Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

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-m
+widget:
+- source_sentence: How did the construction of railways impact the environment during
+    the 1800s?
+  sentences:
+  - 'The boring yet crucial secret behind good system prompts is test-driven development.
+    You don’t write down a system prompt and find ways to test it. You write down
+    tests and find a system prompt that passes them.
+
+
+    It’s become abundantly clear over the course of 2024 that writing good automated
+    evals for LLM-powered systems is the skill that’s most needed to build useful
+    applications on top of these models. If you have a strong eval suite you can adopt
+    new models faster, iterate better and build more reliable and useful product features
+    than your competition.
+
+    Vercel’s Malte Ubl:'
+  - 'An interesting point of comparison here could be the way railways rolled out
+    around the world in the 1800s. Constructing these required enormous investments
+    and had a massive environmental impact, and many of the lines that were built
+    turned out to be unnecessary—sometimes multiple lines from different companies
+    serving the exact same routes!
+
+    The resulting bubbles contributed to several financial crashes, see Wikipedia
+    for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They
+    left us with a lot of useful infrastructure and a great deal of bankruptcies and
+    environmental damage.
+
+    The year of slop'
+  - '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.
+
+    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.
+
+    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.'
+- source_sentence: What significant multi-modal models were released by major vendors
+    in 2024?
+  sentences:
+  - 'DeepSeek v3 is a huge 685B parameter model—one of the largest openly licensed
+    models currently available, significantly bigger than the largest of Meta’s Llama
+    series, Llama 3.1 405B.
+
+    Benchmarks put it up there with Claude 3.5 Sonnet. Vibe benchmarks (aka the Chatbot
+    Arena) currently rank it 7th, just behind the Gemini 2.0 and OpenAI 4o/o1 models.
+    This is by far the highest ranking openly licensed model.
+
+    The really impressive thing about DeepSeek v3 is the training cost. The model
+    was trained on 2,788,000 H800 GPU hours at an estimated cost of $5,576,000. Llama
+    3.1 405B trained 30,840,000 GPU hours—11x that used by DeepSeek v3, for a model
+    that benchmarks slightly worse.'
+  - 'In 2024, almost every significant model vendor released multi-modal models. We
+    saw the Claude 3 series from Anthropic in March, Gemini 1.5 Pro in April (images,
+    audio and video), then September brought Qwen2-VL and Mistral’s Pixtral 12B and
+    Meta’s Llama 3.2 11B and 90B vision models. We got audio input and output from
+    OpenAI in October, then November saw SmolVLM from Hugging Face and December saw
+    image and video models from Amazon Nova.
+
+    In October I upgraded my LLM CLI tool to support multi-modal models via attachments.
+    It now has plugins for a whole collection of different vision models.'
+  - '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?'
+- source_sentence: Why does the author believe that gullibility may hinder the development
+    of AI agents?
+  sentences:
+  - 'An interesting point of comparison here could be the way railways rolled out
+    around the world in the 1800s. Constructing these required enormous investments
+    and had a massive environmental impact, and many of the lines that were built
+    turned out to be unnecessary—sometimes multiple lines from different companies
+    serving the exact same routes!
+
+    The resulting bubbles contributed to several financial crashes, see Wikipedia
+    for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They
+    left us with a lot of useful infrastructure and a great deal of bankruptcies and
+    environmental damage.
+
+    The year of slop'
+  - '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.'
+  - 'Terminology aside, I remain skeptical as to their utility based, once again,
+    on the challenge of gullibility. LLMs believe anything you tell them. Any systems
+    that attempts to make meaningful decisions on your behalf will run into the same
+    roadblock: how good is a travel agent, or a digital assistant, or even a research
+    tool if it can’t distinguish truth from fiction?
+
+    Just the other day Google Search was caught serving up an entirely fake description
+    of the non-existant movie “Encanto 2”. It turned out to be summarizing an imagined
+    movie listing from a fan fiction wiki.'
+- source_sentence: How did the approach to handling the prompt change over time according
+    to the context?
+  sentences:
+  - 'We already knew LLMs were spookily good at writing code. If you prompt them right,
+    it turns out they can build you a full interactive application using HTML, CSS
+    and JavaScript (and tools like React if you wire up some extra supporting build
+    mechanisms)—often in a single prompt.
+
+    Anthropic kicked this idea into high gear when they released Claude Artifacts,
+    a groundbreaking new feature that was initially slightly lost in the noise due
+    to being described half way through their announcement of the incredible Claude
+    3.5 Sonnet.
+
+    With Artifacts, Claude can write you an on-demand interactive application and
+    then let you use it directly inside the Claude interface.
+
+    Here’s my Extract URLs app, entirely generated by Claude:'
+  - 'The two main categories I see are people who think AI agents are obviously things
+    that go and act on your behalf—the travel agent model—and people who think in
+    terms of LLMs that have been given access to tools which they can run in a loop
+    as part of solving a problem. The term “autonomy” is often thrown into the mix
+    too, again without including a clear definition.
+
+    (I also collected 211 definitions on Twitter a few months ago—here they are in
+    Datasette Lite—and had gemini-exp-1206 attempt to summarize them.)
