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

@@ -0,0 +1,694 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:160
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: Snowflake/snowflake-arctic-embed-l
+widget:
+- source_sentence: Why might ChatGPT's answers change as the date approaches the holidays?
+  sentences:
+  - 'There’s now a fascinating ecosystem of people training their own models on top
+    of these foundations, publishing those models, building fine-tuning datasets and
+    sharing those too.
+
+    The Hugging Face Open LLM Leaderboard is one place that tracks these. I can’t
+    even attempt to count them, and any count would be out-of-date within a few hours.
+
+    The best overall openly licensed LLM at any time is rarely a foundation model:
+    instead, it’s whichever fine-tuned community model has most recently discovered
+    the best combination of fine-tuning data.
+
+    This is a huge advantage for open over closed models: the closed, hosted models
+    don’t have thousands of researchers and hobbyists around the world collaborating
+    and competing to improve them.'
+  - 'On the one hand, we keep on finding new things that LLMs can do that we didn’t
+    expect—and that the people who trained the models didn’t expect either. That’s
+    usually really fun!
+
+    But on the other hand, the things you sometimes have to do to get the models to
+    behave are often incredibly dumb.
+
+    Does ChatGPT get lazy in December, because its hidden system prompt includes the
+    current date and its training data shows that people provide less useful answers
+    coming up to the holidays?
+
+    The honest answer is “maybe”! No-one is entirely sure, but if you give it a different
+    date its answers may skew slightly longer.'
+  - 'Getting back to models that beat GPT-4: Anthropic’s Claude 3 series launched
+    in March, and Claude 3 Opus quickly became my new favourite daily-driver. They
+    upped the ante even more in June with the launch of Claude 3.5 Sonnet—a model
+    that is still my favourite six months later (though it got a significant upgrade
+    on October 22, confusingly keeping the same 3.5 version number. Anthropic fans
+    have since taken to calling it Claude 3.6).'
+- source_sentence: What significance did the year 2024 have in relation to the word
+    "slop"?
+  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.'
+  - 'The year of slop
+
+    2024 was the year that the word "slop" became a term of art. I wrote about this
+    in May, expanding on this tweet by @deepfates:'
+  - 'On the other hand, as software engineers we are better placed to take advantage
+    of this than anyone else. We’ve all been given weird coding interns—we can use
+    our deep knowledge to prompt them to solve coding problems more effectively than
+    anyone else can.
+
+    The ethics of this space remain diabolically complex
+
+    In September last year Andy Baio and I produced the first major story on the unlicensed
+    training data behind Stable Diffusion.
+
+    Since then, almost every major LLM (and most of the image generation models) have
+    also been trained on unlicensed data.'
+- source_sentence: Why does the author find large language models (LLMs) infuriating
+    as a computer scientist and software engineer?
+  sentences:
+  - 'Stuff we figured out about AI in 2023
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+    Simon Willison’s Weblog
+
+    Subscribe
+
+
+
+
+
+
+
+    Stuff we figured out about AI in 2023
+
+    31st December 2023
+
+    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.
+
+    Here’s my attempt to round up the highlights in one place!'
+  - 'The May 13th announcement of GPT-4o included a demo of a brand new voice mode,
+    where the true multi-modal GPT-4o (the o is for “omni”) model could accept audio
+    input and output incredibly realistic sounding speech without needing separate
+    TTS or STT models.
+
+    The demo also sounded conspicuously similar to Scarlett Johansson... and after
+    she complained the voice from the demo, Skye, never made it to a production product.
+
+    The delay in releasing the new voice mode after the initial demo caused quite
+    a lot of confusion. I wrote about that in ChatGPT in “4o” mode is not running
+    the new features yet.'
+  - 'Still, I’m surprised that no-one has beaten the now almost year old GPT-4 by
+    now. OpenAI clearly have some substantial tricks that they haven’t shared yet.
+
+    Vibes Based Development
+
+    As a computer scientist and software engineer, LLMs are infuriating.
+
+    Even the openly licensed ones are still the world’s most convoluted black boxes.
+    We continue to have very little idea what they can do, how exactly they work and
+    how best to control them.
+
+    I’m used to programming where the computer does exactly what I tell it to do.
+    Prompting an LLM is decidedly not that!
+
+    The worst part is the challenge of evaluating them.
+
+    There are plenty of benchmarks, but no benchmark is going to tell you if an LLM
+    actually “feels” right when you try it for a given task.'
+- source_sentence: How did Google’s NotebookLM enhance audio output in its September
+    release?
