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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:400
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: What is the title of the dataset introduced by Jin, B. Dhingra,
    Z. Liu, W. Cohen, and X. Lu in their 2019 publication?
  sentences:
  - TechQA [3]
  - 'Q. Jin, B. Dhingra, Z. Liu, W. Cohen, and X. Lu.



    PubMedQA: A dataset for biomedical research question answering.



    In K. Inui, J. Jiang, V. Ng, and X. Wan, editors, Proceedings of the 2019 Conference
    on Empirical Methods in Natural Language Processing and the 9th International
    Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2567–2577,
    Hong Kong, China, Nov. 2019. Association for Computational Linguistics.



    doi: 10.18653/v1/D19-1259.'
  - 'Our contributions address the need for standardized benchmarks and methodologies,
    enabling more precise and actionable insights into the strengths and weaknesses
    of different RAG systems. This, in turn, will facilitate iterative improvement
    of RAG models, driving forward the capabilities of retrieval-augmented generation
    in real-world applications.




    References



    Adlakha et al. [2023]


    V. Adlakha, P. BehnamGhader, X. H. Lu, N. Meade, and S. Reddy.'
- source_sentence: What does the 2024 paper by Es et al. propose regarding the evaluation
    of retrieval augmented generation?
  sentences:
  - 'doi: 10.18653/v1/2023.findings-acl.60.



    URL https://aclanthology.org/2023.findings-acl.60.





    Dinan et al. [2019]


    E. Dinan, S. Roller, K. Shuster, A. Fan, M. Auli, and J. Weston.



    Wizard of wikipedia: Knowledge-powered conversational agents, 2019.





    Es et al. [2024]


    S. Es, J. James, L. Espinosa Anke, and S. Schockaert.



    RAGAs: Automated evaluation of retrieval augmented generation.'
  - 'Source Domains


    RAGBench comprises five distinct domains: bio-medical research (PubmedQA, CovidQA),
    general knowledge (HotpotQA, MS Marco, HAGRID, ExperQA), legal contracts (CuAD),
    customer support (DelucionQA, EManual, TechQA), and finance (FinBench, TAT-QA).
    We select these specific domains based on availability of data, and applicability
    to real-world RAG applications across different industry verticals. For detailed
    descriptions of each component data source, refer to Appendix 7.2.'
  - 'The overall_supported_explanation field is a string explaining why the response

    *as a whole* is or is not supported by the documents. In this field, provide a

    step-by-step breakdown of the claims made in the response and the support (or

    lack thereof) for those claims in the documents. Begin by assessing each claim

    separately, one by one; don’t make any remarks about the response as a whole

    until you have assessed all the claims in isolation.'
- source_sentence: What are some common sources of questions in research or surveys?
  sentences:
  - 'Kwiatkowski et al. [2019]


    T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. Parikh, C. Alberti, D. Epstein,
    I. Polosukhin, M. Kelcey, J. Devlin, K. Lee, K. N. Toutanova, L. Jones, M.-W.
    Chang, A. Dai, J. Uszkoreit, Q. Le, and S. Petrov.



    Natural questions: a benchmark for question answering research.



    Transactions of the Association of Computational Linguistics, 2019.





    Laurer et al. [2022]


    M. Laurer, W. van Atteveldt, A. Casas, and K. Welbers.'
  - Question Sources
  - the overall RAG system performance, with the potential to provide granular, actionable
    insights to the RAG practitioner.
- source_sentence: What evaluation metrics are reported for the response-level hallucination
    detection task?
  sentences:
  - '4.3 Evaluation


    Our granular annotation schema allows for various evaluation setups. For example,
    we could evaluate either span-level or example/response-level predictions. For
    easy comparison with existing RAG evaluation approaches that are less granular,
    we report area under the receiver-operator curve (AUROC) on the response-level
    hallucination detection task, and root mean squared error (RMSE) for example-level
    context Relevance and Utilization predictions.'
  - EManual is a question answer dataset comprising consumer electronic device manuals
    and realistic questions about them composed by human annotators. The subset made
    available at the time of writing amounts to 659 unique questions about the Samsung
    Smart TV/remote and the accompanying user manual, segmented into 261 chunks. To
    form a RAG dataset, we embed the manual segments into a vector database with OpenAI
    embedding and retrieve up to 3 context documents per question from it. For each
  - 'Table 3: Benchmark evaluation on test splits. Reporting AUROC for predicting
    hallucinated responses (Hal), RMSE for predicting Context Relevance (Rel) and
    utilization (Util). ∗ indicates statistical significance at 95% confidence intervals,
    measured by bootstrap comparing the top and second-best results. RAGAS and Trulens
    do not evaluate Utilization.