+
+    Whatever the term may mean, agents still have that feeling of perpetually “coming
+    soon”.'
+  - 'When @v0 first came out we were paranoid about protecting the prompt with all
+    kinds of pre and post processing complexity.
+
+    We completely pivoted to let it rip. A prompt without the evals, models, and especially
+    UX is like getting a broken ASML machine without a manual'
+- source_sentence: How many lines of Python code are typically sufficient to train
+    a basic version of a powerful system?
+  sentences:
+  - 'Intuitively, one would expect that systems this powerful would take millions
+    of lines of complex code. Instead, it turns out a few hundred lines of Python
+    is genuinely enough to train a basic version!
+
+    What matters most is the training  data. You need a lot of data to make these
+    things work, and the quantity and quality of the training data appears to be the
+    most important factor in how good the resulting model is.
+
+    If you can gather the right data, and afford to pay for the GPUs to train it,
+    you can build an LLM.'
+  - 'So far, I think they’re a net positive. I’ve used them on a personal level to
+    improve my productivity (and entertain myself) in all sorts of different ways.
+    I think people who learn how to use them effectively can gain a significant boost
+    to their quality of life.
+
+    A lot of people are yet to be sold on their value! Some think their negatives
+    outweigh their positives, some think they are all hot air, and some even think
+    they represent an existential threat to humanity.
+
+    They’re actually quite easy to build
+
+    The most surprising thing we’ve learned about LLMs this year is that they’re actually
+    quite easy to build.'
+  - 'I’m still trying to figure out the best patterns for doing this for my own work.
+    Everyone knows that evals are important, but there remains a lack of great guidance
+    for how to best implement them—I’m tracking this under my evals tag. My SVG pelican
+    riding a bicycle benchmark is a pale imitation of what a real eval suite should
+    look like.
+
+    Apple Intelligence is bad, Apple’s MLX library is excellent
+
+    As a Mac user I’ve been feeling a lot better about my choice of platform this
+    year.
+
+    Last year it felt like my lack of a Linux/Windows  machine with an NVIDIA GPU
+    was a huge disadvantage in terms of trying out new models.'
+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-m
+  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-m
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). 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:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision fc74610d18462d218e312aa986ec5c8a75a98152 -->
+- **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}) 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): 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("bsmith3715/legal-ft-cert-challengev2")
+# Run inference
+sentences = [
+    'How many lines of Python code are typically sufficient to train a basic version of a powerful system?',
+    'Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!\nWhat matters most is the training  data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is.\nIf you can gather the right data, and afford to pay for the GPUs to train it, you can build an LLM.',
+    'I’m still trying to figure out the best patterns for doing this for my own work. Everyone knows that evals are important, but there remains a lack of great guidance for how to best implement them—I’m tracking this under my evals tag. My SVG pelican riding a bicycle benchmark is a pale imitation of what a real eval suite should look like.\nApple Intelligence is bad, Apple’s MLX library is excellent\nAs a Mac user I’ve been feeling a lot better about my choice of platform this year.\nLast year it felt like my lack of a Linux/Windows  machine with an NVIDIA GPU was a huge disadvantage in terms of trying out new models.',
+]
+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.*
+-->
+
+## 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: 20.49 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 135.35 tokens</li><li>max: 214 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                         | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  |:-----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What significant advancements in AI were made in 2023, particularly regarding Large Language Models (LLMs)?</code>           | <code>Stuff we figured out about AI in 2023<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Stuff we figured out about AI in 2023<br>31st December 2023<br>2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.<br>Here’s my attempt to round up the highlights in one place!</code> |
+  | <code>How does the development of LLMs in 2023 relate to the historical context of Artificial Intelligence since the 1950s?</code> | <code>Stuff we figured out about AI in 2023<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Stuff we figured out about AI in 2023<br>31st December 2023<br>2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.<br>Here’s my attempt to round up the highlights in one place!</code> |
+  | <code>What are some potential applications of Large Language Models (LLMs) mentioned in the context?</code>                        | <code>Large Language Models<br>They’re actually quite easy to build<br>You can run LLMs on your own devices<br>Hobbyists can build their own fine-tuned models<br>We don’t yet know how to build GPT-4<br>Vibes Based Development<br>LLMs are really smart, and also really, really dumb<br>Gullibility is the biggest unsolved problem<br>Code may be the best application<br>The ethics of this space remain diabolically complex<br>My blog in 2023</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   | 0.8895         |
+| 2.0   | 32   | 0.9109         |
+| 3.0   | 48   | 0.9192         |
+| 3.125 | 50   | 0.9192         |
+| 4.0   | 64   | 0.9330         |
+| 5.0   | 80   | 0.9385         |
+| 6.0   | 96   | 0.9385         |
+| 6.25  | 100  | 0.9539         |
+| 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+cpu
+- Accelerate: 1.6.0
+- Datasets: 3.6.0
+- 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 +1,25 @@
+{
+  "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": "4.51.3",
+  "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+cpu"
+  },
+  "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 sha256:1ec4554b368ff016719382a2a651de85cdb4ef496ab23c57545fabe9fc004ec0
+size 435588776

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
+    },
+    "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]",
+  "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]"
+}