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-ehealth_kd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/mdeberta-v3-base-ehealth_kd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/mdeberta-v3-base-ehealth_kd")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/mdeberta-v3-base-ehealth_kd") model = AutoModelForSequenceClassification.from_pretrained("IIC/mdeberta-v3-base-ehealth_kd") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 852bc9402547dc5d7fa24ccc557263fc810b88ecaf82fda0928664b48491c1a0
- Size of remote file:
- 1.12 GB
- SHA256:
- 1192a925901a12c009a808e3cbe479146f301052b09cfa14746a0b609856e150
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