Token Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
biomedical
clinical
EHR
spanish
procedures
Eval Results (legacy)
Instructions to use BSC-NLP4BIA/bsc-bio-ehr-es-carmen-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-carmen-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-carmen-medprocner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BSC-NLP4BIA/bsc-bio-ehr-es-carmen-medprocner") model = AutoModelForTokenClassification.from_pretrained("BSC-NLP4BIA/bsc-bio-ehr-es-carmen-medprocner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f6d3a8eae16694d03d9e26e48330dec7c4960b3e83dec2f1868bcd2afcc94ef
- Size of remote file:
- 496 MB
- SHA256:
- afef8838bfb25b789f0d9aa17c8ae7e8ece5b8b9c3a5467b8119e2125b9f3aaf
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