Token Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
text-classification
biomedical
clinical
spanish
bsc-bio-ehr-es
Eval Results (legacy)
Instructions to use IIC/bsc-bio-ehr-es-ehealth_kd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/bsc-bio-ehr-es-ehealth_kd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/bsc-bio-ehr-es-ehealth_kd")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/bsc-bio-ehr-es-ehealth_kd") model = AutoModelForSequenceClassification.from_pretrained("IIC/bsc-bio-ehr-es-ehealth_kd") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c13f3e03376b60c9c25be42d5c8d1349881bf6d2190b666745093d28d39aa622
- Size of remote file:
- 499 MB
- SHA256:
- 1a42621cdda86f47640bc9636de3c683e369b280c89f4e222b357d95cfebabe8
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