README.md

---
base_model: Snowflake/snowflake-arctic-embed-m
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
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:600
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What considerations should be taken into account regarding the
    specific set or types of users for the AI system?
  sentences:
  - "46 \nMG-4.3-003 \nReport GAI incidents in compliance with legal and regulatory\
    \ requirements (e.g., \nHIPAA breach reporting, e.g., OCR (2023) or NHTSA (2022)\
    \ autonomous vehicle \ncrash reporting requirements. \nInformation Security; Data\
    \ Privacy \nAI Actor Tasks: AI Deployment, Affected Individuals and Communities,\
    \ Domain Experts, End-Users, Human Factors, Operation and \nMonitoring"
  - "reporting, data protection, data privacy, or other laws. \nData Privacy; Human-AI\
    \ \nConfiguration; Information \nSecurity; Value Chain and \nComponent Integration;\
    \ Harmful \nBias and Homogenization \nGV-6.2-004 \nEstablish policies and procedures\
    \ for continuous monitoring of third-party GAI \nsystems in deployment. \nValue\
    \ Chain and Component \nIntegration \nGV-6.2-005 \nEstablish policies and procedures\
    \ that address GAI data redundancy, including \nmodel weights and other system\
    \ artifacts."
  - "times, and availability of critical support. \nHuman-AI Configuration; \nInformation\
    \ Security; Value Chain \nand Component Integration \nAI Actor Tasks: AI Deployment,\
    \ Operation and Monitoring, TEVV, Third-party entities \n \nMAP 1.1: Intended\
    \ purposes, potentially beneficial uses, context specific laws, norms and expectations,\
    \ and prospective settings in \nwhich the AI system will be deployed are understood\
    \ and documented. Considerations include: the specific set or types of users"
- source_sentence: What should organizations leverage when deploying GAI applications
    and using third-party pre-trained models?
  sentences:
  - "external use, narrow vs. broad application scope, fine-tuning, and varieties of\
    \ \ndata sources (e.g., grounding, retrieval-augmented generation). \nData Privacy;\
    \ Intellectual \nProperty"
  - "44 \nMG-3.2-007 \nLeverage feedback and recommendations from organizational boards\
    \ or \ncommittees related to the deployment of GAI applications and content \n\
    provenance when using third-party pre-trained models. \nInformation Integrity;\
    \ Value Chain \nand Component Integration \nMG-3.2-008 \nUse human moderation\
    \ systems where appropriate to review generated content \nin accordance with human-AI\
    \ configuration policies established in the Govern"
  - "Security \nMS-2.7-003 \nConduct user surveys to gather user satisfaction with\
    \ the AI-generated content \nand user perceptions of content authenticity. Analyze\
    \ user feedback to identify \nconcerns and/or current literacy levels related\
    \ to content provenance and \nunderstanding of labels on content. \nHuman-AI Configuration;\
    \ \nInformation Integrity \nMS-2.7-004 \nIdentify metrics that reflect the effectiveness\
    \ of security measures, such as data"
- source_sentence: What are the potential positive and negative impacts of AI system
    uses on individuals and communities?
  sentences:
  - "and Homogenization \nAI Actor Tasks: AI Deployment, Affected Individuals and Communities,\
    \ End-Users, Operation and Monitoring, TEVV \n \nMEASURE 4.2: Measurement results\
    \ regarding AI system trustworthiness in deployment context(s) and across the\
    \ AI lifecycle are \ninformed by input from domain experts and relevant AI Actors\
    \ to validate whether the system is performing consistently as \nintended. Results\
    \ are documented. \nAction ID \nSuggested Action \nGAI Risks \nMS-4.2-001"
  - "bias based on race, gender, disability, or other protected classes.  \nHarmful\
    \ bias in GAI systems can also lead to harms via disparities between how a model\
    \ performs for \ndifferent subgroups or languages (e.g., an LLM may perform less\
    \ well for non-English languages or \ncertain dialects). Such disparities can\
    \ contribute to discriminatory decision-making or amplification of \nexisting societal\
    \ biases. In addition, GAI systems may be inappropriately trusted to perform similarly"
  - "along with their expectations; potential positive and negative impacts of system\
    \ uses to individuals, communities, organizations, \nsociety, and the planet;\
    \ assumptions and related limitations about AI system purposes, uses, and risks\
    \ across the development or \nproduct AI lifecycle; and related TEVV and system\
    \ metrics. \nAction ID \nSuggested Action \nGAI Risks \nMP-1.1-001 \nWhen identifying\
    \ intended purposes, consider factors such as internal vs."
