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. The model is available in two sizes: Base and Large, and can be used for various NLP tasks such as masked language modeling and embedding generation.
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. The model is available in two sizes: Base and Large, and can be used for various NLP tasks such as masked language modeling and embedding generation.
text prompt, should include exactly one [MASK] token
You need to login to use this model
where is my father? (0.09)
where is my mother? (0.08)
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.
Model | Arch. | #Layers | #Params |
---|---|---|---|
neuralmind/bert-base-portuguese-cased | BERT-Base | 12 | 110M |
neuralmind/bert-large-portuguese-cased | BERT-Large | 24 | 335M |
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)
If you use our work, please cite:
@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}
}