sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:200 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:Snowflake/snowflake-arctic-embed-l base_model:finetune:Snowflake/snowflake-arctic-embed-l model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Snowflake/snowflake-arctic-embed-l
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:200
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How do longer inputs enhance the problem-solving capabilities of
    an LLM?
  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.'
  - 'If you think about what they do, this isn’t such a big surprise. The grammar
    rules of programming languages like Python and JavaScript are massively less complicated
    than the grammar of Chinese, Spanish or English.

    It’s still astonishing to me how effective they are though.

    One of the great weaknesses of LLMs is their tendency to hallucinate—to imagine
    things that don’t correspond to reality. You would expect this to be a particularly
    bad problem for code—if an LLM hallucinates a method that doesn’t exist, the code
    should be useless.'
  - "blogging\n            68\n\n\n            ai\n            1098\n\n\n        \
    \    generative-ai\n            942\n\n\n            llms\n            930\n\n\
    Next: Tom Scott, and the formidable power of escalating streaks\nPrevious: Last\
    \ weeknotes of 2023\n\n\n \n \n\n\nColophon\n©\n2002\n2003\n2004\n2005\n2006\n\
    2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n2015\n2016\n2017\n2018\n2019\n\
    2020\n2021\n2022\n2023\n2024\n2025"
- source_sentence: How did other teams respond to the author's use of Claude Artifacts?
  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’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.'
  - Structured and Gradual Learning. In organic datasets, the relationship between
    tokens is often complex and indirect. Many reasoning steps may be required to
    connect the current token to the next, making it challenging for the model to
    learn effectively from next-token prediction. By contrast, each token generated
    by a language model is by definition predicted by the preceding tokens, making
    it easier for a model to follow the resulting reasoning patterns.
- source_sentence: How does the new llamafile improve the process of running an LLM
    on a personal computer?
  sentences:
  - 'A year ago, the only organization that had released a generally useful LLM was
    OpenAI. We’ve now seen better-than-GPT-3 class models produced by Anthropic, Mistral,
    Google, Meta, EleutherAI, Stability AI, TII in Abu Dhabi (Falcon), Microsoft Research,
    xAI, Replit, Baidu and a bunch of other organizations.

    The training cost (hardware and electricity) is still significant—initially millions
    of dollars, but that seems to have dropped to the tens of thousands already. Microsoft’s
    Phi-2 claims to have used “14 days on 96 A100 GPUs”, which works out at around
    $35,000 using current Lambda pricing.'
  - 'Embeddings: What they are and why they matter

    61.7k

    79.3k



    Catching up on the weird world of LLMs

    61.6k

    85.9k



    llamafile is the new best way to run an LLM on your own computer

    52k

    66k



    Prompt injection explained, with video, slides, and a transcript

    51k

    61.9k



    AI-enhanced development makes me more ambitious with my projects

    49.6k

    60.1k



    Understanding GPT tokenizers

    49.5k

    61.1k



    Exploring GPTs: ChatGPT in a trench coat?

    46.4k

    58.5k



    Could you train a ChatGPT-beating model for $85,000 and run it in a browser?

    40.5k

    49.2k



    How to implement Q&A against your documentation with GPT3, embeddings and Datasette

    37.3k

    44.9k



    Lawyer cites fake cases invented by ChatGPT, judge is not amused

    37.1k

    47.4k'
  - 'Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac talks about
    Qwen2.5-Coder-32B in November—an Apache 2.0 licensed model!


    I can now run a GPT-4 class model on my laptop talks about running Meta’s Llama
    3.3 70B (released in December)'
- source_sentence: What technique is being used by an increasing number of labs to
    create training data for smaller models?
  sentences:
  - 'Another common technique is to use larger models to help create training data
    for their smaller, cheaper alternatives—a trick used by an increasing number of
    labs. DeepSeek v3 used “reasoning” data created by DeepSeek-R1. Meta’s Llama 3.3
    70B fine-tuning used over 25M synthetically generated examples.

    Careful design of the training data that goes into an LLM appears to be the entire
    game for creating these models. The days of just grabbing a full scrape of the
    web and indiscriminately dumping it into a training run are long gone.

    LLMs somehow got even harder to use'
  - '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'
  - 'Here’s the sequel to this post: Things we learned about LLMs in 2024.

    Large Language Models

    In the past 24-36 months, our species has discovered that you can take a GIANT
    corpus of text, run it through a pile of GPUs, and use it to create a fascinating
    new kind of software.

