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:
- 668475ce79f36d5b515bb936e482b9d021bff481271f4bb9e8b289f8199daed0
- Size of remote file:
- 1.12 GB
- SHA256:
- 348c05f6279b071378e9ccc8afd741d1192194f3c88f7fe877ab1e079cbb35e5
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