Instructions to use marquesafonso/bertimbau-large-ner-selective with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marquesafonso/bertimbau-large-ner-selective with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="marquesafonso/bertimbau-large-ner-selective")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Parent(s): d204e31
fix highlight in dark mode
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README.md
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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.
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For this model card the we used the <mark style="background-color:
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Available classes are:
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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.
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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](https://github.com/neuralmind-ai/portuguese-bert/tree/master/ner_evaluation) folder of the original Bertimbau repo.
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Available classes are:
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