---
language: 
  - pt
thumbnail: "Portugues BERT for the Legal Domain"
tags:
  - bert
  - pytorch
datasets:
 - rufimelo/PortugueseLegalSentences-v0
license: "mit"
widget:
 - text: "O advogado apresentou [MASK] ao juíz."
---

# Legal_BERTimbau

## Introduction

Legal_BERTimbau Large is a fine-tuned BERT model based on [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.

"BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. It is available in two sizes: Base and Large.

For further information or requests, please go to [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/)."

The performance of Language Models can change drastically when there is a domain shift between training and test data. In order create a Portuguese Language Model adapted to a Legal domain, the original BERTimbau model was submitted to a fine-tuning stage where it was performed 1 "PreTraining" epoch over 30 000 legal Portuguese Legal documents available online.


## Available models

| Model                                    | Arch.      | #Layers | #Params |
| ---------------------------------------- | ---------- | ------- | ------- |
| `rufimelo/Legal-BERTimbau-base`  | BERT-Base	|12	|110M|
| `rufimelo/Legal-BERTimbau-large` | BERT-Large | 24      | 335M    |

## Usage

```python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-base")

model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-base")
```

### Masked language modeling prediction example

```python
from  transformers  import  pipeline
from  transformers  import  AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-base")
model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-base")

pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer)
pipe('O advogado apresentou [MASK] para o juíz')
# [{'score': 0.5034703612327576, 
#'token': 8190, 
#'token_str': 'recurso', 
#'sequence': 'O advogado apresentou recurso para o juíz'}, 
#{'score': 0.07347951829433441, 
#'token': 21973, 
#'token_str': 'petição', 
#'sequence': 'O advogado apresentou petição para o juíz'}, 
#{'score': 0.05165359005331993, 
#'token': 4299, 
#'token_str': 'resposta', 
#'sequence': 'O advogado apresentou resposta para o juíz'}, 
#{'score': 0.04611917585134506,
#'token': 5265, 
#'token_str': 'exposição', 
#'sequence': 'O advogado apresentou exposição para o juíz'}, 
#{'score': 0.04068068787455559, 
#'token': 19737, 'token_str': 
#'alegações', 
#'sequence': 'O advogado apresentou alegações para o juíz'}]

```

### For BERT embeddings

```python
import  torch
from  transformers  import  AutoModel

model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-base')
input_ids = tokenizer.encode('O advogado apresentou recurso para o juíz', return_tensors='pt')

with  torch.no_grad():
	outs = model(input_ids)
	encoded = outs[0][0, 1:-1]
	
#tensor([[ 0.0328, -0.4292, -0.6230, ..., -0.3048, -0.5674, 0.0157], 
#[-0.3569, 0.3326, 0.7013, ..., -0.7778, 0.2646, 1.1310], 
#[ 0.3169, 0.4333, 0.2026, ..., 1.0517, -0.1951, 0.7050], 
#..., 
#[-0.3648, -0.8137, -0.4764, ..., -0.2725, -0.4879, 0.6264], 
#[-0.2264, -0.1821, -0.3011, ..., -0.5428, 0.1429, 0.0509], 
#[-1.4617, 0.6281, -0.0625, ..., -1.2774, -0.4491, 0.3131]])
```

## Citation

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}
}
```