sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:800 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:Snowflake/snowflake-arctic-embed-m base_model:finetune:Snowflake/snowflake-arctic-embed-m model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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:800
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How can bias testing influence the design and launch of automated
    systems?
  sentences:
  - "reinforce those legal protections but extend beyond them to ensure equity for\
    \ underserved communities48 \neven in circumstances where a specific legal protection\
    \ may not be clearly established. These protections \nshould be instituted throughout\
    \ the design, development, and deployment process and are described below \nroughly\
    \ in the order in which they would be instituted. \nProtect the public from algorithmic\
    \ discrimination in a proactive and ongoing manner \nProactive assessment of equity\
    \ in design. Those responsible for the development, use, or oversight of"
  - "the severity of certain diseases in Black Americans. Instances of discriminatory\
    \ practices built into and \nresulting from AI and other automated systems exist\
    \ across many industries, areas, and contexts. While automated \nsystems have\
    \ the capacity to drive extraordinary advances and innovations, algorithmic discrimination\
    \ \nprotections should be built into their design, deployment, and ongoing use.\
    \ \nMany companies, non-profits, and federal government agencies are already taking\
    \ steps to ensure the public \nis protected from algorithmic discrimination. Some\
    \ companies have instituted bias testing as part of their product \nquality assessment\
    \ and launch procedures, and in some cases this testing has led products to be\
    \ changed or not"
  - "accuracy), and enable human users to understand, appropriately trust, and effectively\
    \ manage the emerging \ngeneration of artificially intelligent partners.95 The\
    \ National Science Foundation’s program on Fairness in \nArtificial Intelligence\
    \ also includes a specific interest in research foundations for explainable AI.96\n\
    45"
- source_sentence: What is the intended use of the systems mentioned in the context?
  sentences:
  - 'In discussion of technical and governance interventions that that are needed
    to protect against the harms of these technologies, panelists individually described
    the importance of: receiving community input into the design and use of technologies,
    public reporting on crucial elements of these systems, better notice and consent
    procedures that ensure privacy based on context and use case, ability to opt-out
    of using these systems and receive a fallback to a human process, providing explanations
    of decisions and how these systems work, the need for governance including training
    in using these systems, ensuring the technological use cases are genuinely related
    to the goal task and are locally validated to work, and the need for institution'
  - 'part of its loan underwriting and pricing model was found to be much more likely
    to charge an applicant whoattended a Historically Black College or University
    (HBCU) higher loan prices for refinancing a student loanthan an applicant who
    did not attend an HBCU. This was found to be true even when controlling for

    other credit-related factors.32

    •A hiring tool that learned the features of a company''s employees (predominantly
    men) rejected women appli -

    cants for spurious and discriminatory reasons; resumes with the word “women’s,”
    such as “women’s

    chess club captain,” were penalized in the candidate ranking.33'
  - systems with an intended use within sensi
- source_sentence: How did the hospital's software error affect the patient's access
    to pain medication?
  sentences:
  - '101

    •A fraud detection system for unemployment insurance distribution incorrectly
    flagged entries as fraudulent,leading to people with slight discrepancies or complexities
    in their files having their wages withheld and taxreturns seized without any chance
    to explain themselves or receive a review by a person.

    102

    •A patient was wrongly denied access to pain medication when the hospital’s software
    confused her medica -

    tion history with that of her dog’s. Even after she tracked down an explanation
    for the problem, doctorswere afraid to override the system, and she was forced
    to go without pain relief due to the system’s error.

    103'
  - "This section provides a brief summary of the problems that the principle seeks\
    \ to address and protect against, including illustrative examples. \nWHAT SHOULD\
    \ BE EXPECTED OF AUTOMATED SYSTEMS : \n•The expectations for automated systems\
    \ are meant to serve as a blueprint for the development of additional technical\n\
    standards and practices that should be tailored for particular sectors and contexts.\n\
    •This section outlines practical steps that can be implemented to realize the\
    \ vision of the Blueprint for an AI Bill of Rights. The"
  - "97 A human\ncuring process,98 which helps voters to confirm their signatures\
    \ and correct other voting mistakes, is\nimportant to ensure all votes are counted,99\
    \ and it is already standard practice in much of the country for\nboth an election\
    \ official and the voter to have the opportunity to review and correct any such\
    \ issues.100 \n47"
- source_sentence: Which organizations and individuals submitted the documents mentioned
    in the context?
  sentences:
  - "114 and were submitted by the below\nlisted organizations and individuals:\n\
    Accenture \nAccess Now ACT | The App Association AHIP \nAIethicist.org"
  - "APPENDIX\nPanelists discussed the benefits of AI-enabled systems and their potential\
    \ to build better and more \ninnovative infrastructure. They individually noted\
    \ that while AI technologies may be new, the process of \ntechnological diffusion\
    \ is not, and that it was critical to have thoughtful and responsible development\
    \ and \nintegration of technology within communities. Some p anelists suggested\
    \ that the integration of technology \ncould benefit from examining how technological\
    \ diffusion has worked in the realm of urban planning: \nlessons learned from\
    \ successes and failures there include the importance of balancing ownership rights,\
    \ use \nrights, and community health, safety and welfare, as well ensuring better\
    \ representation of all voices,"
  - "26Algorithmic \nDiscrimination \nProtections"
- source_sentence: What types of risks should be identified and mitigated before the
    deployment of an automated system?
  sentences:
  - "APPENDIX\nSystems  that impact the safety of communities such as automated traffic\
    \ control systems, elec \n-ctrical grid controls, smart city technologies, and\
    \ industrial emissions and environmental\nimpact control algorithms; and\nSystems\
    \  related to access to benefits or services or assignment of penalties such as\
    \ systems that"
  - "points to numerous examples of effective and proactive stakeholder engagement,\
    \ including the Community-\nBased Participatory Research Program developed by\
    \ the National Institutes of Health and the participatory \ntechnology assessments\
    \ developed by the National Oceanic and Atmospheric Administration.18\nThe National\
    \ Institute of Standards and Technology (NIST) is developing a risk \nmanagement\
    \ framework to better manage risks posed to individuals, organizations, and \n\
    society by AI.19 The NIST AI Risk Management Framework, as mandated by Congress,\
    \ is intended for \nvoluntary use to help incorporate trustworthiness considerations\
    \ into the design, development, use, and"
  - 'Risk identification and mitigation. Before deployment, and in a proactive and
    ongoing manner, poten -

