sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:156 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:156
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How much has the cost of using OpenAI's most expensive model changed
    compared to the previous pricing?
  sentences:
  - Synthetic data as a substantial component of pretraining is becoming increasingly
    common, and the Phi series of models has consistently emphasized the importance
    of synthetic data. Rather than serving as a cheap substitute for organic data,
    synthetic data has several direct advantages over organic data.
  - 'Here’s the rest of the transcript. It’s bland and generic, but my phone can pitch
    bland and generic Christmas movies to Netflix now!

    LLM prices crashed, thanks to competition and increased efficiency

    The past twelve months have seen a dramatic collapse in the cost of running a
    prompt through the top tier hosted LLMs.

    In December 2023 (here’s the Internet Archive for the OpenAI pricing page) OpenAI
    were charging $30/million input tokens for GPT-4, $10/mTok for the then-new GPT-4
    Turbo and $1/mTok for GPT-3.5 Turbo.

    Today $30/mTok gets you OpenAI’s most expensive model, o1. GPT-4o is $2.50 (12x
    cheaper than GPT-4) and GPT-4o mini is $0.15/mTok—nearly 7x cheaper than GPT-3.5
    and massively more capable.'
  - 'Then there’s the rest. If you browse the Chatbot Arena leaderboard today—still
    the most useful single place to get a vibes-based evaluation of models—you’ll
    see that GPT-4-0314 has fallen to around 70th place. The 18 organizations with
    higher scoring models are Google, OpenAI, Alibaba, Anthropic, Meta, Reka AI, 01
    AI, Amazon, Cohere, DeepSeek, Nvidia, Mistral, NexusFlow, Zhipu AI, xAI, AI21
    Labs, Princeton and Tencent.

    Training a GPT-4 beating model was a huge deal in 2023. In 2024 it’s an achievement
    that isn’t even particularly notable, though I personally still celebrate any
    time a new organization joins that list.

    Some of those GPT-4 models run on my laptop'
- source_sentence: What are some potential consequences of making decisions based
    on hype and misinformation?
  sentences:
  - 'The GPT-4 barrier was comprehensively broken

    In my December 2023 review I wrote about how We don’t yet know how to build GPT-4—OpenAI’s
    best model was almost a year old at that point, yet no other AI lab had produced
    anything better. What did OpenAI know that the rest of us didn’t?

    I’m relieved that this has changed completely in the past twelve months. 18 organizations
    now have models on the Chatbot Arena Leaderboard that rank higher than the original
    GPT-4 from March 2023 (GPT-4-0314 on the board)—70 models in total.'
  - 'I like people who are skeptical of this stuff. The hype has been deafening for
    more than two years now, and there are enormous quantities of snake oil and misinformation
    out there. A lot of very bad decisions are being made based on that hype. Being
    critical is a virtue.

    If we want people with decision-making authority to make good decisions about
    how to apply these tools we first need to acknowledge that there ARE good applications,
    and then help explain how to put those into practice while avoiding the many unintiutive
    traps.

    (If you still don’t think there are any good applications at all I’m not sure
    why you made it to this point in the article!)'
  - '17th: AI for Data Journalism: demonstrating what we can do with this stuff right
    now


    22nd: Options for accessing Llama 3 from the terminal using LLM




    May


    8th: Slop is the new name for unwanted AI-generated content


    15th: ChatGPT in “4o” mode is not running the new features yet


    29th: Training is not the same as chatting: ChatGPT and other LLMs don’t remember
    everything you say




    June


    6th: Accidental prompt injection against RAG applications


    10th: Thoughts on the WWDC 2024 keynote on Apple Intelligence


    17th: Language models on the command-line


    21st: Building search-based RAG using Claude, Datasette and Val Town


    27th: Open challenges for AI engineering




    July


    14th: Imitation Intelligence, my keynote for PyCon US 2024'
- source_sentence: What advancements have been made in multimodal vision and audio/video
    capabilities in LLMs?
  sentences:
  - '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:'
  - 'The GPT-4 barrier was comprehensively broken

    Some of those GPT-4 models run on my laptop

    LLM prices crashed, thanks to competition and increased efficiency

    Multimodal vision is common, audio and video are starting to emerge

    Voice and live camera mode are science fiction come to life

    Prompt driven app generation is a commodity already

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

    “Agents” still haven’t really happened yet

    Evals really matter

    Apple Intelligence is bad, Apple’s MLX library is excellent

    The rise of inference-scaling “reasoning” models

    Was the best currently available LLM trained in China for less than $6m?

