README.md

---
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: How did Steve Krouse from Val Town demonstrate the capabilities
    of a 2,000 token/second LLM?
  sentences:
  - 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.
  - 'I’ve found myself using this a lot. I noticed how much I was relying on it in
    October and wrote Everything I built with Claude Artifacts this week, describing
    14 little tools I had put together in a seven day period.

    Since then, a whole bunch of other teams have built similar systems. GitHub announced
    their version of this—GitHub Spark—in October. Mistral Chat added it as a feature
    called Canvas in November.

    Steve Krouse from Val Town built a version of it against Cerebras, showcasing
    how a 2,000 token/second LLM can iterate on an application with changes visible
    in less than a second.'
  - 'I run a bunch of them on my laptop. I run Mistral 7B (a surprisingly great model)
    on my iPhone. You can install several different apps to get your own, local, completely
    private LLM. My own LLM project provides a CLI tool for running an array of different
    models via plugins.

    You can even run them entirely in your browser using WebAssembly and the latest
    Chrome!

    Hobbyists can build their own fine-tuned models

    I said earlier that building an LLM was still out of reach of hobbyists. That
    may be true for training from scratch, but fine-tuning one of those models is
    another matter entirely.'
- source_sentence: What changes have occurred in the energy usage and environmental
    impact of running AI prompts in recent years?
  sentences:
  - 'Law is not ethics. Is it OK to train models on people’s content without their
    permission, when those models will then be used in ways that compete with those
    people?

    As the quality of results produced by AI models has increased over the year, these
    questions have become even more pressing.

    The impact on human society in terms of these models is already huge, if difficult
    to objectively measure.

    People have certainly lost work to them—anecdotally, I’ve seen this for copywriters,
    artists and translators.

    There are a great deal of untold stories here. I’m hoping 2024 sees significant
    amounts of dedicated journalism on this topic.

    My blog in 2023

    Here’s a tag cloud for content I posted to my blog in 2023 (generated using Django
    SQL Dashboard):'
  - '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.'
  - 'An interesting point of comparison here could be the way railways rolled out
    around the world in the 1800s. Constructing these required enormous investments
    and had a massive environmental impact, and many of the lines that were built
    turned out to be unnecessary—sometimes multiple lines from different companies
    serving the exact same routes!

    The resulting bubbles contributed to several financial crashes, see Wikipedia
    for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They
    left us with a lot of useful infrastructure and a great deal of bankruptcies and
    environmental damage.

    The year of slop'
- source_sentence: What is the main topic discussed in the article titled "Industry’s
    Tardy Response to the AI Prompt Injection Vulnerability" on RedMonk Conversations?
  sentences:
  - '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).'
  - "Industry’s Tardy Response to the AI Prompt Injection Vulnerability on RedMonk\
    \ Conversations\n\n\nPosted 31st December 2023 at 11:59 pm · Follow me on Mastodon,\
    \ Bluesky, Twitter or subscribe to my newsletter\n\n\nMore recent articles\n\n\
    Live blog: Claude 4 launch at Code with Claude - 22nd May 2025\nI really don't\
    \ like ChatGPT's new memory dossier - 21st May 2025\nBuilding software on top\
    \ of Large Language Models - 15th May 2025\n\n\n \n\n\nThis is Stuff we figured\
    \ out about AI in 2023 by Simon Willison, posted on 31st December 2023.\n\nPart\
    \ of series LLMs annual review\n\nStuff we figured out about AI in 2023 - Dec.\
    \ 31, 2023, 11:59 p.m. \nThings we learned about LLMs in 2024 - Dec. 31, 2024,\
    \ 6:07 p.m. \n\n\n\n            blogging\n            105"
  - '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.

    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.'
- source_sentence: How can LLMs like Claude create full interactive applications using
    web technologies in a single prompt?
  sentences:
  - 'This prompt-driven custom interface feature is so powerful and easy to build
    (once you’ve figured out the gnarly details of browser sandboxing) that I expect
    it to show up as a feature in a wide range of products in 2025.

    Universal access to the best models lasted for just a few short months

    For a few short months this year all three of the best available models—GPT-4o,
    Claude 3.5 Sonnet and Gemini 1.5 Pro—were freely available to most of the world.'
  - 'I find I have to work with an LLM for a few weeks in order to get a good intuition
    for it’s strengths and weaknesses. This greatly limits how many I can evaluate
    myself!

    The most frustrating thing for me is at the level of individual prompting.

    Sometimes I’ll tweak a prompt and capitalize some of the words in it, to emphasize
    that I really want it to OUTPUT VALID MARKDOWN or similar. Did capitalizing those
    words make a difference? I still don’t have a good methodology for figuring that
    out.

    We’re left with what’s effectively Vibes Based Development. It’s vibes all the
    way down.

    I’d love to see us move beyond vibes in 2024!

    LLMs are really smart, and also really, really dumb'
  - 'We already knew LLMs were spookily good at writing code. If you prompt them right,
    it turns out they can build you a full interactive application using HTML, CSS
    and JavaScript (and tools like React if you wire up some extra supporting build
    mechanisms)—often in a single prompt.

    Anthropic kicked this idea into high gear when they released Claude Artifacts,
    a groundbreaking new feature that was initially slightly lost in the noise due
    to being described half way through their announcement of the incredible Claude
    3.5 Sonnet.