    tial risks of the automated system should be identified and mitigated. Identified
    risks should focus on the potential for meaningful impact on people’s rights,
    opportunities, or access and include those to impacted communities that may not
    be direct users of the automated system, risks resulting from purposeful misuse
    of the system, and other concerns identified via the consultation process. Assessment
    and, where possible, mea

    -'
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: 0.815
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.93
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.945
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.98
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.815
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18899999999999997
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09799999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.815
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.93
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.945
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.98
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.903113322967423
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8781230158730159
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8794547466422467
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.815
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.93
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.945
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.98
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.815
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.31
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.18899999999999997
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09799999999999999
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.815
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.93
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.945
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.98
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.903113322967423
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.8781230158730159
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.8794547466422467
      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 types of risks should be identified and mitigated before the deployment of an automated system?',
    'Risk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten -\ntial risks of the automated system should be identified and mitigated. Identified risks should focus on the potential for meaningful impact on people’s rights, opportunities, or access and include those to impacted communities that may not be direct users of the automated system, risks resulting from purposeful misuse of the system, and other concerns identified via the consultation process. Assessment and, where possible, mea\n-',
    'APPENDIX\nSystems  that impact the safety of communities such as automated traffic control systems, elec \n-ctrical grid controls, smart city technologies, and industrial emissions and environmental\nimpact control algorithms; and\nSystems  related to access to benefits or services or assignment of penalties such as systems that',
]
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   | 0.815      |
| cosine_accuracy@3   | 0.93       |
| cosine_accuracy@5   | 0.945      |
| cosine_accuracy@10  | 0.98       |
| cosine_precision@1  | 0.815      |
| cosine_precision@3  | 0.31       |
| cosine_precision@5  | 0.189      |
| cosine_precision@10 | 0.098      |
| cosine_recall@1     | 0.815      |
| cosine_recall@3     | 0.93       |
| cosine_recall@5     | 0.945      |
| cosine_recall@10    | 0.98       |
| cosine_ndcg@10      | 0.9031     |
| cosine_mrr@10       | 0.8781     |
| **cosine_map@100**  | **0.8795** |
| dot_accuracy@1      | 0.815      |
| dot_accuracy@3      | 0.93       |
| dot_accuracy@5      | 0.945      |
| dot_accuracy@10     | 0.98       |
| dot_precision@1     | 0.815      |
| dot_precision@3     | 0.31       |
| dot_precision@5     | 0.189      |
| dot_precision@10    | 0.098      |
| dot_recall@1        | 0.815      |
| dot_recall@3        | 0.93       |
| dot_recall@5        | 0.945      |
| dot_recall@10       | 0.98       |
| dot_ndcg@10         | 0.9031     |
| dot_mrr@10          | 0.8781     |
| dot_map@100         | 0.8795     |

<!--
## 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: 800 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 800 samples:
  |         | sentence_0                                                                         | sentence_1                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              |
  | details | <ul><li>min: 10 tokens</li><li>mean: 20.05 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 116.96 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                       | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the purpose of the AI Bill of Rights mentioned in the context?</code>                                                              | <code>BLUEPRINT FOR AN <br>AI B ILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
  | <code>When was the Blueprint for an AI Bill of Rights published?</code>                                                                          | <code>BLUEPRINT FOR AN <br>AI B ILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
  | <code>What is the purpose of the Blueprint for an AI Bill of Rights published by the White House Office of Science and Technology Policy?</code> | <code>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was <br>published by the White House Office of Science and Technology Policy in October 2022. This framework was <br>released one year after OSTP announced  the launch of a process to develop “a bill of rights for an AI-powered <br>world.” Its release follows a year of public engagement to inform this initiative. The framework is available <br>online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights <br>About the Office of Science and Technology Policy <br>The Office of Science and Technology Policy (OSTP)  was established by the National Science 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   | 40   | 0.8784         |
| 1.25  | 50   | 0.8759         |
| 2.0   | 80   | 0.8795         |


### 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: 3.0.1
- 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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