sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:1539 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:1539
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How do the models ensure the production of valid, reliable, and
    factually accurate outputs while assessing risks associated with content provenance
    and offensive cyber activities?
  sentences:
  - "Information or Capabilities  \nMS-1.1-0 05 Evaluate novel methods and technologies\
    \ for the measurement of GAI-related \nrisks in cluding in  content provenance\
    \ , oﬀensive cy ber, and CBRN , while \nmaintaining the models’ ability to produce\
    \ valid, reliable, and factually accurate outputs.  Information Integrity ; CBRN\
    \ \nInformation or Capabilities ; \nObscene, Degrading, and/or Abusive Content"
  - Testing. Systems should undergo extensive testing before deployment. This testing
    should follow domain-specific best practices, when available, for ensuring the
    technology will work in its real-world context. Such testing should take into
    account both the specific technology used and the roles of any human operators
    or reviewers who impact system outcomes or effectiveness; testing should include
    both automated systems testing and human-led (manual) testing. Testing conditions
    should mirror as
  - "oping technologies related to a sensitive domain and those collecting, using,\
    \ storing, or sharing sensitive data \nshould, whenever appropriate, regularly\
    \ provide public reports describing: any data security lapses or breaches \nthat\
    \ resulted in sensitive data leaks; the numbe r, type, and outcomes of ethical\
    \ pre-reviews undertaken; a \ndescription of any data sold, shared, or made public,\
    \ and how that data was assessed to determine it did not pres-"
- source_sentence: How should automated systems handle user data in terms of collection
    and user consent according to the provided context?
  sentences:
  - 'Property Appraisal and Valuation Equity: Closing the Racial Wealth Gap by Addressing
    Mis-valuations for

    Families and Communities of Color. March 2022. https://pave.hud.gov/sites/pave.hud.gov/files/

    documents/PAVEActionPlan.pdf

    53. U.S. Equal Employment Opportunity Commission. The Americans with Disabilities
    Act and the Use of

    Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and
    Employees . EEOC-'
  - "defense, substantive or procedural, enforceable at law or in equity by any party\
    \ against the United States, its \ndepartments, agencies, or entities, its officers,\
    \ employees, or agents, or any other person, nor does it constitute a \nwaiver\
    \ of sovereign immunity. \nCopyright Information \nThis document is a work of\
    \ the United States Government and is in the public domain (see 17 U.S.C. §105).\
    \ \n2"
  - "privacy through design choices that ensure such protections are included by default,\
    \ including ensuring that data collection conforms to reasonable expectations\
    \ and that only data strictly necessary for the specific context is collected.\
    \ Designers, developers, and deployers of automated systems should seek your permission\
    \ \nand respect your decisions regarding collection, use, access, transfer, and\
    \ deletion of your data in appropriate"
- source_sentence: How many participants attended the listening sessions organized
    for members of the public?
  sentences:
  - "37 MS-2.11-0 05 Assess the proportion of synthetic to non -synthetic training\
    \ data and verify \ntraining data is not overly homogenous or  GAI-produced to\
    \ mitigate concerns of \nmodel collapse.  Harmful Bias and Homogenization  \n\
    AI Actor Tasks:  AI Deployment, AI Impact Assessment, Aﬀected Individuals and\
    \ Communities, Domain Experts, End -Users, \nOperation and Monitoring, TEVV"
  - "lenders who may be avoiding serving communities of color are conducting targeted\
    \ marketing and advertising.51 \nThis initiative will draw upon strong partnerships\
    \ across federal agencies, including the Consumer Financial \nProtection Bureau\
    \ and prudential regulators. The Action Plan to Advance Property Appraisal and\
    \ Valuation \nEquity includes a commitment from the agencies that oversee mortgage\
    \ lending to include a"
  - 'for members of the public. The listening sessions together drew upwards of 300
    participants. The Science and

    Technology Policy Institute produced a synopsis of both the RFI submissions and
    the feedback at the listeningsessions.

