Token Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
biomedical
clinical
EHR
spanish
humans
Eval Results (legacy)
Instructions to use BSC-NLP4BIA/bsc-bio-ehr-es-carmen-humano 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-humano 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-humano")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BSC-NLP4BIA/bsc-bio-ehr-es-carmen-humano") model = AutoModelForTokenClassification.from_pretrained("BSC-NLP4BIA/bsc-bio-ehr-es-carmen-humano", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de92d465d2ddb7e7506567d50617531408458273328acdd6190da70369a19a8d
- Size of remote file:
- 3.95 kB
- SHA256:
- 85bcca216160c2efc3ea1d5b91cca1cd27f24237ec4ac5dbc7922eccc4e3629c
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