---
language: 
  - pt
thumbnail: "Portugues SBERT for the Legal Domain"
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- transformers
datasets:
- assin
- assin2
- stsb_multi_mt

widget:
- source_sentence: "O advogado apresentou as provas ao juíz."
  sentences:
    - "O juíz leu as provas."
    - "O juíz leu o recurso."
    - "O juíz atirou uma pedra."
  example_title: "Example 1"
model-index:
- name: SBERTimbau
  results:
  - task:
      name: STS
      type: STS
    metrics:
      - name: PearsonCorrelation
        type: PearsonCorrelation
        value: 0.84
---

# rufimelo/Legal-SBERTimbau-sts-large-ma

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/Legal-SBERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) alrge.
It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

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

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]

model = SentenceTransformer('rufimelo/Legal-SBERTimbau-sts-large-ma-v3')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)


```python
from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-SBERTimbau-sts-large-ma-v3')
model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-sts-large-ma-v3')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```


## Evaluation Results STS

| Model| Assin | Assin2|stsb_multi_mt pt|
| ---------------------------------------- | ---------- | ---------- |---------- |
| Legal-SBERTimbau-sts-base| 0.71457| 0.73545 | |
| Legal-SBERTimbau-sts-base-ma| 0.74874 | 0.79532|0.82254 |
| Legal-SBERTimbau-sts-base-ma-v2| 0.75481 | 0.80262|0.82178|
| Legal-SBERTimbau-sts-large| 0.76629| 0.82357 | |
| Legal-SBERTimbau-sts-large-v2| 0.76299 | 0.81121|0.81726 |
| Legal-SBERTimbau-sts-large-ma| 0.76195| 0.81622 | 0.82608|
| Legal-SBERTimbau-sts-large-ma-v2| 0.7836| 0.8462| 0.8261| 
| Legal-SBERTimbau-sts-large-ma-v3| 0.7749| 0.8470| 0.8364| 
| ---------------------------------------- | ---------- |---------- |---------- |
| BERTimbau base Fine-tuned for STS|0.78455 | 0.80626|0.82841|
| BERTimbau large Fine-tuned for STS|0.78193 | 0.81758|0.83784|
| ---------------------------------------- | ---------- |---------- |---------- |
| paraphrase-multilingual-mpnet-base-v2| 0.71457| 0.79831 |0.83999 |
| paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831   |0.84575 |
## Training

rufimelo/Legal-SBERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.

Firstly, due to the lack of portuguese datasets, it was trained using multilingual knowledge distillation. For the Multilingual Knowledge Distillation process, the teacher model was 'sentence-transformers/stsb-roberta-large', the supposed supported language as English and the language to learn was portuguese.

It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2) and [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt) datasets. (batch 8, 5 epochs 'lr': 1e-5)


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)
```

## Citing & Authors

If you use this work, please cite BERTimbau's work:

```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}
}
```