Upload folder using huggingface_hub

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

@@ -0,0 +1,539 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:3056
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: intfloat/e5-base-v2
+widget:
+- source_sentence: Assess the strengths and weaknesses of initiatives aimed at growing
+    indigenous agribusiness.
+  sentences:
+  - 'In Chile, 80% of sources go to irrigation and agriculture, making irrigation
+    in agriculture a relevant element to consider. The main aspects associated with
+    irrigation in agriculture are salinity, toxicity, and microbiological quality
+    due to pathogenic organisms present in wastewater. When discussing irrigation,
+    the type of irrigation must be taken into account, as there are globally two types:
+    restrictive irrigation, which applies to products eaten raw, and irrigation without
+    restriction, which has no significant effects on agriculture, animals, or humans.'
+  - Mr Yoshiyuki Arima discussed the World Bank's focus on sustainable solutions to
+    challenges like climate change and gender equality. The World Bank is moving from
+    Green Bonds to Sustainable Development Bonds, using SDGs as a framework. They
+    are working with the Government Pension Investment Fund on research related to
+    SDGs.
+  - Technical and leadership development to grow indigenous agribusiness. Commercialisation
+    and access to market channels – both domestic and international – for indigenous
+    goods and services. Building networks to strengthen and increase participation
+    in the food system of indigenous people in the Asia Pacific region.
+- source_sentence: What is the largest water-consuming sector in Australia's economy?
+  sentences:
+  - Navarrot holds a Minor in Sustainability Studies.
+  - Australia’s agricultural sector is the largest water consuming sector in the economy,
+    accounting for 65 percent of total consumption in 2005. In the Murray-Darling
+    Basin, climate change will lead to decreased water levels and difficulties meeting
+    demand for irrigation while maintaining environmental flows. Additionally, vegetation
+    will consume more water under higher temperatures.
+  - The project contributes to the implementation of the APEC Food Security Roadmap
+    Towards 2030, focusing on food production, processing, and distribution. It includes
+    targets such as improving food system related digital literacy, promoting public-private
+    investment, modernizing food storage facilities, and sharing best practices among
+    APEC economies.
+- source_sentence: How would you use anaerobic digestion to reduce landfill reliance
+    in a city?
+  sentences:
+  - 'Innovation Approach: Technologies like anaerobic digestion and microbial transformation
+    create biogas and animal feed, turning waste into valuable resources and reducing
+    landfill reliance.'
+  - The initiative started from the previous satellite communication project that
+    ITU implemented in the Pacific. ITU provided 9 economies with 93 units of satellite
+    ground stations, so the remote islands were connected with the satellites. For
+    the islands, the satellites became essential communication means when disaster
+    hits the region. For instance, when the hurricane hit in 2020, the satellite ground
+    stations were the only communication means when the economies tried to initiate
+    the disaster response efforts during the Covid lockdown. Additionally, according
+    to ITU’s assessment, this communication means were used by communities and remote
+    and previously unconnected communities for education and health, and to provide
+    and receive government services.
+  - Mexico cited changes to the lengths of growing seasons, with increased temperatures
+    leading to shorter growing seasons in traditional agricultural areas as temperatures
+    become too extreme for both crops and livestock.
+- source_sentence: What would happen if APEC economies failed to coordinate across
+    borders?
+  sentences:
+  - APEC economies must co-ordinate across borders to facilitate services. The greater
+    the coherence between industry and governments, the greater the likelihood of
+    success.
+  - Another key issue she made clear about the food systems was the transaction costs.
+    To unlock the potential of the food systems, the transaction costs issues should
+    be addressed. These transactions are all over the food systems. They are encouraged
+    by farmers, their business partners to find each other, make deals and ensure
+    that these deals are enforced. While the transactions being essential to the production
+    of goods, the costs following them drive farmers to choose quantity over quality
+    at the expense of the environment, which ultimately affect consumers product choices.
+  - '• Mortality risk: lack of real time data to react.
+
+    • Yield optimization: no proper water quality data for yield optimization.'
+- source_sentence: Identify the main goal of closing resource loops.
+  sentences:
+  - Closing resource loops aims to create new value through the reuse and recycling
+    of used materials.
+  - Shelf life can be extended up to 18 month, would this violate the expiration date?
+  - Closing resource loops aims to create new value through the reuse and recycling
+    of used materials.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on intfloat/e5-base-v2
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.7447643979057592
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8992146596858639
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.93717277486911
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9607329842931938
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.7447643979057592
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.32504363001745196
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.20863874345549735
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.10863874345549739
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.6882635253054101
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8697643979057592
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9212478184991274
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9526614310645725
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.849824960377896
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8267877919055926
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8125610657293678
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on intfloat/e5-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2). 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:** [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) <!-- at revision f52bf8ec8c7124536f0efb74aca902b2995e5bcd -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **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': 512, 'do_lower_case': False, 'architecture': '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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Identify the main goal of closing resource loops.',
+    'Closing resource loops aims to create new value through the reuse and recycling of used materials.',
+    'Shelf life can be extended up to 18 month, would this violate the expiration date?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.8682, 0.4450],
+#         [0.8682, 1.0000, 0.4960],
+#         [0.4450, 0.4960, 1.0000]])
+```
+
+<!--
+### 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.7448     |
+| cosine_accuracy@3   | 0.8992     |
+| cosine_accuracy@5   | 0.9372     |
+| cosine_accuracy@10  | 0.9607     |
+| cosine_precision@1  | 0.7448     |
+| cosine_precision@3  | 0.325      |
+| cosine_precision@5  | 0.2086     |
+| cosine_precision@10 | 0.1086     |
+| cosine_recall@1     | 0.6883     |
+| cosine_recall@3     | 0.8698     |
+| cosine_recall@5     | 0.9212     |
+| cosine_recall@10    | 0.9527     |
+| **cosine_ndcg@10**  | **0.8498** |
+| cosine_mrr@10       | 0.8268     |
+| cosine_map@100      | 0.8126     |
+
+<!--
+## 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: 3,056 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: 7 tokens</li><li>mean: 17.94 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 82.66 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                             | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+  |:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How does the proximity of energy generation to consumption benefit floating solar plants?</code> | <code>What are the benefits of using a floating solar plant? At first, the interest is the use solar energy to generate electricity. The performance peak of solar panels is at 25 degrees Celcius, anything above generates a performance loss of 0.4%. Thus, when using water as a cooling system, the photovoltaic panel stays close to 25 degrees. Another aspect to consider is the point of energy consumption, which is close to the generation point.</code> |
+  | <code>Who won the Chilean award for women entrepreneurs at the regional level?</code>                  | <code>Mrs Curumilla won the Chilean award for women entrepreneurs at the regional level.</code>                                                                                                                                                                                                                                                                                                                                                                      |
+  | <code>How did the follow-up survey contribute to the establishment of working groups?</code>           | <code>The answers and interventions collected from the survey helped establish the different working groups and address common challenges in the workshop.</code>                                                                                                                                                                                                                                                                                                    |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          384,
+          256
+      ],
+      "matryoshka_weights": [
+          1.0,
+          0.8,
+          0.6,
+          0.4
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 6
+- `per_device_eval_batch_size`: 6
+- `num_train_epochs`: 4
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 6
+- `per_device_eval_batch_size`: 6
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_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`: 4
+- `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`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `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
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | cosine_ndcg@10 |
+|:------:|:----:|:--------------:|
+| 0.7812 | 100  | 0.7980         |
+| 1.0    | 128  | 0.8078         |
+| 1.5625 | 200  | 0.8259         |
+| 2.0    | 256  | 0.8463         |
+| 2.3438 | 300  | 0.8446         |
+| 3.0    | 384  | 0.8483         |
+| 3.125  | 400  | 0.8498         |
+
+
+### Framework Versions
+- Python: 3.10.18
+- Sentence Transformers: 5.0.0
+- Transformers: 4.53.1
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.8.1
+- Datasets: 2.14.0
+- Tokenizers: 0.21.2
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### 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.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.53.1",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/Information-Retrieval_evaluation_results.csv

