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---
language: pt
license: mit
tags:
- bert
- pytorch
datasets:
- brWaC
---
# BERTimbau Base (aka "bert-base-portuguese-cased")
## Introduction
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/).
## Available models
| Model | Arch. | #Layers | #Params |
| ---------------------------------------- | ---------- | ------- | ------- |
| `neuralmind/bert-base-portuguese-cased` | BERT-Base | 12 | 110M |
| `neuralmind/bert-large-portuguese-cased` | BERT-Large | 24 | 335M |
## Usage
```python
from transformers import AutoTokenizer # Or BertTokenizer
from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads
from transformers import AutoModel # or BertModel, for BERT without pretraining heads
model = AutoModelForPreTraining.from_pretrained('neuralmind/bert-base-portuguese-cased')
tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-base-portuguese-cased', do_lower_case=False)
```
### Masked language modeling prediction example
```python
from transformers import pipeline
pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer)
pipe('Tinha uma [MASK] no meio do caminho.')
# [{'score': 0.14287759363651276,
# 'sequence': '[CLS] Tinha uma pedra no meio do caminho. [SEP]',
# 'token': 5028,
# 'token_str': 'pedra'},
# {'score': 0.06213393807411194,
# 'sequence': '[CLS] Tinha uma árvore no meio do caminho. [SEP]',
# 'token': 7411,
# 'token_str': 'árvore'},
# {'score': 0.05515013635158539,
# 'sequence': '[CLS] Tinha uma estrada no meio do caminho. [SEP]',
# 'token': 5675,
# 'token_str': 'estrada'},
# {'score': 0.0299188531935215,
# 'sequence': '[CLS] Tinha uma casa no meio do caminho. [SEP]',
# 'token': 1105,
# 'token_str': 'casa'},
# {'score': 0.025660505518317223,
# 'sequence': '[CLS] Tinha uma cruz no meio do caminho. [SEP]',
# 'token': 3466,
# 'token_str': 'cruz'}]
```
### For BERT embeddings
```python
import torch
model = AutoModel.from_pretrained('neuralmind/bert-base-portuguese-cased')
input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt')
with torch.no_grad():
outs = model(input_ids)
encoded = outs[0][0, 1:-1] # Ignore [CLS] and [SEP] special tokens
# encoded.shape: (8, 768)
# tensor([[-0.0398, -0.3057, 0.2431, ..., -0.5420, 0.1857, -0.5775],
# [-0.2926, -0.1957, 0.7020, ..., -0.2843, 0.0530, -0.4304],
# [ 0.2463, -0.1467, 0.5496, ..., 0.3781, -0.2325, -0.5469],
# ...,
# [ 0.0662, 0.7817, 0.3486, ..., -0.4131, -0.2852, -0.2819],
# [ 0.0662, 0.2845, 0.1871, ..., -0.2542, -0.2933, -0.0661],
# [ 0.2761, -0.1657, 0.3288, ..., -0.2102, 0.0029, -0.2009]])
```
## Citation
If you use our work, please cite:
```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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