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-caresC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/mdeberta-v3-base-caresC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/mdeberta-v3-base-caresC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/mdeberta-v3-base-caresC") model = AutoModelForSequenceClassification.from_pretrained("IIC/mdeberta-v3-base-caresC") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1a59f830a11040497cc701987ef8a140404ee6ec8075d686060834ef0ac9a52
- Size of remote file:
- 1.12 GB
- SHA256:
- 00c71c859159d4ba3c31a26d0cd7d91af667353fc5d7d72c68dc3900078dd6c6
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