- source_sentence: How does the suggested action MG-41-001 aim to address GAI risks?
  sentences:
  - "most appropriate baseline is to compare against, which can result in divergent\
    \ views on when a disparity between \nAI behaviors for different subgroups constitutes\
    \ a harm. In discussing harms from disparities such as biased \nbehavior, this\
    \ document highlights examples where someone’s situation is worsened relative\
    \ to what it would have \nbeen in the absence of any AI system, making the outcome\
    \ unambiguously a harm of the system."
  - "Harmful Bias Managed, Privacy Enhanced, Safe, Secure and Resilient, Valid and\
    \ Reliable \n3. \nSuggested Actions to Manage GAI Risks \nThe following suggested\
    \ actions target risks unique to or exacerbated by GAI. \nIn addition to the suggested\
    \ actions below, AI risk management activities and actions set forth in the AI\
    \ \nRMF 1.0 and Playbook are already applicable for managing GAI risks. Organizations\
    \ are encouraged to"
  - "MANAGE 4.1: Post-deployment AI system monitoring plans are implemented, including\
    \ mechanisms for capturing and evaluating \ninput from users and other relevant\
    \ AI Actors, appeal and override, decommissioning, incident response, recovery,\
    \ and change \nmanagement. \nAction ID \nSuggested Action \nGAI Risks \nMG-4.1-001\
    \ \nCollaborate with external researchers, industry experts, and community \n\
    representatives to maintain awareness of emerging best practices and"
- source_sentence: What are some examples of input data features that may serve as
    proxies for demographic group membership in GAI systems?
  sentences:
  - "data privacy violations, obscenity, extremism, violence, or CBRN information\
    \ in \nsystem training data. \nData Privacy; Intellectual Property; \nObscene,\
    \ Degrading, and/or \nAbusive Content; Harmful Bias and \nHomogenization; Dangerous,\
    \ \nViolent, or Hateful Content; CBRN \nInformation or Capabilities \nMS-2.6-003\
    \ Re-evaluate safety features of fine-tuned models when the negative risk exceeds\
    \ \norganizational risk tolerance. \nDangerous, Violent, or Hateful \nContent"
  - "GAI. \nInformation Integrity; Intellectual \nProperty \nAI Actor Tasks: Governance\
    \ and Oversight, Operation and Monitoring \n \nGOVERN 1.6: Mechanisms are in place\
    \ to inventory AI systems and are resourced according to organizational risk priorities.\
    \ \nAction ID \nSuggested Action \nGAI Risks \nGV-1.6-001 Enumerate organizational\
    \ GAI systems for incorporation into AI system inventory \nand adjust AI system\
    \ inventory requirements to account for GAI risks. \nInformation Security"
  - "complex or unstructured data; Input data features that may serve as proxies for\
    \ \ndemographic group membership (i.e., image metadata, language dialect) or \n\
    otherwise give rise to emergent bias within GAI systems; The extent to which \n\
    the digital divide may negatively impact representativeness in GAI system \ntraining\
    \ and TEVV data; Filtering of hate speech or content in GAI system \ntraining\
    \ data; Prevalence of GAI-generated data in GAI system training data. \nHarmful\
    \ Bias and Homogenization"
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.85
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.975
      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: 0.85
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.325
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.85
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.975
      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: 0.9341754705038519
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.911875
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9118749999999999
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.85
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.975
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 1.0
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 1.0
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.85
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.325
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.19999999999999998
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09999999999999999
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.85
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.975
      name: Dot Recall@3
    - type: dot_recall@5
      value: 1.0
      name: Dot Recall@5
    - type: dot_recall@10
      value: 1.0
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.9341754705038519
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.911875
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.9118749999999999
      name: Dot Map@100
---

# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). It maps sentences & paragraphs to a 768-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-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision e2b128b9fa60c82b4585512b33e1544224ffff42 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **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': 768, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'What are some examples of input data features that may serve as proxies for demographic group membership in GAI systems?',
