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 +1,625 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:164
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: Snowflake/snowflake-arctic-embed-l
+widget:
+- source_sentence: What significant multi-modal models were released in 2024
+  sentences:
+  - '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.'
+  - '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'
+  - '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: What is the advantage of a 64GB Mac for running models
+  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:'
+  - '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.'
+  - '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 is the main innovation discussed in the context regarding
+    model scaling?
+  sentences:
+  - 'The biggest innovation here is that it opens up a new way to scale a model: instead
+    of improving model performance purely through additional compute at training time,
+    models can now take on harder problems by spending more compute on inference.
+
+    The sequel to o1, o3 (they skipped “o2” for European trademark reasons) was announced
+    on 20th December with an impressive result against the ARC-AGI benchmark, albeit
+    one that likely involved more than $1,000,000 of compute time expense!
+
+    o3 is expected to ship in January. I doubt many people have real-world problems
+    that would benefit from that level of compute expenditure—I certainly don’t!—but
+    it appears to be a genuine next step in LLM architecture for taking on much harder
+    problems.'
+  - Meanwhile, it’s increasingly common for end users to develop wildly inaccurate
+    mental models of how these things work and what they are capable of. I’ve seen
+    so many examples of people trying to win an argument with a screenshot from ChatGPT—an
+    inherently ludicrous proposition, given the inherent unreliability of these models
+    crossed with the fact that you can get them to say anything if you prompt them
+    right.
+  - 'I think this means that, as individual users, we don’t need to feel any guilt
+    at all for the energy consumed by the vast majority of our prompts. The impact
+    is likely neglible compared to driving a car down the street or maybe even watching
+    a video on YouTube.
+
+    Likewise, training. DeepSeek v3 training for less than $6m is a fantastic sign
+    that training costs can and should continue to drop.
+
+    For less efficient models I find it useful to compare their energy usage to commercial
+    flights. The largest Llama 3 model cost about the same as a single digit number
+    of fully loaded passenger flights from New York to London. That’s certainly not
+    nothing, but once trained that model can be used by millions of people at no extra
+    training cost.'
+- source_sentence: What new feature was introduced in ChatGPT's voice mode in December?
+  sentences:
+  - 'Nothing yet from Anthropic or Meta but I would be very surprised if they don’t
+    have their own inference-scaling models in the works. Meta published a relevant
+    paper Training Large Language Models to Reason in a Continuous Latent Space in
+    December.
+
+    Was the best currently available LLM trained in China for less than $6m?
+
+    Not quite, but almost! It does make for a great attention-grabbing headline.
+
+    The big news to end the year was the release of DeepSeek v3—dropped on Hugging
+    Face on Christmas Day without so much as a README file, then followed by documentation
+    and a paper the day after that.'
+  - 'Then in December, the Chatbot Arena team introduced a whole new leaderboard for
+    this feature, driven by users building the same interactive app twice with two
+    different models and voting on the answer. Hard to come up with a more convincing
+    argument that this feature is now a commodity that can be effectively implemented
+    against all of the leading models.
+
+    I’ve been tinkering with a version of this myself for my Datasette project, with
+    the goal of letting users use prompts to build and iterate on custom widgets and
+    data visualizations against their own data. I also figured out a similar pattern
+    for writing one-shot Python programs, enabled by uv.'
+  - 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.
+- source_sentence: Why is it important to learn how to work with unreliable technology
+    like LLMs?
+  sentences:
+  - 'Longer inputs dramatically increase the scope of problems that can be solved
+    with an LLM: you can now throw in an entire book and ask questions about its contents,
+    but more importantly you can feed in a lot of example code to help the model correctly
+    solve a coding problem. LLM use-cases that involve long inputs are far more interesting
+    to me than short prompts that rely purely on the information already baked into
+    the model weights. Many of my tools were built using this pattern.'
+  - 'There’s a flipside to this too: a lot of better informed people have sworn off
+    LLMs entirely because they can’t see how anyone could benefit from a tool with
+    so many flaws. The key skill in getting the most out of LLMs is learning to work
+    with tech that is both inherently unreliable and incredibly powerful at the same
+    time. This is a decidedly non-obvious skill to acquire!
+
+    There is so much space for helpful education content here, but we need to do do
+    a lot better than outsourcing it all to AI grifters with bombastic Twitter threads.
