Token Classification
Transformers
PyTorch
Safetensors
Spanish
deberta-v2
text-classification
biomedical
clinical
spanish
mdeberta-v3-base
Eval Results (legacy)
Instructions to use IIC/mdeberta-v3-base-meddocan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/mdeberta-v3-base-meddocan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/mdeberta-v3-base-meddocan")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/mdeberta-v3-base-meddocan") model = AutoModelForSequenceClassification.from_pretrained("IIC/mdeberta-v3-base-meddocan") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67b1df565f69509140fd0701c4c6a765df27690f117eaf8ce6a184bb7c2ea2c8
- Size of remote file:
- 1.12 GB
- SHA256:
- 39f8eb382e98448154fe622886116b6b5244e10d5c48e0940b74e0e91af1babd
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