Instructions to use jaggernaut007/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaggernaut007/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jaggernaut007/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jaggernaut007/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("jaggernaut007/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d98ca2bc89dce6e2190de48474e3ea4d624b80592c7e90b858755c63cee75de
- Size of remote file:
- 502 MB
- SHA256:
- 4915eb4775a69b26a468be7bbcb3595df5aa99865e9d968acfa35a22fcb4efab
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