Text Classification
Transformers
PyTorch
Safetensors
Spanish
deberta-v2
biomedical
clinical
eHR
spanish
mdeberta-v3-base
Eval Results (legacy)
text-embeddings-inference
Instructions to use IIC/mdeberta-v3-base-cantemist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/mdeberta-v3-base-cantemist with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/mdeberta-v3-base-cantemist")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/mdeberta-v3-base-cantemist") model = AutoModelForSequenceClassification.from_pretrained("IIC/mdeberta-v3-base-cantemist", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from IIC/mdeberta-v3-base-cantemist: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/IIC/mdeberta-v3-base-cantemist/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://IIC/mdeberta-v3-base-cantemist/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/IIC/mdeberta-v3-base-cantemist/resolve/main/pytorch_model.bin
1.12 GB
- Xet hash:
- 6e4ed7646ab614fcf95b98676f97e03badb87e1ae748d3aac1791f702ef91e52
- Size of remote file:
- 1.12 GB
- SHA256:
- 9289c14857b4334cc80906c65b2bc3ffc0b19f343b11322161c488e62e4aecfa
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