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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a1c5ad267aff67b3ee8bd807e729306305644ff6c970e85420d7e43d7e31916
- Size of remote file:
- 709 MB
- SHA256:
- 5ffc868b0938e28ee2b9be683b8aef9afeb6d2d86eaa54f47e3d7cf3e58a4af6
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