Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:156
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: Snowflake/snowflake-arctic-embed-l
+widget:
+- source_sentence: What was the typical context length accepted by most models last
+    year?
+  sentences:
+  - 'Prompt injection is a natural consequence of this gulibility. I’ve seen precious
+    little progress on tackling that problem in 2024, and we’ve been talking about
+    it since September 2022.
+
+    I’m beginning to see the most popular idea of “agents” as dependent on AGI itself.
+    A model that’s robust against gulliblity is a very tall order indeed.
+
+    Evals really matter
+
+    Anthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):'
+  - 'Gemini 1.5 Pro also illustrated one of the key themes of 2024: increased context
+    lengths. Last year most models accepted 4,096 or 8,192 tokens, with the notable
+    exception of Claude 2.1 which accepted 200,000. Today every serious provider has
+    a 100,000+ token model, and Google’s Gemini series accepts up to 2 million.'
+  - 'Here’s the rest of the transcript. It’s bland and generic, but my phone can pitch
+    bland and generic Christmas movies to Netflix now!
+
+    LLM prices crashed, thanks to competition and increased efficiency
+
+    The past twelve months have seen a dramatic collapse in the cost of running a
+    prompt through the top tier hosted LLMs.
+
+    In December 2023 (here’s the Internet Archive for the OpenAI pricing page) OpenAI
+    were charging $30/million input tokens for GPT-4, $10/mTok for the then-new GPT-4
+    Turbo and $1/mTok for GPT-3.5 Turbo.'
+- source_sentence: What challenges does the author face when trying to evaluate multiple
+    LLMs?
+  sentences:
+  - 'We don’t yet know how to build GPT-4
+
+    Frustratingly, despite the enormous leaps ahead we’ve had this year, we are yet
+    to see an alternative model that’s better than GPT-4.
+
+    OpenAI released GPT-4 in March, though it later turned out we had a sneak peak
+    of it in February when Microsoft used it as part of the new Bing.
+
+    This may well change in the next few weeks: Google’s Gemini Ultra has big claims,
+    but isn’t yet available for us to try out.
+
+    The team behind Mistral are working to beat GPT-4 as well, and their track record
+    is already extremely strong considering their first public model only came out
+    in September, and they’ve released two significant improvements since then.'
+  - 'I find I have to work with an LLM for a few weeks in order to get a good intuition
+    for it’s strengths and weaknesses. This greatly limits how many I can evaluate
+    myself!
+
+    The most frustrating thing for me is at the level of individual prompting.
+
+    Sometimes I’ll tweak a prompt and capitalize some of the words in it, to emphasize
+    that I really want it to OUTPUT VALID MARKDOWN or similar. Did capitalizing those
+    words make a difference? I still don’t have a good methodology for figuring that
+    out.
+
+    We’re left with what’s effectively Vibes Based Development. It’s vibes all the
+    way down.
+
+    I’d love to see us move beyond vibes in 2024!
+
+    LLMs are really smart, and also really, really dumb'
+  - 'Except... you can run generated code to see if it’s correct. And with patterns
+    like ChatGPT Code Interpreter the LLM can execute the code itself, process the
+    error message, then rewrite it and keep trying until it works!
+
+    So hallucination is a much lesser problem for code generation than for anything
+    else. If only we had the equivalent of Code Interpreter for fact-checking natural
+    language!
+
+    How should we feel about this as software engineers?
+
+    On the one hand, this feels like a threat: who needs a programmer if ChatGPT can
+    write code for you?'
+- source_sentence: What are some ways mentioned to run local, private large language
+    models (LLMs) on personal devices?
+  sentences:
+  - 'A lot of people are excited about AI agents—an infuriatingly vague term that
+    seems to be converging on “AI systems that can go away and act on your behalf”.
+    We’ve been talking about them all year, but I’ve seen few if any examples of them
+    running in production, despite lots of exciting prototypes.
+
+    I think this is because of gullibility.
+
+    Can we solve this? Honestly, I’m beginning to suspect that you can’t fully solve
+    gullibility without achieving AGI. So it may be quite a while before those agent
+    dreams can really start to come true!
+
+    Code may be the best application
+
+    Over the course of the year, it’s become increasingly clear that writing code
+    is one of the things LLMs are most capable of.'
+  - 'I run a bunch of them on my laptop. I run Mistral 7B (a surprisingly great model)
+    on my iPhone. You can install several different apps to get your own, local, completely
+    private LLM. My own LLM project provides a CLI tool for running an array of different
+    models via plugins.
