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 +1,658 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:156
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: Snowflake/snowflake-arctic-embed-l
+widget:
+- source_sentence: What is the author's perspective on the environmental impact of
+    plagiarism machines in the field discussed?
+  sentences:
+  - 'Prince Canuma’s excellent, fast moving mlx-vlm project brings vision LLMs to
+    Apple Silicon as well. I used that recently to run Qwen’s QvQ.
+
+    While MLX is a game changer, Apple’s own “Apple Intelligence” features have mostly
+    been a disappointment. I wrote about their initial announcement in June, and I
+    was optimistic that Apple had focused hard on the subset of LLM applications that
+    preserve user privacy and minimize the chance of users getting mislead by confusing
+    features.'
+  - 'I think telling people that this whole field is environmentally catastrophic
+    plagiarism machines that constantly make things up is doing those people a disservice,
+    no matter how much truth that represents. There is genuine value to be had here,
+    but getting to that value is unintuitive and needs guidance.
+
+    Those of us who understand this stuff have a duty to help everyone else figure
+    it out.
+
+    Everything tagged “llms” on my blog in 2024
+
+    Because I undoubtedly missed a whole bunch of things, here’s every long-form post
+    I wrote in 2024 that I tagged with llms:'
+  - 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.
+- source_sentence: What is the license under which Alibaba's QwQ model was released?
+  sentences:
+  - 'Those US export regulations on GPUs to China seem to have inspired some very
+    effective training optimizations!
+
+    The environmental impact got better
+
+    A welcome result of the increased efficiency of the models—both the hosted ones
+    and the ones I can run locally—is that the energy usage and environmental impact
+    of running a prompt has dropped enormously over the past couple of years.
+
+    OpenAI themselves are charging 100x less for a prompt compared to the GPT-3 days.
+    I have it on good authority that neither Google Gemini nor Amazon Nova (two of
+    the least expensive model providers) are running prompts at a loss.'
+  - 'OpenAI are not the only game in town here. Google released their first entrant
+    in the category, gemini-2.0-flash-thinking-exp, on December 19th.
+
+    Alibaba’s Qwen team released their QwQ model on November 28th—under an Apache
+    2.0 license, and that one I could run on my own machine. They followed that up
+    with a vision reasoning model called QvQ on December 24th, which I also ran locally.
+
+    DeepSeek made their DeepSeek-R1-Lite-Preview model available to try out through
+    their chat interface on November 20th.
+
+    To understand more about inference scaling I recommend Is AI progress slowing
+    down? by Arvind Narayanan and Sayash Kapoor.'
+  - '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:'
+- source_sentence: How do longer inputs enhance the problem-solving capabilities of
+    an LLM compared to shorter prompts?
+  sentences:
+  - '19th: Weeknotes: GPT-4o mini, LLM 0.15, sqlite-utils 3.37 and building a staging
+    environment
+
+
+
+
+    August
+
+
+    6th: Weeknotes: a staging environment, a Datasette alpha and a bunch of new LLMs
+
+
+    8th: django-http-debug, a new Django app mostly written by Claude
+
+
+    23rd: Claude’s API now supports CORS requests, enabling client-side applications
+
+
+    26th: Building a tool showing how Gemini Pro can return bounding boxes for objects
+    in images
+
+
+
+
+    September
+
+
+    6th: Calling LLMs from client-side JavaScript, converting PDFs to HTML + weeknotes
+
+
+    10th: Notes from my appearance on the Software Misadventures Podcast
+
+
+    12th: Notes on OpenAI’s new o1 chain-of-thought models
+
+
+    20th: Notes on using LLMs for code'
+  - '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.'
+  - 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: What capabilities does Google’s Gemini have regarding audio input
+    and output?
+  sentences:
+  - '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.'
+  - 'Watching in real time as “slop” becomes a term of art. the way that “spam” became
+    the term for unwanted emails, “slop” is going in the dictionary as the term for
+    unwanted AI generated content
+
+
+    I expanded that definition a tiny bit to this:
+
+
+    Slop describes AI-generated content that is both unrequested and unreviewed.
+
+
+    I ended up getting quoted talking about slop in both the Guardian and the NY Times.