    GPT-3.5

    RAGAS

    TruLens

    DeBERTA



    Dataset



    Hal↑↑\uparrow↑





    Rel↓↓\downarrow↓



    Util↓↓\downarrow↓




    Hal↑↑\uparrow↑





    Rel↓↓\downarrow↓'
- source_sentence: What is the main contribution of Kwiatkowski et al. [2019] in the
    field of question answering research?
  sentences:
  - 'The sentence_support_information field is a list of objects, one for each sentence

    in the response. Each object MUST have the following fields:

    - response_sentence_key: a string identifying the sentence in the response.

    This key is the same as the one used in the response above.

    - explanation: a string explaining why the sentence is or is not supported by
    the

    documents.

    - supporting_sentence_keys: keys (e.g. ’0a’) of sentences from the documents that'
  - 'Kwiatkowski et al. [2019]


    T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. Parikh, C. Alberti, D. Epstein,
    I. Polosukhin, M. Kelcey, J. Devlin, K. Lee, K. N. Toutanova, L. Jones, M.-W.
    Chang, A. Dai, J. Uszkoreit, Q. Le, and S. Petrov.



    Natural questions: a benchmark for question answering research.



    Transactions of the Association of Computational Linguistics, 2019.





    Laurer et al. [2022]


    M. Laurer, W. van Atteveldt, A. Casas, and K. Welbers.'
  - 'with consistent annotations. To best represent real-world RAG scenarios, we vary
    a number parameters to construct the benchmark: the source domain, number of context
    documents, context token length, and the response generator model Figure 1 illustrates
    where these variable parameters fall in the RAG pipeline.'
pipeline_tag: sentence-similarity
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
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: 0.8571428571428571
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9642857142857143
      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: 0.8571428571428571
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.32142857142857145
      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: 0.8571428571428571
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9642857142857143
      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: 0.9385586452838898
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9178571428571428
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9178571428571428
      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("chelleboyer/llm-evals-2-79b954ef-4798-4994-be72-a88d46b8ecca")
# Run inference
sentences = [
    'What is the main contribution of Kwiatkowski et al. [2019] in the field of question answering research?',
    'Kwiatkowski et\xa0al. [2019]\n\nT.\xa0Kwiatkowski, J.\xa0Palomaki, O.\xa0Redfield, M.\xa0Collins, A.\xa0Parikh, C.\xa0Alberti, D.\xa0Epstein, I.\xa0Polosukhin, M.\xa0Kelcey, J.\xa0Devlin, K.\xa0Lee, K.\xa0N. Toutanova, L.\xa0Jones, M.-W. Chang, A.\xa0Dai, J.\xa0Uszkoreit, Q.\xa0Le, and S.\xa0Petrov.\n\n\nNatural questions: a benchmark for question answering research.\n\n\nTransactions of the Association of Computational Linguistics, 2019.\n\n\n\n\nLaurer et\xa0al. [2022]\n\nM.\xa0Laurer, W.\xa0van Atteveldt, A.\xa0Casas, and K.\xa0Welbers.',
    'The sentence_support_information field is a list of objects, one for each sentence\nin the response. Each object MUST have the following fields:\n- response_sentence_key: a string identifying the sentence in the response.\nThis key is the same as the one used in the response above.\n- explanation: a string explaining why the sentence is or is not supported by the\ndocuments.\n- supporting_sentence_keys: keys (e.g. ’0a’) of sentences from the documents that',
]
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   | 0.8571     |
| cosine_accuracy@3   | 0.9643     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8571     |
| cosine_precision@3  | 0.3214     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8571     |
| cosine_recall@3     | 0.9643     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.9386** |
| cosine_mrr@10       | 0.9179     |
| cosine_map@100      | 0.9179     |