    115

    61'
- source_sentence: Why is it particularly important to monitor the risks of confabulated
    content when integrating Generative AI (GAI) into applications that involve consequential
    decision making?
  sentences:
  - of how and what the technologies are doing. Some panelists suggested that technology
    should be used to help people receive benefits, e.g., by pushing benefits to those
    in need and ensuring automated decision-making systems are only used to provide
    a positive outcome; technology shouldn't be used to take supports away from people
    who need them.
  - "many real -world applications, such as in healthcare, where a confabulated summary\
    \ of patient \ninformation reports could  cause doctors to make  incorrect diagnoses\
    \  and/or recommend the wrong \ntreatments.  Risks of confabulated content may\
    \ be especially important to monitor  when integrating GAI \ninto applications\
    \ involving  consequential  decision making. \nGAI outputs may also include confabulated\
    \ logic or citations  that purport to justify or explain the"
  - "settings or in the public domain.  \nOrganizations can restrict AI applications\
    \ that cause harm, exceed stated risk tolerances, or that conﬂict with their tolerances\
    \ or values. Governance tools and protocols that are applied to other types of\
    \ AI systems can be applied to GAI systems. These p lans and actions include:\
    \ \n• Accessibility and reasonable accommodations  \n• AI actor credentials and\
    \ qualiﬁcations  \n• Alignment to organizational values  • Auditing and assessment"
- source_sentence: How does the framework address the concerns related to the rapid
    innovation and changing definitions of AI systems?
  sentences:
  - or inequality. Assessment could include both qualitative and quantitative evaluations
    of the system. This equity assessment should also be considered a core part of
    the goals of the consultation conducted as part of the safety and efficacy review.
  - "deactivate AI systems that demonstrate performance or outcomes inconsistent with\
    \ intended use.  \nAction ID  Suggested Action  GAI Risks  \nMG-2.4-001 Establish\
    \ and maintain communication plans to inform AI stakeholders as part of \nthe\
    \ deactivation or disengagement process of a speciﬁc GAI system (including for\
    \ open -source  models) or context of use, including r easons, workarounds, user\
    \ \naccess removal, alternative processes, contact information, etc.  Human -AI\
    \ Conﬁguration"
  - "SECTION  TITLE\nApplying The Blueprint for an AI Bill of Rights \nWhile many\
    \ of the concerns addressed in this framework derive from the use of AI, the technical\
    \ \ncapabilities and specific definitions of such systems change with the speed\
    \ of innovation, and the potential \nharms of their use occur even with less technologically\
    \ sophisticated tools. Thus, this framework uses a two-\npart test to determine\
    \ what systems are in scope. This framework applies to (1) automated systems that\
    \ (2)"
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: 0.9270833333333334
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9947916666666666
      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: 0.9270833333333334
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.33159722222222227
      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: 0.9270833333333334
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9947916666666666
      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: 0.969317939271961
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9587673611111113
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9587673611111112
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.9270833333333334
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.9947916666666666
      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: 0.9270833333333334
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.33159722222222227
      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: 0.9270833333333334
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.9947916666666666
      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: 0.969317939271961
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.9587673611111113
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.9587673611111112
      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("Technocoloredgeek/midterm-finetuned-embedding")
# Run inference
sentences = [
    'How does the framework address the concerns related to the rapid innovation and changing definitions of AI systems?',
    'SECTION  TITLE\nApplying The Blueprint for an AI Bill of Rights \nWhile many of the concerns addressed in this framework derive from the use of AI, the technical \ncapabilities and specific definitions of such systems change with the speed of innovation, and the potential \nharms of their use occur even with less technologically sophisticated tools. Thus, this framework uses a two-\npart test to determine what systems are in scope. This framework applies to (1) automated systems that (2)',
    'or inequality. Assessment could include both qualitative and quantitative evaluations of the system. This equity assessment should also be considered a core part of the goals of the consultation conducted as part of the safety and efficacy review.',
]
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   | 0.9271     |
| cosine_accuracy@3   | 0.9948     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9271     |
| cosine_precision@3  | 0.3316     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9271     |
| cosine_recall@3     | 0.9948     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9693     |
| cosine_mrr@10       | 0.9588     |
| **cosine_map@100**  | **0.9588** |
| dot_accuracy@1      | 0.9271     |
| dot_accuracy@3      | 0.9948     |
| dot_accuracy@5      | 1.0        |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.9271     |
| dot_precision@3     | 0.3316     |
| dot_precision@5     | 0.2        |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.9271     |
| dot_recall@3        | 0.9948     |
| dot_recall@5        | 1.0        |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.9693     |
| dot_mrr@10          | 0.9588     |
| dot_map@100         | 0.9588     |

<!--
## 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: 1,539 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                         | sentence_1                                                                        |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 12 tokens</li><li>mean: 23.91 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 84.9 tokens</li><li>max: 335 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                 | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
  |:-------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What are confabulations in the context of generative AI outputs, and how do they arise from the design of generative models?</code>  | <code>Confabulations can occur across GAI outputs  and contexts .9,10 Confabulations are a natural result of the <br>way generative models  are designed : they  generate outputs that approximate the statistical distribution <br>of their training data ; for example,  LLMs  predict the next  token or word  in a sentence or phrase . While <br>such statistical  prediction can produce factual ly accurate  and consistent  outputs , it can  also produce</code>             |
  | <code>What roles do Rashida Richardson and Karen Kornbluh hold in relation to technology and democracy as mentioned in the context?</code> | <code>products, advanced platforms and services, “Internet of Things” (IoT) devices, and smart city products and services. <br>Welcome :<br>•Rashida Richardson, Senior Policy Advisor for Data and Democracy, White House Office of Science andTechnology Policy<br>•Karen Kornbluh, Senior Fellow and Director of the Digital Innovation and Democracy Initiative, GermanMarshall Fund<br>Moderator :</code>                                                                        |
  | <code>What are some best practices that entities should follow to ensure privacy and security in automated systems?</code>                 | <code>Privacy-preserving security. Entities creating, using, or governing automated systems should follow privacy and security best practices designed to ensure data and metadata do not leak beyond the specific consented use case. Best practices could include using privacy-enhancing cryptography or other types of privacy-enhancing technologies or fine-grained permissions and access control mechanisms, along with conventional system security protocols. <br>33</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 |
|:------:|:----:|:--------------:|
| 0.6494 | 50   | 0.9436         |
| 1.0    | 77   | 0.9501         |
| 1.2987 | 100  | 0.9440         |
| 1.9481 | 150  | 0.9523         |
| 2.0    | 154  | 0.9488         |
| 2.5974 | 200  | 0.9549         |
| 3.0    | 231  | 0.9536         |
| 3.2468 | 250  | 0.9562         |
| 3.8961 | 300  | 0.9562         |
| 4.0    | 308  | 0.9562         |
| 4.5455 | 350  | 0.9562         |
| 5.0    | 385  | 0.9588         |


### 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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