    'complex or unstructured data; Input data features that may serve as proxies for \ndemographic group membership (i.e., image metadata, language dialect) or \notherwise give rise to emergent bias within GAI systems; The extent to which \nthe digital divide may negatively impact representativeness in GAI system \ntraining and TEVV data; Filtering of hate speech or content in GAI system \ntraining data; Prevalence of GAI-generated data in GAI system training data. \nHarmful Bias and Homogenization',
    'GAI. \nInformation Integrity; Intellectual \nProperty \nAI Actor Tasks: Governance and Oversight, Operation and Monitoring \n \nGOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities. \nAction ID \nSuggested Action \nGAI Risks \nGV-1.6-001 Enumerate organizational GAI systems for incorporation into AI system inventory \nand adjust AI system inventory requirements to account for GAI risks. \nInformation Security',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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   | 0.85       |
| cosine_accuracy@3   | 0.975      |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.85       |
| cosine_precision@3  | 0.325      |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.85       |
| cosine_recall@3     | 0.975      |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9342     |
| cosine_mrr@10       | 0.9119     |
| **cosine_map@100**  | **0.9119** |
| dot_accuracy@1      | 0.85       |
| dot_accuracy@3      | 0.975      |
| dot_accuracy@5      | 1.0        |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.85       |
| dot_precision@3     | 0.325      |
| dot_precision@5     | 0.2        |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.85       |
| dot_recall@3        | 0.975      |
| dot_recall@5        | 1.0        |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.9342     |
| dot_mrr@10          | 0.9119     |
| dot_map@100         | 0.9119     |

<!--
## 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: 600 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 600 samples:
  |         | sentence_0                                                                         | sentence_1                                                                         |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 11 tokens</li><li>mean: 20.85 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 89.39 tokens</li><li>max: 335 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                         | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
  |:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the title of the publication related to Artificial Intelligence Risk Management by NIST?</code>      | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600-1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br> <br> <br>This publication is available free of charge from: <br>https://doi.org/10.6028/NIST.AI.600-1</code>                                                                                                                                                                                                                                                                |
  | <code>Where can the NIST AI 600-1 publication be accessed for free?</code>                                         | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600-1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br> <br> <br>This publication is available free of charge from: <br>https://doi.org/10.6028/NIST.AI.600-1</code>                                                                                                                                                                                                                                                                |
  | <code>What is the title of the publication released by NIST in July 2024 regarding artificial intelligence?</code> | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600-1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br> <br> <br>This publication is available free of charge from: <br>https://doi.org/10.6028/NIST.AI.600-1 <br> <br>July 2024 <br> <br> <br> <br> <br>U.S. Department of Commerce  <br>Gina M. Raimondo, Secretary <br>National Institute of Standards and Technology  <br>Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology</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`: 20
- `per_device_eval_batch_size`: 20
- `num_train_epochs`: 5
- `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`: 20
- `per_device_eval_batch_size`: 20
- `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`: 5
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | cosine_map@100 |
|:------:|:----:|:--------------:|
| 1.0    | 30   | 0.9271         |
| 1.6667 | 50   | 0.9306         |
| 2.0    | 60   | 0.9187         |
| 3.0    | 90   | 0.9244         |
| 3.3333 | 100  | 0.9244         |
| 4.0    | 120  | 0.9244         |
| 5.0    | 150  | 0.9119         |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.0
- Tokenizers: 0.19.1

## 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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