Instructions to use garNER/bert-base-multilingual-cased-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use garNER/bert-base-multilingual-cased-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="garNER/bert-base-multilingual-cased-es")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("garNER/bert-base-multilingual-cased-es") model = AutoModelForTokenClassification.from_pretrained("garNER/bert-base-multilingual-cased-es") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 827b7b1d6ff07203adf2db92b67ce41a27bf13074e04d76d003145d8b7f987b3
- Size of remote file:
- 3.52 kB
- SHA256:
- 5e182b1b003d1064279ab486aea6c43fd421271afc0ca663040ffea408b6c321
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