sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:30 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-large-en-v1.5 base_model:finetune:BAAI/bge-large-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-large-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:30
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: O NAT foi criado em 13 de setembro de 2012 pelo Ato n.º 25 da Procuradoria-Geral
    de Justiça do MPAC.
  sentences:
  - Quando o NAT foi criado?
  - O que significa NAT?
  - Quem instituiu o NAT?
- source_sentence: 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.
  sentences:
  - O NAT é parte de qual organização?
  - Qual é a função do NAT no MPAC?
  - Como o NAT foi regulamentado pela Lei Complementar 291?
- source_sentence: O NAT é o Núcleo de Apoio Técnico do MPAC, criado para prestar
    apoio em inteligência, segurança e operações técnico-científicas aos órgãos de
    execução, especialmente ao GAECO.
  sentences:
  - Quem são os coordenadores do NAT?
  - Qual é a função do NAT no LAB-LD?
  - Me explique o que é o NAT no Ministério Público.
- source_sentence: O NAT é responsável por fornecer inteligência, suporte técnico-científico
    e segurança ao MPAC, além de gerenciar o SIMBA e o LAB-LD.
  sentences:
  - O que é o SIMBA, gerenciado pelo NAT?
  - Quais são as responsabilidades do NAT?
  - Para que foi criado o NAT?
- source_sentence: 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.
  sentences:
  - Explique o que é o NAT no MPAC.
  - O NAT trabalha com o GAECO?
  - O que significa NAT no Ministério Público?
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: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.75
      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: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.2
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.75
      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: 0.7544456402014998
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.675
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.675
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 1.0
      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: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.2
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 1.0
      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: 0.7827324383928644
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7083333333333333
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7083333333333333
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.75
      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: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.2
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.75
      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: 0.7544456402014998
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.675
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.675
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.75
      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: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.2
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.75
      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: 0.7326691395183482
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6458333333333333
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6458333333333333
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5
      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: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.2
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.1
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5
      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: 0.7043823413269836
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6125
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6125
      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("mpac/mpac-bge-large")
# Run inference
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 que significa NAT no Ministério Público?',
    'O NAT trabalha com o GAECO?',
]
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]
```

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</details>
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You can finetune this model on your own dataset.

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

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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   | 0.5        | 0.5        | 0.5        | 0.5        | 0.5        |
| cosine_accuracy@3   | 0.75       | 1.0        | 0.75       | 0.75       | 0.5        |
| cosine_accuracy@5   | 1.0        | 1.0        | 1.0        | 1.0        | 1.0        |
| cosine_accuracy@10  | 1.0        | 1.0        | 1.0        | 1.0        | 1.0        |
| cosine_precision@1  | 0.5        | 0.5        | 0.5        | 0.5        | 0.5        |
| cosine_precision@3  | 0.25       | 0.3333     | 0.25       | 0.25       | 0.1667     |
| cosine_precision@5  | 0.2        | 0.2        | 0.2        | 0.2        | 0.2        |
| cosine_precision@10 | 0.1        | 0.1        | 0.1        | 0.1        | 0.1        |
| cosine_recall@1     | 0.5        | 0.5        | 0.5        | 0.5        | 0.5        |
| cosine_recall@3     | 0.75       | 1.0        | 0.75       | 0.75       | 0.5        |
| cosine_recall@5     | 1.0        | 1.0        | 1.0        | 1.0        | 1.0        |
| cosine_recall@10    | 1.0        | 1.0        | 1.0        | 1.0        | 1.0        |
| **cosine_ndcg@10**  | **0.7544** | **0.7827** | **0.7544** | **0.7327** | **0.7044** |
| cosine_mrr@10       | 0.675      | 0.7083     | 0.675      | 0.6458     | 0.6125     |
| cosine_map@100      | 0.675      | 0.7083     | 0.675      | 0.6458     | 0.6125     |

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 30 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 30 samples:
  |         | positive                                                                          | anchor                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           |
  | details | <ul><li>min: 38 tokens</li><li>mean: 54.3 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 13.5 tokens</li><li>max: 18 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                  | anchor                                         |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------|
  | <code>O NAT foi criado em 13 de setembro de 2012 pelo Ato n.º 25 da Procuradoria-Geral de Justiça do MPAC.</code>                                                         | <code>Quando o NAT foi criado?</code>          |
  | <code>O NAT é vinculado à Procuradoria-Geral de Justiça e presta apoio técnico especializado ao MPAC.</code>                                                              | <code>O NAT é vinculado a qual órgão?</code>   |
  | <code>Os coordenadores do Núcleo de Apoio Técnico (NAT) são Marcela Cristina Ozório, como Coordenadora Geral e Bernardo Fiterman Albano, como Coordenador Adjunto </code> | <code>Quem são os coordenadores do NAT?</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`: 4
- `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`: 4
- `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     | 0.75                   | 0.75                   | 0.75                   | 0.75                   | 0.75                  |
| **2.0** | **2** | **0.7468**             | **0.7468**             | **0.6967**             | **0.7654**             | **0.6973**            |
| 3.0     | 3     | 0.7544                 | 0.7654                 | 0.7544                 | 0.7327                 | 0.6967                |
| 4.0     | 4     | 0.7544                 | 0.7827                 | 0.7544                 | 0.7327                 | 0.7044                |

* 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: 1.1.0
- Datasets: 2.19.1
- 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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