Text Classification
Transformers
PyTorch
Safetensors
Spanish
deberta-v2
biomedical
clinical
spanish
mdeberta-v3-base
Eval Results (legacy)
text-embeddings-inference
Instructions to use IIC/mdeberta-v3-base-caresA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/mdeberta-v3-base-caresA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/mdeberta-v3-base-caresA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/mdeberta-v3-base-caresA") model = AutoModelForSequenceClassification.from_pretrained("IIC/mdeberta-v3-base-caresA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 482de8082e422ee6c4df64b9b60d8c86dae40c77acb720ccaf7ee5c2eb61adb0
- Size of remote file:
- 16.3 MB
- SHA256:
- c6b52ff7043b8e7c0712d94ffa3c8a9c9522538157a11f5b59dd9051105497b7
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