+  sentences:
+  - 'Your browser does not support the audio element.
+
+
+    OpenAI aren’t the only group with a multi-modal audio model. Google’s Gemini also
+    accepts audio input, and the Google Gemini apps can speak in a similar way to
+    ChatGPT now. Amazon also pre-announced voice mode for Amazon Nova, but that’s
+    meant to roll out in Q1 of 2025.
+
+    Google’s NotebookLM, released in September, took audio output to a new level by
+    producing spookily realistic conversations between two “podcast hosts” about anything
+    you fed into their tool. They later added custom instructions, so naturally I
+    turned them into pelicans:
+
+
+
+    Your browser does not support the audio element.'
+  - 'If you think about what they do, this isn’t such a big surprise. The grammar
+    rules of programming languages like Python and JavaScript are massively less complicated
+    than the grammar of Chinese, Spanish or English.
+
+    It’s still astonishing to me how effective they are though.
+
+    One of the great weaknesses of LLMs is their tendency to hallucinate—to imagine
+    things that don’t correspond to reality. You would expect this to be a particularly
+    bad problem for code—if an LLM hallucinates a method that doesn’t exist, the code
+    should be useless.'
+  - 'I think people who complain that LLM improvement has slowed are often missing
+    the enormous advances in these multi-modal models. Being able to run prompts against
+    images (and audio and video) is a fascinating new way to apply these models.
+
+    Voice and live camera mode are science fiction come to life
+
+    The audio and live video modes that have started to emerge deserve a special mention.
+
+    The ability to talk to ChatGPT first arrived in September 2023, but it was mostly
+    an illusion: OpenAI used their excellent Whisper speech-to-text model and a new
+    text-to-speech model (creatively named tts-1) to enable conversations with the
+    ChatGPT mobile apps, but the actual model just saw text.'
+- source_sentence: What type of dish is shown in the photo and what does it contain?
+  sentences:
+  - 'Against this photo of butterflies at the California Academy of Sciences:
+
+
+
+    A shallow dish, likely a hummingbird or butterfly feeder, is red.  Pieces of orange
+    slices of fruit are visible inside the dish.
+
+    Two butterflies are positioned in the feeder, one is a dark brown/black butterfly
+    with white/cream-colored markings.  The other is a large, brown butterfly with
+    patterns of lighter brown, beige, and black markings, including prominent eye
+    spots. The larger brown butterfly appears to be feeding on the fruit.'
+  - '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?'
+  - 'On the other hand, as software engineers we are better placed to take advantage
+    of this than anyone else. We’ve all been given weird coding interns—we can use
+    our deep knowledge to prompt them to solve coding problems more effectively than
+    anyone else can.
+
+    The ethics of this space remain diabolically complex
+
+    In September last year Andy Baio and I produced the first major story on the unlicensed
+    training data behind Stable Diffusion.
+
+    Since then, almost every major LLM (and most of the image generation models) have
+    also been trained on unlicensed data.'
+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: 0.95
+      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: 0.95
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.33333333333333326
+      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: 0.95
+      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: 0.9815464876785729
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.975
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.975
+      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, 'architecture': '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("rajatgupta99924/AIE6-S09-eca4bfc6-eb64-44a4-a71d-e09bf2b78f50")
+# Run inference
+queries = [
+    "What type of dish is shown in the photo and what does it contain?",
+]
+documents = [
+    'Against this photo of butterflies at the California Academy of Sciences:\n\n\nA shallow dish, likely a hummingbird or butterfly feeder, is red.  Pieces of orange slices of fruit are visible inside the dish.\nTwo butterflies are positioned in the feeder, one is a dark brown/black butterfly with white/cream-colored markings.  The other is a large, brown butterfly with patterns of lighter brown, beige, and black markings, including prominent eye spots. The larger brown butterfly appears to be feeding on the fruit.',
+    '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!\nSo 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!\nHow should we feel about this as software engineers?\nOn the one hand, this feels like a threat: who needs a programmer if ChatGPT can write code for you?',
