---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:34
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-large-en-v1.5
widget:
- source_sentence: Quais são as iniciativas do Seringal Lab?
  sentences:
  - O objetivo do Seringal Lab é atuar como um catalisador da transformação interna
    do Ministério Público do Acre, promovendo melhorias contínuas que otimizam o funcionamento
    da instituição e geram um impacto positivo direto para a sociedade.
  - O NAT é vinculado à Procuradoria-Geral de Justiça e presta apoio técnico especializado
    ao MPAC.
  - Algumas das iniciativas do Seringal Lab incluem a Anton.IA, o TranscreveAI e o
    Simplifica.
- source_sentence: Em que ano o NAT foi instituído?
  sentences:
  - O SIMBA é o Sistema de Investigação de Movimentação Bancária, gerenciado pelo
    NAT, para monitoramento de atividades financeiras suspeitas no Acre.
  - O NAT foi criado em 2012 pelo Ato n.º 25, visando oferecer apoio técnico-científico
    e de segurança institucional ao MPAC.
  - O NAT foi instituído no ano de 2012 como uma unidade de suporte técnico e segurança
    ao MPAC.
- source_sentence: Qual o impacto do NAT no combate ao crime organizado?
  sentences:
  - NAT é o Núcleo de Apoio Técnico do Ministério Público do Estado do Acre, criado
    para fornecer suporte especializado em inteligência, segurança institucional e
    operações técnico-científicas.
  - O NAT fortalece o combate ao crime organizado ao fornecer suporte técnico e científico
    ao GAECO e outros órgãos do MPAC.
  - O NAT foi criado para oferecer suporte especializado ao MPAC, garantindo apoio
    em áreas técnico-científicas e de segurança para facilitar as operações de investigação
    e combate ao crime.
- source_sentence: Quem regulamenta o NAT?
  sentences:
  - O escopo do NAT envolve oferecer apoio de inteligência, segurança institucional,
    e suporte técnico-científico ao MPAC, especialmente nas operações do GAECO.
  - NAT significa Núcleo de Apoio Técnico, uma unidade de suporte técnico e de segurança
    ao Ministério Público do Acre.
  - O NAT é regulamentado pelo Ministério Público do Estado do Acre e foi formalizado
    pela Lei Complementar n.º 291 de 2014.
- source_sentence: Qual a importância do NAT para o MPAC?
  sentences:
  - O TranscreveAI transforma áudios em textos de maneira automática e precisa, além
    de registrar o tempo exato do início e do fim de cada fala (timestamp).
  - O NAT é essencial para o MPAC, fornecendo apoio técnico especializado e segurança,
    fortalecendo as operações de investigação e combate ao crime.
  - A Lei Complementar n.º 291 de 2014 regulamentou o NAT como um órgão auxiliar do
    MPAC, fortalecendo seu papel de apoio técnico e científico.
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: MPAC BGE Large
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.7777777777777778
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8888888888888888
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8888888888888888
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8888888888888888
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7777777777777778
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2962962962962963
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17777777777777778
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08888888888888889
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7777777777777778
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8888888888888888
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8888888888888888
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8888888888888888
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8333333333333334
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8148148148148149
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8249158249158248
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7777777777777778
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8888888888888888
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8888888888888888
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7777777777777778
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2962962962962963
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17777777777777778
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7777777777777778
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8888888888888888
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8888888888888888
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8813288610261599
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.845679012345679
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.845679012345679
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7777777777777778
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8888888888888888
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8888888888888888
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7777777777777778
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2962962962962963
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17777777777777778
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7777777777777778
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8888888888888888
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8888888888888888
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.884918120767199
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8492063492063493
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8492063492063492
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.7777777777777778
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8888888888888888
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8888888888888888
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7777777777777778
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2962962962962963
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17777777777777778
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7777777777777778
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8888888888888888
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8888888888888888
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8813288610261599
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.845679012345679
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.845679012345679
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.7777777777777778
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8888888888888888
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8888888888888888
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7777777777777778
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2962962962962963
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17777777777777778
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7777777777777778
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8888888888888888
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8888888888888888
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.884918120767199
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8492063492063493
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8492063492063492
      name: Cosine Map@100
---