    The environmental impact got better

    The environmental impact got much, much worse'
  - "Posted 31st December 2024 at 6:07 pm · Follow me on Mastodon or Twitter or subscribe\
    \ to my newsletter\n\n\nMore recent articles\n\nLLM 0.22, the annotated release\
    \ notes - 17th February 2025\nRun LLMs on macOS using llm-mlx and Apple's MLX\
    \ framework - 15th February 2025\nURL-addressable Pyodide Python environments\
    \ - 13th February 2025\n\n\n \n\n\nThis is Things we learned about LLMs in 2024\
    \ by Simon Willison, posted on 31st December 2024.\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 \
    \           google\n            347\n\n\n            ai\n            1100\n\n\n\
    \            openai\n            257"
- source_sentence: When did the author first run a large language model on their laptop?
  sentences:
  - '24th: Notes on the new Claude analysis JavaScript code execution tool


    27th: Run a prompt to generate and execute jq programs using llm-jq


    29th: You can now run prompts against images, audio and video in your terminal
    using LLM


    30th: W̶e̶e̶k̶n̶o̶t̶e̶s̶  Monthnotes for October




    November


    4th: Claude 3.5 Haiku


    7th: Project: VERDAD—tracking misinformation in radio broadcasts using Gemini
    1.5


    12th: Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac


    19th: Notes from Bing Chat—Our First Encounter With Manipulative AI


    25th: Ask questions of SQLite databases and CSV/JSON files in your terminal




    December


    4th: First impressions of the new Amazon Nova LLMs (via a new llm-bedrock plugin)


    7th: Prompts.js'
  - '260 input tokens, 92 output tokens. Cost approximately 0.0024 cents (that’s less
    than a 400th of a cent).

    This increase in efficiency and reduction in price is my single favourite trend
    from 2024. I want the utility of LLMs at a fraction of the energy cost and it
    looks like that’s what we’re getting.

    Multimodal vision is common, audio and video are starting to emerge

    My butterfly example above illustrates another key trend from 2024: the rise of
    multi-modal LLMs.

    A year ago the single most notable example of these was GPT-4 Vision, released
    at OpenAI’s DevDay in November 2023. Google’s multi-modal Gemini 1.0 was announced
    on December 7th 2023 so it also (just) makes it into the 2023 window.'
  - 'My personal laptop is a 64GB M2 MacBook Pro from 2023. It’s a powerful machine,
    but it’s also nearly two years old now—and crucially it’s the same laptop I’ve
    been using ever since I first ran an LLM on my computer back in March 2023 (see
    Large language models are having their Stable Diffusion moment).

    That same laptop that could just about run a GPT-3-class model in March last year
    has now run multiple GPT-4 class models! Some of my notes on that:'
- source_sentence: What notable development in LLM technology occurred in the final
    quarter of 2024?
  sentences:
  - 'Now that those features are rolling out they’re pretty weak. As an LLM power-user
    I know what these models are capable of, and Apple’s LLM features offer a pale
    imitation of what a frontier LLM can do. Instead we’re getting notification summaries
    that misrepresent news headlines and writing assistant tools that I’ve not found
    useful at all. Genmoji are kind of fun though.

    The rise of inference-scaling “reasoning” models

    The most interesting development in the final quarter of 2024 was the introduction
    of a new shape of LLM, exemplified by OpenAI’s o1 models—initially released as
    o1-preview and o1-mini on September 12th.'
  - 'The year of slop

    Synthetic training data works great

    LLMs somehow got even harder to use

    Knowledge is incredibly unevenly distributed

    LLMs need better criticism

    Everything tagged “llms” on my blog in 2024'
  - '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):'
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: 0.9330328858630988
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9097222222222222
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9097222222222222
      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("dabraldeepti25/legal-ft-v0")
# Run inference
sentences = [
    'What notable development in LLM technology occurred in the final quarter of 2024?',
    'Now that those features are rolling out they’re pretty weak. As an LLM power-user I know what these models are capable of, and Apple’s LLM features offer a pale imitation of what a frontier LLM can do. Instead we’re getting notification summaries that misrepresent news headlines and writing assistant tools that I’ve not found useful at all. Genmoji are kind of fun though.\nThe rise of inference-scaling “reasoning” models\nThe most interesting development in the final quarter of 2024 was the introduction of a new shape of LLM, exemplified by OpenAI’s o1 models—initially released as o1-preview and o1-mini on September 12th.',
    '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**  | **0.933** |
| cosine_mrr@10       | 0.9097    |
| cosine_map@100      | 0.9097    |

<!--
## 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: 20.06 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 130.5 tokens</li><li>max: 204 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                        | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  |:----------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the significance of Claude Artifacts in the context of LLMs and application development?</code>                     | <code>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.<br>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.<br>With Artifacts, Claude can write you an on-demand interactive application and then let you use it directly inside the Claude interface.<br>Here’s my Extract URLs app, entirely generated by Claude:</code> |
  | <code>How does Claude enable users to interact with applications generated by its capabilities?</code>                            | <code>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.<br>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.<br>With Artifacts, Claude can write you an on-demand interactive application and then let you use it directly inside the Claude interface.<br>Here’s my Extract URLs app, entirely generated by Claude:</code> |
  | <code>What are some of the new capabilities introduced in multi-modal models that enhance their functionality beyond text?</code> | <code>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.<br>Voice and live camera mode are science fiction come to life<br>The audio and live video modes that have started to emerge deserve a special mention.<br>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.</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   | 0.9039         |
| 2.0   | 32   | 0.9010         |
| 3.0   | 48   | 0.9218         |
| 3.125 | 50   | 0.9218         |
| 4.0   | 64   | 0.9218         |
| 5.0   | 80   | 0.9247         |
| 6.0   | 96   | 0.9330         |
| 6.25  | 100  | 0.9330         |
| 7.0   | 112  | 0.9330         |
| 8.0   | 128  | 0.9330         |
| 9.0   | 144  | 0.9330         |
| 9.375 | 150  | 0.9330         |
| 10.0  | 160  | 0.9330         |


### 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}
}
```

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