@@ -0,0 +1,5 @@
+epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
+1.0,128,0.7185863874345549,0.8429319371727748,0.8743455497382199,0.9267015706806283,0.7185863874345549,0.6638307155322862,0.30497382198952877,0.8150087260034904,0.19214659685863875,0.8516579406631762,0.10340314136125654,0.9105584642233856,0.7891885855563868,0.8078339615296005,0.7720311813695621
+2.0,256,0.743455497382199,0.893979057591623,0.930628272251309,0.9568062827225131,0.743455497382199,0.68782722513089,0.32373472949389176,0.8647469458987783,0.20602094240837698,0.9112129144851657,0.10811518324607329,0.9485165794066317,0.8239637870855145,0.8462801265101514,0.8090963694552542
+3.0,384,0.7421465968586387,0.8965968586387435,0.9384816753926701,0.9594240837696335,0.7421465968586387,0.6863001745200699,0.32373472949389176,0.8664921465968587,0.20916230366492145,0.9223385689354275,0.10850785340314137,0.9513525305410123,0.8252659353444691,0.8483219990153066,0.8107808263896493
+4.0,512,0.731675392670157,0.8965968586387435,0.9358638743455497,0.9646596858638743,0.731675392670157,0.6747382198952879,0.3276614310645724,0.8708551483420592,0.2089005235602094,0.9197207678883071,0.1094240837696335,0.9583333333333333,0.8188943945815673,0.8457351092280725,0.8054387999493405

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34263a19eec29031a98481677982169825739eada679a697a633bb3c69199b86
+size 437951328

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}