File size: 2,891 Bytes
2f84858 2800b54 2f84858 264e9ec 8eaf58c dd61a25 8eaf58c dd61a25 8eaf58c b7c43aa 8eaf58c b7c43aa 8eaf58c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
---
license: mit
language:
- pt
---
# bertimbau-large-ner
This model card aims to simplify the use of the [portuguese Bert, a.k.a, Bertimbau](https://github.com/neuralmind-ai/portuguese-bert) for the Named Entity Recognition task.
For this model card the we used the BERT-CRF (selective scenario, 5 classes) model available in the [ner_evalutaion](https://github.com/neuralmind-ai/portuguese-bert/tree/master/ner_evaluation) folder of the original Bertimbau repo.
## Usage
```
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("marquesafonso/bertimbau-large-ner")
model = AutoModelForTokenClassification.from_pretrained("marquesafonso/bertimbau-large-ner")
```
## Example
```
from transformers import pipeline
pipe = pipeline("ner", model="marquesafonso/bertimbau-large-ner", aggregation_strategy='simple')
sentence = "Acima de Ederson, abaixo de Rúben Dias. É entre os dois jogadores do Manchester City que se vai colocar Gonçalo Ramos no ranking de vendas mais avultadas do Benfica."
result = pipe([sentence])
print(f"{sentence}\n{result}")
# Acima de Ederson, abaixo de Rúben Dias. É entre os dois jogadores do Manchester City que se vai colocar Gonçalo Ramos no ranking de vendas mais avultadas do Benfica.
# [[
# {'entity_group': 'PESSOA', 'score': 0.99694395, 'word': 'Ederson', 'start': 9, 'end': 16},
# {'entity_group': 'PESSOA', 'score': 0.9918462, 'word': 'Rúben Dias', 'start': 28, 'end': 38},
# {'entity_group': 'ORGANIZACAO', 'score': 0.96376556, 'word': 'Manchester City', 'start': 69, 'end': 84},
# {'entity_group': 'PESSOA', 'score': 0.9993823, 'word': 'Gonçalo Ramos', 'start': 104, 'end': 117},
# {'entity_group': 'ORGANIZACAO', 'score': 0.9033079, 'word': 'Benfica', 'start': 157, 'end': 164}
# ]]
```
## Acknowledgements
This work is an adaptation of [portuguese Bert, a.k.a, Bertimbau](https://github.com/neuralmind-ai/portuguese-bert). You may check and/or cite their [work](http://arxiv.org/abs/1909.10649):
```
@InProceedings{souza2020bertimbau,
author="Souza, F{\'a}bio and Nogueira, Rodrigo and Lotufo, Roberto",
editor="Cerri, Ricardo and Prati, Ronaldo C.",
title="BERTimbau: Pretrained BERT Models for Brazilian Portuguese",
booktitle="Intelligent Systems",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="403--417",
isbn="978-3-030-61377-8"
}
@article{souza2019portuguese,
title={Portuguese Named Entity Recognition using BERT-CRF},
author={Souza, F{\'a}bio and Nogueira, Rodrigo and Lotufo, Roberto},
journal={arXiv preprint arXiv:1909.10649},
url={http://arxiv.org/abs/1909.10649},
year={2019}
}
```
Note that the authors - Fabio Capuano de Souza, Rodrigo Nogueira, Roberto de Alencar Lotufo - have used an MIT LICENSE for their work. |