    LLMs can do a lot of things. They can answer questions, summarize documents, translate
    from one language to another, extract information and even write surprisingly
    competent code.

    They can also help you cheat at your homework, generate unlimited streams of fake
    content and be used for all manner of nefarious purposes.'
- source_sentence: What are some reasons why better informed people have chosen to
    avoid using LLMs?
  sentences:
  - '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 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'
  - 'The year of slop

    2024 was the year that the word "slop" became a term of art. I wrote about this
    in May, expanding on this tweet by @deepfates:'
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: 0.7580645161290323
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.967741935483871
      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: 0.7580645161290323
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3225806451612902
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19999999999999993
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999996
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7580645161290323
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.967741935483871
      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: 0.8958019499724179
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.860215053763441
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8602150537634409
      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("dataera2013/mt-1")
# Run inference
sentences = [
    'What are some reasons why better informed people have chosen to avoid using 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?',
    'The year of slop\n2024 was the year that the word "slop" became a term of art. I wrote about this in May, expanding on this tweet by @deepfates:',
]
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   | 0.7581     |
| cosine_accuracy@3   | 0.9677     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.7581     |
| cosine_precision@3  | 0.3226     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.7581     |
| cosine_recall@3     | 0.9677     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.8958** |
| cosine_mrr@10       | 0.8602     |
| cosine_map@100      | 0.8602     |

<!--
## 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: 200 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 200 samples:
  |         | sentence_0                                                                         | sentence_1                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 12 tokens</li><li>mean: 20.32 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 134.92 tokens</li><li>max: 214 tokens</li></ul> |
* Samples:
  | sentence_0                                                                            | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
  |:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the new name for unwanted AI-generated content mentioned in May?</code> | <code>17th: AI for Data Journalism: demonstrating what we can do with this stuff right now<br><br>22nd: Options for accessing Llama 3 from the terminal using LLM<br><br><br><br>May<br><br>8th: Slop is the new name for unwanted AI-generated content<br><br>15th: ChatGPT in “4o” mode is not running the new features yet<br><br>29th: Training is not the same as chatting: ChatGPT and other LLMs don’t remember everything you say<br><br><br><br>June<br><br>6th: Accidental prompt injection against RAG applications<br><br>10th: Thoughts on the WWDC 2024 keynote on Apple Intelligence<br><br>17th: Language models on the command-line<br><br>21st: Building search-based RAG using Claude, Datasette and Val Town<br><br>27th: Open challenges for AI engineering<br><br><br><br>July<br><br>14th: Imitation Intelligence, my keynote for PyCon US 2024</code> |
  | <code>What are the options for accessing Llama 3 from the terminal?</code>            | <code>17th: AI for Data Journalism: demonstrating what we can do with this stuff right now<br><br>22nd: Options for accessing Llama 3 from the terminal using LLM<br><br><br><br>May<br><br>8th: Slop is the new name for unwanted AI-generated content<br><br>15th: ChatGPT in “4o” mode is not running the new features yet<br><br>29th: Training is not the same as chatting: ChatGPT and other LLMs don’t remember everything you say<br><br><br><br>June<br><br>6th: Accidental prompt injection against RAG applications<br><br>10th: Thoughts on the WWDC 2024 keynote on Apple Intelligence<br><br>17th: Language models on the command-line<br><br>21st: Building search-based RAG using Claude, Datasette and Val Town<br><br>27th: Open challenges for AI engineering<br><br><br><br>July<br><br>14th: Imitation Intelligence, my keynote for PyCon US 2024</code> |
  | <code>What is the name of the model that the author runs on their iPhone?</code>      | <code>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.<br>You can even run them entirely in your browser using WebAssembly and the latest Chrome!<br>Hobbyists can build their own fine-tuned models<br>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.</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   | 20   | 0.9008         |
| 2.0   | 40   | 0.9190         |
| 2.5   | 50   | 0.9050         |
| 3.0   | 60   | 0.9050         |
| 4.0   | 80   | 0.8990         |
| 5.0   | 100  | 0.9109         |
| 6.0   | 120  | 0.9029         |
| 7.0   | 140  | 0.9018         |
| 7.5   | 150  | 0.9029         |
| 8.0   | 160  | 0.9029         |
| 9.0   | 180  | 0.8958         |
| 10.0  | 200  | 0.8958         |


### Framework Versions
- Python: 3.13.1
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- 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}
}
```

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