+    'On the other hand, as software engineers we are better placed to take advantage of this than anyone else. We’ve all been given weird coding interns—we can use our deep knowledge to prompt them to solve coding problems more effectively than anyone else can.\nThe ethics of this space remain diabolically complex\nIn September last year Andy Baio and I produced the first major story on the unlicensed training data behind Stable Diffusion.\nSince then, almost every major LLM (and most of the image generation models) have also been trained on unlicensed data.',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 1024] [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[ 0.4179, -0.0420,  0.0399]])
+```
+
+<!--
+### 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   | 0.95       |
+| cosine_accuracy@3   | 1.0        |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.95       |
+| cosine_precision@3  | 0.3333     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.95       |
+| cosine_recall@3     | 1.0        |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| **cosine_ndcg@10**  | **0.9815** |
+| cosine_mrr@10       | 0.975      |
+| cosine_map@100      | 0.975      |
+
+<!--
+## 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: 160 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 160 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 12 tokens</li><li>mean: 20.58 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 133.43 tokens</li><li>max: 214 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                               | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What topics are covered in the articles related to large language models (LLMs) and AI development in the provided context?</code> | <code>Embeddings: What they are and why they matter<br>61.7k<br>79.3k<br><br><br>Catching up on the weird world of LLMs<br>61.6k<br>85.9k<br><br><br>llamafile is the new best way to run an LLM on your own computer<br>52k<br>66k<br><br><br>Prompt injection explained, with video, slides, and a transcript<br>51k<br>61.9k<br><br><br>AI-enhanced development makes me more ambitious with my projects<br>49.6k<br>60.1k<br><br><br>Understanding GPT tokenizers<br>49.5k<br>61.1k<br><br><br>Exploring GPTs: ChatGPT in a trench coat?<br>46.4k<br>58.5k<br><br><br>Could you train a ChatGPT-beating model for $85,000 and run it in a browser?<br>40.5k<br>49.2k<br><br><br>How to implement Q&A against your documentation with GPT3, embeddings and Datasette<br>37.3k<br>44.9k<br><br><br>Lawyer cites fake cases invented by ChatGPT, judge is not amused<br>37.1k<br>47.4k</code> |
+  | <code>Which article discusses the potential cost and feasibility of training a ChatGPT-beating model to run in a browser?</code>         | <code>Embeddings: What they are and why they matter<br>61.7k<br>79.3k<br><br><br>Catching up on the weird world of LLMs<br>61.6k<br>85.9k<br><br><br>llamafile is the new best way to run an LLM on your own computer<br>52k<br>66k<br><br><br>Prompt injection explained, with video, slides, and a transcript<br>51k<br>61.9k<br><br><br>AI-enhanced development makes me more ambitious with my projects<br>49.6k<br>60.1k<br><br><br>Understanding GPT tokenizers<br>49.5k<br>61.1k<br><br><br>Exploring GPTs: ChatGPT in a trench coat?<br>46.4k<br>58.5k<br><br><br>Could you train a ChatGPT-beating model for $85,000 and run it in a browser?<br>40.5k<br>49.2k<br><br><br>How to implement Q&A against your documentation with GPT3, embeddings and Datasette<br>37.3k<br>44.9k<br><br><br>Lawyer cites fake cases invented by ChatGPT, judge is not amused<br>37.1k<br>47.4k</code> |
+  | <code>What are some of the capabilities of Large Language Models mentioned in the context?</code>                                        | <code>Here’s the sequel to this post: Things we learned about LLMs in 2024.<br>Large Language Models<br>In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.<br>LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.<br>They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.</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}
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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 |
+|:-----:|:----:|:--------------:|
+| 1.0   | 16   | 0.9815         |
+| 2.0   | 32   | 0.9815         |
+| 1.0   | 16   | 0.9815         |
+| 2.0   | 32   | 0.9815         |
+| 3.0   | 48   | 0.9815         |
+| 3.125 | 50   | 0.9815         |
+| 4.0   | 64   | 0.9815         |
+| 5.0   | 80   | 0.9815         |
+| 6.0   | 96   | 0.9815         |
+| 6.25  | 100  | 0.9815         |
+| 7.0   | 112  | 0.9815         |
+| 8.0   | 128  | 0.9815         |
+| 9.0   | 144  | 0.9815         |
+| 9.375 | 150  | 0.9815         |
+| 10.0  | 160  | 0.9815         |
+
+
+### Framework Versions
+- Python: 3.13.7
+- Sentence Transformers: 5.1.0
+- Transformers: 4.56.1
+- PyTorch: 2.8.0+cpu
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.22.0
+
+## 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,
+  "dtype": "float32",
+  "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",
+  "transformers_version": "4.56.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.56.1",
+    "pytorch": "2.8.0+cpu"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: ",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8711c49a1bee20d28b62e5a5391f7ff98932c2ec2f6df0a374a691b843ac524b
+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
+    },
+    "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]"
+}