---
license: cc-by-nc-4.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- loss:CosineSimilarityLoss
base_model: neuralmind/bert-large-portuguese-cased
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_dot
- spearman_dot
- pearson_euclidean
- spearman_euclidean
- pearson_manhattan
- spearman_manhattan
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on neuralmind/bert-large-portuguese-cased
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev dot
      type: sts-dev-dot
    metrics:
    - type: pearson_dot
      value: 0.18794469022476173
      name: Pearson Dot
    - type: spearman_dot
      value: 0.17330642951334718
      name: Spearman Dot
    - type: pearson_dot
      value: 0.18794469022476173
      name: Pearson Dot
    - type: spearman_dot
      value: 0.17330642951334718
      name: Spearman Dot
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev euclidian
      type: sts-dev-euclidian
    metrics:
    - type: pearson_euclidean
      value: 0.6901184462009367
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6918422814802583
      name: Spearman Euclidean
    - type: pearson_euclidean
      value: 0.6901184462009367
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6918422814802583
      name: Spearman Euclidean
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev manhattan
      type: sts-dev-manhattan
    metrics:
    - type: pearson_manhattan
      value: 0.6894045916517573
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6915146514062644
      name: Spearman Manhattan
    - type: pearson_manhattan
      value: 0.6894045916517573
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6915146514062644
      name: Spearman Manhattan
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev cosine
      type: sts-dev-cosine
    metrics:
    - type: pearson_cosine
      value: 0.6697919427315735
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6927776866317362
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.6697919427315735
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6927776866317362
      name: Spearman Cosine
---

# SentenceTransformer based on neuralmind/bert-large-portuguese-cased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased). 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:** [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) <!-- at revision aa302f6ea73b759f7df9cad58bd272127b67ec28 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity


### 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, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
            "o autor possuía..., ",
            "a parte autora é servidor pública...",
            "a parte autora é..."
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.8019],
#         [1.0000, 1.0000, 0.8019],
#         [0.8019, 0.8019, 1.0000]])
```



## Evaluation

### Metrics

#### Semantic Similarity

* Dataset: `sts-dev-dot`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric           | Value      |
|:-----------------|:-----------|
| pearson_dot      | 0.1879     |
| **spearman_dot** | **0.1733** |

#### Semantic Similarity

* Dataset: `sts-dev-euclidian`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric                 | Value      |
|:-----------------------|:-----------|
| pearson_euclidean      | 0.6901     |
| **spearman_euclidean** | **0.6918** |

#### Semantic Similarity

* Dataset: `sts-dev-manhattan`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric                 | Value      |
|:-----------------------|:-----------|
| pearson_manhattan      | 0.6894     |
| **spearman_manhattan** | **0.6915** |

#### Semantic Similarity

* Dataset: `sts-dev-cosine`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6698     |
| **spearman_cosine** | **0.6928** |

#### Semantic Similarity

* Dataset: `sts-dev-dot`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric           | Value      |
|:-----------------|:-----------|
| pearson_dot      | 0.1879     |
| **spearman_dot** | **0.1733** |

#### Semantic Similarity

* Dataset: `sts-dev-euclidian`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric                 | Value      |
|:-----------------------|:-----------|
| pearson_euclidean      | 0.6901     |
| **spearman_euclidean** | **0.6918** |

#### Semantic Similarity

* Dataset: `sts-dev-manhattan`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric                 | Value      |
|:-----------------------|:-----------|
| pearson_manhattan      | 0.6894     |
| **spearman_manhattan** | **0.6915** |

#### Semantic Similarity

* Dataset: `sts-dev-cosine`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6698     |
| **spearman_cosine** | **0.6928** |

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## Training Details

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 1e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
- `resume_from_checkpoint`: True

#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 1e-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`: linear
- `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`: False
- `fp16`: True
- `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`: True
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>



### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 5.0.0
- Transformers: 4.53.3
- PyTorch: 2.7.1+cu126
- Accelerate: 1.9.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
- 
## Authors
*Diretoria de Inteligência Artificial, Ciência de Dados e Estatística do Tribunal de Justiça do Estado de Goiás (TJGO).*

## 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",
}
```

#### BERTIMBAU

```bibtex
@inproceedings{souza2020bertimbau,
  author    = {F{\'a}bio Souza and
               Rodrigo Nogueira and
               Roberto Lotufo},
  title     = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
  booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
  year      = {2020}
}
```
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