    With Artifacts, Claude can write you an on-demand interactive application and
    then let you use it directly inside the Claude interface.

    Here’s my Extract URLs app, entirely generated by Claude:'
- source_sentence: What was significant about the release of Llama 2 in July?
  sentences:
  - 'Then in February, Meta released Llama. And a few weeks later in March, Georgi
    Gerganov released code that got it working on a MacBook.

    I wrote about how Large language models are having their Stable Diffusion moment,
    and with hindsight that was a very good call!

    This unleashed a whirlwind of innovation, which was accelerated further in July
    when Meta released Llama 2—an improved version which, crucially, included permission
    for commercial use.

    Today there are literally thousands of LLMs that can be run locally, on all manner
    of different devices.'
  - '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.'
  - 'Prompt injection is a natural consequence of this gulibility. I’ve seen precious
    little progress on tackling that problem in 2024, and we’ve been talking about
    it since September 2022.

    I’m beginning to see the most popular idea of “agents” as dependent on AGI itself.
    A model that’s robust against gulliblity is a very tall order indeed.

    Evals really matter

    Anthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):'
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: 0.8333333333333334
      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: 0.8333333333333334
      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: 0.8333333333333334
      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: 0.9384882922619097
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9166666666666666
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9166666666666666
      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("dwb2023/legal-ft-b5869012-93ce-4e45-bca9-2eb86f3ef4b9")
# Run inference
sentences = [
    'What was significant about the release of Llama 2 in July?',
    'Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook.\nI wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call!\nThis unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use.\nToday there are literally thousands of LLMs that can be run locally, on all manner of different devices.',
    'Prompt injection is a natural consequence of this gulibility. I’ve seen precious little progress on tackling that problem in 2024, and we’ve been talking about it since September 2022.\nI’m beginning to see the most popular idea of “agents” as dependent on AGI itself. A model that’s robust against gulliblity is a very tall order indeed.\nEvals really matter\nAnthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):',
]
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   | 0.8333     |
| cosine_accuracy@3   | 1.0        |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8333     |
| cosine_precision@3  | 0.3333     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8333     |
| cosine_recall@3     | 1.0        |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.9385** |
| cosine_mrr@10       | 0.9167     |
| cosine_map@100      | 0.9167     |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 156 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 156 samples:
  |         | sentence_0                                                                         | sentence_1                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              |
  | details | <ul><li>min: 12 tokens</li><li>mean: 20.89 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 135.1 tokens</li><li>max: 214 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                         | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
  |:---------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What are some of the topics covered in the annotated presentations given in 2023?</code>     | <code>I also gave a bunch of talks and podcast appearances. I’ve started habitually turning my talks into annotated presentations—here are my best from 2023:<br><br>Prompt injection explained, with video, slides, and a transcript<br>Catching up on the weird world of LLMs<br>Making Large Language Models work for you<br>Open questions for AI engineering<br>Embeddings: What they are and why they matter<br>Financial sustainability for open source projects at GitHub Universe<br><br>And in podcasts:<br><br><br>What AI can do for you on the Theory of Change<br><br>Working in public on Path to Citus Con<br><br>LLMs break the internet on the Changelog<br><br>Talking Large Language Models on Rooftop Ruby<br><br>Thoughts on the OpenAI board situation on Newsroom Robots</code> |
  | <code>Which podcasts featured discussions related to Large Language Models and AI topics?</code>   | <code>I also gave a bunch of talks and podcast appearances. I’ve started habitually turning my talks into annotated presentations—here are my best from 2023:<br><br>Prompt injection explained, with video, slides, and a transcript<br>Catching up on the weird world of LLMs<br>Making Large Language Models work for you<br>Open questions for AI engineering<br>Embeddings: What they are and why they matter<br>Financial sustainability for open source projects at GitHub Universe<br><br>And in podcasts:<br><br><br>What AI can do for you on the Theory of Change<br><br>Working in public on Path to Citus Con<br><br>LLMs break the internet on the Changelog<br><br>Talking Large Language Models on Rooftop Ruby<br><br>Thoughts on the OpenAI board situation on Newsroom Robots</code> |
  | <code>What is the main subject of the New York Times' lawsuit against OpenAI and Microsoft?</code> | <code>Just this week, the New York Times launched a landmark lawsuit against OpenAI and Microsoft over this issue. The 69 page PDF is genuinely worth reading—especially the first few pages, which lay out the issues in a way that’s surprisingly easy to follow. The rest of the document includes some of the clearest explanations of what LLMs are, how they work and how they are built that I’ve read anywhere.<br>The legal arguments here are complex. I’m not a lawyer, but I don’t think this one will be easily decided. Whichever way it goes, I expect this case to have a profound impact on how this technology develops in the future.</code>                                                                                                                                         |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| 1.0   | 16   | 0.9330         |
| 2.0   | 32   | 0.9539         |
| 3.0   | 48   | 0.9484         |
| 3.125 | 50   | 0.9484         |
| 4.0   | 64   | 0.9385         |
| 5.0   | 80   | 0.9539         |
| 6.0   | 96   | 0.9539         |
| 6.25  | 100  | 0.9539         |
| 7.0   | 112  | 0.9385         |
| 8.0   | 128  | 0.9385         |
| 9.0   | 144  | 0.9385         |
| 9.375 | 150  | 0.9385         |
| 10.0  | 160  | 0.9385         |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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