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marquesafonso/bertimbau-large-ner-selective
bertimbau-large-ner-selective is a token classification model from marquesafonso. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
This model card aims to simplify the use of the portuguese Bert, a.k.a, Bertimbau for the Named Entity Recognition task.
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From the Hugging Face model README
This model card aims to simplify the use of the portuguese Bert, a.k.a, Bertimbau for the Named Entity Recognition task.
For this model card the we used the <mark style="background-color: grey"> BERT-CRF (selective scenario, 5 classes) </mark> model available in the ner_evaluation folder of the original Bertimbau repo.
Available classes are:
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("marquesafonso/bertimbau-large-ner-selective")
model = AutoModelForTokenClassification.from_pretrained("marquesafonso/bertimbau-large-ner-selective")
from transformers import pipeline
pipe = pipeline("ner", model="marquesafonso/bertimbau-large-ner-selective", 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}
# ]]
This work is an adaptation of portuguese Bert, a.k.a, Bertimbau. You may check and/or cite their work:
@article{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.