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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4b914ec5ab295fbee3b0e0fe62f22febb3c09d34dd3778745e0de034a76df33
- Size of remote file:
- 4.31 MB
- SHA256:
- 13c8d666d62a7bc4ac8f040aab68e942c861f93303156cc28f5c7e885d86d6e3
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