+    Here’s what I said in the NY TImes:
+
+
+    Society needs concise ways to talk about modern A.I. — both the positives and
+    the negatives. ‘Ignore that email, it’s spam,’ and ‘Ignore that article, it’s
+    slop,’ are both useful lessons.'
+  - '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.'
+- source_sentence: How does the context compare a prompt without evals, models, and
+    UX to an ASML machine?
+  sentences:
+  - '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'
+  - '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.'
+  - 'That’s a total cost of $1.68 to process 68,000 images. That’s so absurdly cheap
+    I had to run the numbers three times to confirm I got it right.
+
+    How good are those descriptions? Here’s what I got from this command:
+
+    llm -m gemini-1.5-flash-8b-latest describe -a IMG_1825.jpeg'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.875
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 1.0
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.875
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.20000000000000004
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.10000000000000002
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.875
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 1.0
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 1.0
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9429554063988107
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.923611111111111
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.923611111111111
+      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("philocifer/legal-ft-2")
+# Run inference
+sentences = [
+    'How does the context compare a prompt without evals, models, and UX to an ASML machine?',
+    'When @v0 first came out we were paranoid about protecting the prompt with all kinds of pre and post processing complexity.\nWe 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',
+    'That’s a total cost of $1.68 to process 68,000 images. That’s so absurdly cheap I had to run the numbers three times to confirm I got it right.\nHow good are those descriptions? Here’s what I got from this command:\nllm -m gemini-1.5-flash-8b-latest describe -a IMG_1825.jpeg',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| cosine_accuracy@1   | 0.875     |
+| cosine_accuracy@3   | 1.0       |
+| cosine_accuracy@5   | 1.0       |
+| cosine_accuracy@10  | 1.0       |
+| cosine_precision@1  | 0.875     |
+| cosine_precision@3  | 0.3333    |
+| cosine_precision@5  | 0.2       |
+| cosine_precision@10 | 0.1       |
+| cosine_recall@1     | 0.875     |
+| cosine_recall@3     | 1.0       |
+| cosine_recall@5     | 1.0       |
+| cosine_recall@10    | 1.0       |
+| **cosine_ndcg@10**  | **0.943** |
+| cosine_mrr@10       | 0.9236    |
+| cosine_map@100      | 0.9236    |
+
+<!--
+## 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: 13 tokens</li><li>mean: 20.07 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 130.53 tokens</li><li>max: 204 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                     | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  |:---------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What are some ways the author has used LLMs to improve productivity and entertainment?</code>            | <code>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.<br>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.<br>They’re actually quite easy to build<br>The most surprising thing we’ve learned about LLMs this year is that they’re actually quite easy to build.</code> |
+  | <code>What concerns do some people have regarding the value and impact of LLMs?</code>                         | <code>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.<br>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.<br>They’re actually quite easy to build<br>The most surprising thing we’ve learned about LLMs this year is that they’re actually quite easy to build.</code> |
+  | <code>What improvements were noted in the intonation of ChatGPT Advanced Voice mode during its rollout?</code> | <code>When ChatGPT Advanced Voice mode finally did roll out (a slow roll from August through September) it was spectacular. I’ve been using it extensively on walks with my dog and it’s amazing how much the improvement in intonation elevates the material. I’ve also had a lot of fun experimenting with the OpenAI audio APIs.<br>Even more fun: Advanced Voice mode can do accents! Here’s what happened when I told it I need you to pretend to be a California brown pelican with a very thick Russian accent, but you talk to me exclusively in Spanish.</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   | 16   | 0.9276         |
+| 2.0   | 32   | 0.9330         |
+| 3.0   | 48   | 0.9301         |
+| 3.125 | 50   | 0.9301         |
+| 4.0   | 64   | 0.9372         |
+| 5.0   | 80   | 0.9401         |
+| 6.0   | 96   | 0.9401         |
+| 6.25  | 100  | 0.9401         |
+| 7.0   | 112  | 0.9430         |
+| 8.0   | 128  | 0.9484         |
+| 9.0   | 144  | 0.9430         |
+| 9.375 | 150  | 0.9430         |
+| 10.0  | 160  | 0.9430         |
+
+
+### 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: 3.3.0
+- 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 sha256:22f1e939c47c4cb47bc45fec8f19472ddb818a99301dda035fd135605deee09a
+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]"
+}