+
+    You can even run them entirely in your browser using WebAssembly and the latest
+    Chrome!
+
+    Hobbyists can build their own fine-tuned models
+
+    I said earlier that building an LLM was still out of reach of hobbyists. That
+    may be true for training from scratch, but fine-tuning one of those models is
+    another matter entirely.'
+  - 'Prompt injection is a natural consequence of this gulibility. I’ve seen precious
+    little progress on tackling that problem in 2024, and we’ve been talking about
+    it since September 2022.
+
+    I’m beginning to see the most popular idea of “agents” as dependent on AGI itself.
+    A model that’s robust against gulliblity is a very tall order indeed.
+
+    Evals really matter
+
+    Anthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):'
+- source_sentence: How has the value of prompt-driven app generation changed from
+    2023 to 2024?
+  sentences:
+  - 'On paper, a 64GB Mac should be a great machine for running models due to the
+    way the CPU and GPU can share the same memory. In practice, many models are released
+    as model weights and libraries that reward NVIDIA’s CUDA over other platforms.
+
+    The llama.cpp ecosystem helped a lot here, but the real breakthrough has been
+    Apple’s MLX library, “an array framework for Apple Silicon”. It’s fantastic.
+
+    Apple’s mlx-lm Python library supports running a wide range of MLX-compatible
+    models on my Mac, with excellent performance. mlx-community on Hugging Face offers
+    more than 1,000 models that have been converted to the necessary format.'
+  - 'The environmental impact got much, much worse
+
+    The much bigger problem here is the enormous competitive buildout of the infrastructure
+    that is imagined to be necessary for these models in the future.
+
+    Companies like Google, Meta, Microsoft and Amazon are all spending billions of
+    dollars rolling out new datacenters, with a very material impact on the electricity
+    grid and the environment. There’s even talk of spinning up new nuclear power stations,
+    but those can take decades.
+
+    Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued
+    crash in LLM prices might hint that it’s not. But would you want to be the big
+    tech executive that argued NOT to build out this infrastructure only to be proven
+    wrong in a few years’ time?'
+  - 'These abilities are just a few weeks old at this point, and I don’t think their
+    impact has been fully felt yet. If you haven’t tried them out yet you really should.
+
+    Both Gemini and OpenAI offer API access to these features as well. OpenAI started
+    with a WebSocket API that was quite challenging to use, but in December they announced
+    a new WebRTC API which is much easier to get started with. Building a web app
+    that a user can talk to via voice is easy now!
+
+    Prompt driven app generation is a commodity already
+
+    This was possible with GPT-4 in 2023, but the value it provides became evident
+    in 2024.'
+- source_sentence: What makes the prompt-driven custom interface feature powerful
+    and easy to build despite the challenges of browser sandboxing?
+  sentences:
+  - 'This prompt-driven custom interface feature is so powerful and easy to build
+    (once you’ve figured out the gnarly details of browser sandboxing) that I expect
+    it to show up as a feature in a wide range of products in 2025.
+
+    Universal access to the best models lasted for just a few short months
+
+    For a few short months this year all three of the best available models—GPT-4o,
+    Claude 3.5 Sonnet and Gemini 1.5 Pro—were freely available to most of the world.'
+  - 'The environmental impact got much, much worse
+
+    The much bigger problem here is the enormous competitive buildout of the infrastructure
+    that is imagined to be necessary for these models in the future.
+
+    Companies like Google, Meta, Microsoft and Amazon are all spending billions of
+    dollars rolling out new datacenters, with a very material impact on the electricity
+    grid and the environment. There’s even talk of spinning up new nuclear power stations,
+    but those can take decades.
+
+    Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued
+    crash in LLM prices might hint that it’s not. But would you want to be the big
+    tech executive that argued NOT to build out this infrastructure only to be proven
+    wrong in a few years’ time?'
+  - 'We don’t yet know how to build GPT-4
+
+    Frustratingly, despite the enormous leaps ahead we’ve had this year, we are yet
+    to see an alternative model that’s better than GPT-4.
+
+    OpenAI released GPT-4 in March, though it later turned out we had a sneak peak
+    of it in February when Microsoft used it as part of the new Bing.
+
+    This may well change in the next few weeks: Google’s Gemini Ultra has big claims,
+    but isn’t yet available for us to try out.
+
+    The team behind Mistral are working to beat GPT-4 as well, and their track record
+    is already extremely strong considering their first public model only came out
+    in September, and they’ve released two significant improvements since then.'