# MPAC BGE Large

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the json dataset. 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:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
- **Language:** en
- **License:** apache-2.0

### 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': True}) 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("mp-ac/mpac-bge-large-v1.2")
# Run inference
sentences = [
    'Qual a importância do NAT para o MPAC?',
    'O NAT é essencial para o MPAC, fornecendo apoio técnico especializado e segurança, fortalecendo as operações de investigação e combate ao crime.',
    'A Lei Complementar n.º 291 de 2014 regulamentou o NAT como um órgão auxiliar do MPAC, fortalecendo seu papel de apoio técnico e científico.',
]
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)

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</details>
-->

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1   | 0.7778     | 0.7778     | 0.7778     | 0.7778     | 0.7778     |
| cosine_accuracy@3   | 0.8889     | 0.8889     | 0.8889     | 0.8889     | 0.8889     |
| cosine_accuracy@5   | 0.8889     | 0.8889     | 0.8889     | 0.8889     | 0.8889     |
| cosine_accuracy@10  | 0.8889     | 1.0        | 1.0        | 1.0        | 1.0        |
| cosine_precision@1  | 0.7778     | 0.7778     | 0.7778     | 0.7778     | 0.7778     |
| cosine_precision@3  | 0.2963     | 0.2963     | 0.2963     | 0.2963     | 0.2963     |
| cosine_precision@5  | 0.1778     | 0.1778     | 0.1778     | 0.1778     | 0.1778     |
| cosine_precision@10 | 0.0889     | 0.1        | 0.1        | 0.1        | 0.1        |
| cosine_recall@1     | 0.7778     | 0.7778     | 0.7778     | 0.7778     | 0.7778     |
| cosine_recall@3     | 0.8889     | 0.8889     | 0.8889     | 0.8889     | 0.8889     |
| cosine_recall@5     | 0.8889     | 0.8889     | 0.8889     | 0.8889     | 0.8889     |
| cosine_recall@10    | 0.8889     | 1.0        | 1.0        | 1.0        | 1.0        |
| **cosine_ndcg@10**  | **0.8333** | **0.8813** | **0.8849** | **0.8813** | **0.8849** |
| cosine_mrr@10       | 0.8148     | 0.8457     | 0.8492     | 0.8457     | 0.8492     |
| cosine_map@100      | 0.8249     | 0.8457     | 0.8492     | 0.8457     | 0.8492     |

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## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 34 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 34 samples:
  |         | anchor                                                                            | positive                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 13.85 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 53.62 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
  | anchor                                         | positive                                                                                                                                                                                                                    |
  |:-----------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Qual é o objetivo do Simplifica?</code>  | <code>O objetivo do Simplifica é implementar e disseminar a Linguagem Simples no Ministério Público do Estado do Acre, tornando a comunicação institucional mais acessível, clara e objetiva para todos os cidadãos.</code> |
  | <code>Qual é a função do NAT no LAB-LD?</code> | <code>O NAT gerencia o LAB-LD, oferecendo suporte especializado em investigações financeiras para combater a lavagem de dinheiro.</code>                                                                                    |
  | <code>O que é o NAT?</code>                    | <code>O NAT, Núcleo de Apoio Técnico, é uma unidade do Ministério Público do Estado do Acre criada em 2012 para oferecer apoio técnico, científico e de segurança aos órgãos de execução do MPAC.</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`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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`: True
- `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_fused
- `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
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step  | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0     | 1     | 0.7368                 | 0.7368                 | 0.7222                 | 0.6686                 | 0.7222                |
| 2.0     | 2     | 0.8128                 | 0.7738                 | 0.7292                 | 0.7738                 | 0.7702                |
| 3.0     | 3     | 0.8256                 | 0.8258                 | 0.8542                 | 0.8800                 | 0.8591                |
| **4.0** | **4** | **0.8333**             | **0.8258**             | **0.8704**             | **0.8813**             | **0.8829**            |
| 5.0     | 5     | 0.8333                 | 0.8813                 | 0.8849                 | 0.8813                 | 0.8849                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.7
- Sentence Transformers: 3.3.1
- Transformers: 4.41.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.1.1
- Datasets: 3.1.0
- 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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