش بیش‌تر اشنا بشو predicted: ماجرا رو براش تعریف کردم اون گفت مریم اگه میدونی پسر خوبیه خب چه اشکالی داره با‌هاش بیش‌تر اشنا بشو --- reference: بیا پایین تو اجازه نداری بری اون بالا predicted: بیا پایین تو اجازه نداری بری اون بالا --- reference: هر روز یک دو مداد کش می رفتتم تااین که تا پایان ترم از تمامی دوستانم مداد برداشته بودم predicted: هر روز یک دو مداد کش می رفتم تااین که تا پایین ترم از تمامی دوستان و مداد برداشته بودم --- reference: فکر میکنی آروم میشینه predicted: فکر میکنی آروم میشینه --- reference: هرکسی با گوشی هوشمند خود میتواند با کایلا متصل گردد در یک محدوده مکانی predicted: هرکسی با گوشی هوشمند خود میتواند با کایلا متصل گردد در یک محدوده مکانی --- reference: برو از مهرداد بپرس predicted: برو از مهرداد بپرس --- reference: می خواهم شما را با این قدم‌ها آشنا کنم predicted: می خواهم شما را با این قدم‌ها آشنا کنم --- reference: میدونم یه روز دوباره می تونم تو رو ببینم predicted: میدونم یه روز دوباره می تونم تو رو ببینم --- reference: بسیار خوب خواهد بود دعوت او را بپذیری predicted: بسیار خوب خواهد بود دعوت او را بپذیری --- reference: بهت بگن آشغالی خوبه predicted: بهت بگن آشغالی خوبه --- reference: چرا معاشرت با هم ایمانان ما را محفوظ نگه میدارد predicted: چرا معاشرت با هم ایمانان آ را م حفوظ نگه میدارد --- reference: بولیوی پس از گویان فقیر‌ترین کشور آمریکای جنوبی است predicted: بولیوی پس از گویان فقیر‌ترین کشور آمریکای جنوبی است --- reference: بعد از مدتی اینکار برایم عادی شد predicted: بعد از مدتی اینکار برایم عادو شد --- reference: به نظر اون هم همینطوره predicted: به نظر اون هم همینطوره --- reference: هیچ مایونز ی دارید predicted: هیچ مایونز ی دارید --- reference: هیچ یک از انان کاری به سنگ نداشتند predicted: هیچ شک از انان کاری به سنگ نداشتند --- reference: می خواهم کمی کتاب شعر ببینم predicted: می خواهم کتاب شعر ببینم --- reference: همین شوهر فهیمه مگه نمی گفتی فرمانده بوده کو predicted: همین شوهر فهیمه بینامی گفتی فهمانده بود کو --- reference: اون جا‌ها کسی رو نمیبینی که تو دستش کتاب نباشه predicted: اون جا‌ها کسی رو نمیبینی که تو دستش کتاب نباشه --- reference: زندان رفتن من در این سال‌های اخیر برام شانس بزرگی بود که معما و مشکل چندین سال‌هام را حل کرد predicted: زندان رفتن من در این سال‌ها اخی براب شانس بزرگی بود که معما و مشکل چندین سال‌هام را حل کرد --- ``` ## Evaluation **Test Result:** - WER: 10.36%

okaris/autotrain-hate-speech-3k-89642143970

okaris

transformers

text-classification

--- tags: - autotrain - text-regression language: - en widget: - text: "I love AutoTrain" datasets: - okaris/autotrain-data-hate-speech-3k co2_eq_emissions: emissions: 0.023898445665108296 --- # Model Trained Using AutoTrain - Problem type: Single Column Regression - Model ID: 89642143970 - CO2 Emissions (in grams): 0.0239 ## Validation Metrics - Loss: 1.768 - MSE: 1.768 - MAE: 1.007 - R2: 0.604 - RMSE: 1.330 - Explained Variance: 0.614 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/okaris/autotrain-hate-speech-3k-89642143970 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("okaris/autotrain-hate-speech-3k-89642143970", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("okaris/autotrain-hate-speech-3k-89642143970", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

Darshan03/Edu-Model-v7

Darshan03

transformers

text-generation

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Xu-Ouyang/pythia-14m-int3-step36000-GPTQ-wikitext2

Xu-Ouyang

transformers

text-generation

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