Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
multilingual
Portuguese
bert
bert-base-portuguese-cased
semantic role labeling
finetuned
Instructions to use liaad/srl-pt_bertimbau-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liaad/srl-pt_bertimbau-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liaad/srl-pt_bertimbau-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-pt_bertimbau-base") model = AutoModel.from_pretrained("liaad/srl-pt_bertimbau-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43d9db13edd79adaf2a1d0a30879cb33282db8a4dd713c8a5d47ec6621927493
- Size of remote file:
- 436 MB
- SHA256:
- 02281463bab5033778c7abc783dd2500fef30c14b9e0dda822fc57d1926b9c04
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