+
+    Knowledge is incredibly unevenly distributed
+
+    Most people have heard of ChatGPT by now. How many have heard of Claude?'
+  - '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.'
+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: 0.9583333333333334
+      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: 0.3194444444444444
+      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: 0.9583333333333334
+      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: 0.9455223360506796
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9270833333333334
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9270833333333334
+      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("KireetiKunam/legal-ft-2")
+# Run inference
+sentences = [
+    'Why is it important to learn how to work with unreliable technology like LLMs?',
+    'There’s a flipside to this too: a lot of better informed people have sworn off LLMs entirely because they can’t see how anyone could benefit from a tool with so many flaws. The key skill in getting the most out of LLMs is learning to work with tech that is both inherently unreliable and incredibly powerful at the same time. This is a decidedly non-obvious skill to acquire!\nThere is so much space for helpful education content here, but we need to do do a lot better than outsourcing it all to AI grifters with bombastic Twitter threads.\nKnowledge is incredibly unevenly distributed\nMost people have heard of ChatGPT by now. How many have heard of Claude?',
+    '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.\nVoice and live camera mode are science fiction come to life\nThe audio and live video modes that have started to emerge deserve a special mention.\nThe 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.',
+]
+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   | 0.9583     |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.875      |
+| cosine_precision@3  | 0.3194     |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.875      |
+| cosine_recall@3     | 0.9583     |
+| cosine_recall@5     | 1.0        |
+| cosine_recall@10    | 1.0        |
+| **cosine_ndcg@10**  | **0.9455** |
+| cosine_mrr@10       | 0.9271     |
+| cosine_map@100      | 0.9271     |
+
+<!--
+## 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: 164 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 164 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 15.43 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 130.65 tokens</li><li>max: 204 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                              | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  |:----------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What key themes were identified in the review of LLMs in 2024?</code>             | <code>Things we learned about LLMs in 2024<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>Things we learned about LLMs in 2024<br>31st December 2024<br>A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.<br>This is a sequel to my review of 2023.<br>In this article:</code>                                                                                                                                                                                                                                         |
+  | <code>What pivotal moments in the field of LLMs were highlighted in the article?</code> | <code>Things we learned about LLMs in 2024<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>Things we learned about LLMs in 2024<br>31st December 2024<br>A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.<br>This is a sequel to my review of 2023.<br>In this article:</code>                                                                                                                                                                                                                                         |
+  | <code>What advancements have been made in multimodal vision technology?</code>          | <code>The GPT-4 barrier was comprehensively broken<br>Some of those GPT-4 models run on my laptop<br>LLM prices crashed, thanks to competition and increased efficiency<br>Multimodal vision is common, audio and video are starting to emerge<br>Voice and live camera mode are science fiction come to life<br>Prompt driven app generation is a commodity already<br>Universal access to the best models lasted for just a few short months<br>“Agents” still haven’t really happened yet<br>Evals really matter<br>Apple Intelligence is bad, Apple’s MLX library is excellent<br>The rise of inference-scaling “reasoning” models<br>Was the best currently available LLM trained in China for less than $6m?<br>The environmental impact got better<br>The environmental impact got much, much worse</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}
+- `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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `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    | 17   | 0.9382         |
+| 2.0    | 34   | 0.9161         |
+| 2.9412 | 50   | 0.9270         |
+| 3.0    | 51   | 0.9270         |
+| 4.0    | 68   | 0.9283         |
+| 5.0    | 85   | 0.9437         |
+| 5.8824 | 100  | 0.9455         |
+| 6.0    | 102  | 0.9455         |
+| 7.0    | 119  | 0.9455         |
+| 8.0    | 136  | 0.9455         |
+| 8.8235 | 150  | 0.9455         |
+| 9.0    | 153  | 0.9455         |
+| 10.0   | 170  | 0.9455         |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.3.1
+- Tokenizers: 0.21.0
+
+## 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 +1,25 @@
+{
+  "_name_or_path": "Snowflake/snowflake-arctic-embed-l",
+  "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": "4.48.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "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:eac41d81d8e5754a813ef1c52453826a737362b1b61a376c4268ce8404b7ba6f
+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": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
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+    "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
+    },
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+      "lstrip": false,
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+      "rstrip": false,
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+      "lstrip": false,
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+      "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]"
+}