Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
multilingual
Portuguese
bert
bert-base-multilingual-cased
semantic role labeling
finetuned
Instructions to use liaad/srl-pt_mbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liaad/srl-pt_mbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liaad/srl-pt_mbert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-pt_mbert-base") model = AutoModel.from_pretrained("liaad/srl-pt_mbert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from liaad/srl-pt_mbert-base: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/liaad/srl-pt_mbert-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://liaad/srl-pt_mbert-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/liaad/srl-pt_mbert-base/resolve/main/flax_model.msgpack
711 MB
- Xet hash:
- 1fd48b69e373a404998c3dc5a629d0ba6c9710a33d2987b79a1cb99577c4b799
- Size of remote file:
- 711 MB
- SHA256:
- bb795bd6187b7967e49eea6f8d87fd5cfbbc8ead1517fbf42ff4fe4d1caeb57f
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