+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 Snowflake/snowflake-arctic-embed-l
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.875
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 1.0
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.875
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.20000000000000004
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.10000000000000002
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.875
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 1.0
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9538662191964322
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9375
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9375
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 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}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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): 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("manmah/legal-ft-2aefb51e-1a19-43c1-a5ff-7d28d65534da")
+# Run inference
+sentences = [
+    'What makes the prompt-driven custom interface feature powerful and easy to build despite the challenges of browser sandboxing?',
+    'This prompt-driven custom interface feature is so powerful and easy to build (once you’ve figured out the gnarly details of browser sandboxing) that I expect it to show up as a feature in a wide range of products in 2025.\nUniversal access to the best models lasted for just a few short months\nFor a few short months this year all three of the best available models—GPT-4o, Claude 3.5 Sonnet and Gemini 1.5 Pro—were freely available to most of the world.',
+    'We don’t yet know how to build GPT-4\nFrustratingly, despite the enormous leaps ahead we’ve had this year, we are yet to see an alternative model that’s better than GPT-4.\nOpenAI released GPT-4 in March, though it later turned out we had a sneak peak of it in February when Microsoft used it as part of the new Bing.\nThis may well change in the next few weeks: Google’s Gemini Ultra has big claims, but isn’t yet available for us to try out.\nThe team behind Mistral are working to beat GPT-4 as well, and their track record is already extremely strong considering their first public model only came out in September, and they’ve released two significant improvements since then.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# 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.*
+-->
+
+## 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   | 0.875      |
+| cosine_accuracy@3   | 1.0        |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.875      |
+| cosine_precision@3  | 0.3333     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.875      |
+| cosine_recall@3     | 1.0        |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| **cosine_ndcg@10**  | **0.9539** |
+| cosine_mrr@10       | 0.9375     |
+| cosine_map@100      | 0.9375     |
+
+<!--
+## 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: 156 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 156 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 12 tokens</li><li>mean: 20.82 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 135.28 tokens</li><li>max: 214 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                            | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What new feature does ChatGPT voice mode offer as of December?</code>           | <code>The most recent twist, again from December (December was a lot) is live video. ChatGPT voice mode now provides the option to share your camera feed with the model and talk about what you can see in real time. Google Gemini have a preview of the same feature, which they managed to ship the day before ChatGPT did.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  | <code>Which company released a similar live video feature just before ChatGPT?</code> | <code>The most recent twist, again from December (December was a lot) is live video. ChatGPT voice mode now provides the option to share your camera feed with the model and talk about what you can see in real time. Google Gemini have a preview of the same feature, which they managed to ship the day before ChatGPT did.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  | <code>When did OpenAI make GPT-4o free for all users?</code>                          | <code>OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was freely available from its launch in June. This was a momentus change, because for the previous year free users had mostly been restricted to GPT-3.5 level models, meaning new users got a very inaccurate mental model of what a capable LLM could actually do.<br>That era appears to have ended, likely permanently, with OpenAI’s launch of ChatGPT Pro. This $200/month subscription service is the only way to access their most capable model, o1 Pro.<br>Since the trick behind the o1 series (and the future models it will undoubtedly inspire) is to expend more compute time to get better results, I don’t think those days of free access to the best available models are likely to return.</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,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 10
+- `per_device_eval_batch_size`: 10
+- `num_train_epochs`: 10
+- `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`: 10
+- `per_device_eval_batch_size`: 10
+- `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`: 10
+- `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}
+- `tp_size`: 0
+- `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
+- `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
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch | Step | cosine_ndcg@10 |
+|:-----:|:----:|:--------------:|
+| 1.0   | 16   | 0.9484         |
+| 2.0   | 32   | 0.9539         |
+| 3.0   | 48   | 0.9692         |
+| 3.125 | 50   | 0.9846         |
+| 4.0   | 64   | 0.9692         |
+| 5.0   | 80   | 0.9692         |
+| 6.0   | 96   | 0.9539         |
+| 6.25  | 100  | 0.9385         |
+| 7.0   | 112  | 0.9539         |
+| 8.0   | 128  | 0.9539         |
+| 9.0   | 144  | 0.9539         |
+| 9.375 | 150  | 0.9539         |
+| 10.0  | 160  | 0.9539         |
+
+
+### Framework Versions
+- Python: 3.13.2
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.7.0
+- Accelerate: 1.6.0
+- Datasets: 3.5.1
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+#### 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 +1,24 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.7.0"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:905623312325c379d4fe8d7b8aef711b755649f16129291f5801d0f2f9565841
+size 1336413848

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 +1,63 @@
+{
+  "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
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+      "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]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}