Token Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
biomedical
clinical
EHR
spanish
procedures
Eval Results (legacy)
Instructions to use BSC-NLP4BIA/bsc-bio-ehr-es-medprocner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-NLP4BIA/bsc-bio-ehr-es-medprocner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BSC-NLP4BIA/bsc-bio-ehr-es-medprocner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BSC-NLP4BIA/bsc-bio-ehr-es-medprocner") model = AutoModelForTokenClassification.from_pretrained("BSC-NLP4BIA/bsc-bio-ehr-es-medprocner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ecd3ecd23fe1ed1f509529ba05402816c008d6ffab63163fb748864dbd6a561
- Size of remote file:
- 3.89 kB
- SHA256:
- 0496a7c07b2057de81f4e079ae96a53e5fbf3ecedee3039a188be090848a7b4b
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