<!--
## 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: 400 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 400 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 21.42 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 93.8 tokens</li><li>max: 200 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                     | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  |:-------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What are the key components and criteria used in the TRACe Evaluation Framework within RAGBench?</code>                  | <code>RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>1 Introduction<br><br>2 Related Work<br><br>RAG evaluation<br>Finetuned RAG evaluation models<br><br><br><br>3 RAGBench Construction<br><br><br>3.1 Component Datasets<br><br>Source Domains<br>Context Token Length<br>Task Types<br>Question Sources<br>Response Generation<br>Data Splits<br><br><br><br>3.2 TRACe Evaluation Framework<br><br>Definitions<br>Context Relevance<br>Context Utilization<br>Completeness<br>Adherence<br><br><br>3.3 RAGBench Statistics<br><br>3.4 LLM annotator</code> |
  | <code>How does RAGBench utilize component datasets to construct a benchmark for Retrieval-Augmented Generation systems?</code> | <code>RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>1 Introduction<br><br>2 Related Work<br><br>RAG evaluation<br>Finetuned RAG evaluation models<br><br><br><br>3 RAGBench Construction<br><br><br>3.1 Component Datasets<br><br>Source Domains<br>Context Token Length<br>Task Types<br>Question Sources<br>Response Generation<br>Data Splits<br><br><br><br>3.2 TRACe Evaluation Framework<br><br>Definitions<br>Context Relevance<br>Context Utilization<br>Completeness<br>Adherence<br><br><br>3.3 RAGBench Statistics<br><br>3.4 LLM annotator</code> |
  | <code>What are the key components and findings discussed in the RAGBench Statistics and Case Study sections?</code>            | <code>3.3 RAGBench Statistics<br><br>3.4 LLM annotator<br><br>Alignment with Human Judgements<br><br><br>3.5 RAG Case Study<br><br><br><br>4 Experiments<br><br>4.1 LLM Judge<br>4.2 Fine-tuned Judge<br>4.3 Evaluation<br><br><br><br>5 Results<br><br>Estimating Context Relevance is Difficult<br><br><br>6 Conclusion<br><br>7 Appendix<br><br>7.1 RAGBench Code and Data<br><br>7.2 RAGBench Dataset Details<br><br>PubMedQA [14]<br>CovidQA-RAG<br>HotpotQA [42]<br>MS Marco [28]<br>CUAD [12]<br>DelucionQA [33]<br>EManual [27]<br>TechQA [3]<br>FinQA [6]<br>TAT-QA [47]<br>HAGRID [15]<br>ExpertQA [25]</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`: 5
- `per_device_eval_batch_size`: 5
- `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`: 5
- `per_device_eval_batch_size`: 5
- `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}
- `tp_size`: 0
- `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
- `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 | Training Loss | cosine_ndcg@10 |
|:-----:|:----:|:-------------:|:--------------:|
| 0.625 | 50   | -             | 0.9517         |
| 1.0   | 80   | -             | 0.9649         |
| 1.25  | 100  | -             | 0.9649         |
| 1.875 | 150  | -             | 0.9517         |
| 2.0   | 160  | -             | 0.9517         |
| 2.5   | 200  | -             | 0.9386         |
| 3.0   | 240  | -             | 0.9386         |
| 3.125 | 250  | -             | 0.9517         |
| 3.75  | 300  | -             | 0.9386         |
| 4.0   | 320  | -             | 0.9517         |
| 4.375 | 350  | -             | 0.9517         |
| 5.0   | 400  | -             | 0.9517         |
| 5.625 | 450  | -             | 0.9517         |
| 6.0   | 480  | -             | 0.9401         |
| 6.25  | 500  | 0.3877        | 0.9401         |
| 6.875 | 550  | -             | 0.9386         |
| 7.0   | 560  | -             | 0.9386         |
| 7.5   | 600  | -             | 0.9401         |
| 8.0   | 640  | -             | 0.9401         |
| 8.125 | 650  | -             | 0.9401         |
| 8.75  | 700  | -             | 0.9386         |
| 9.0   | 720  | -             | 0.9386         |
| 9.375 | 750  | -             | 0.9386         |
| 10.0  | 800  | -             | 0.9386         |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 2.14.4
- Tokenizers: 0.21.1

## 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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