Token Classification
Transformers
PyTorch
Safetensors
Spanish
deberta-v2
text-classification
biomedical
clinical
spanish
mdeberta-v3-base
Eval Results (legacy)
Instructions to use IIC/mdeberta-v3-base-ctebmsp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/mdeberta-v3-base-ctebmsp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/mdeberta-v3-base-ctebmsp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/mdeberta-v3-base-ctebmsp") model = AutoModelForSequenceClassification.from_pretrained("IIC/mdeberta-v3-base-ctebmsp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from IIC/mdeberta-v3-base-ctebmsp: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/IIC/mdeberta-v3-base-ctebmsp/resolve/main/tokenizer.json
- Command line
-
hf download hf://IIC/mdeberta-v3-base-ctebmsp/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/IIC/mdeberta-v3-base-ctebmsp/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 52d1937ebe62e4c35edda1a7537b33c71ffbd65a8694bb5fe5e09a29307869b9
- Size of remote file:
- 16.3 MB
- SHA256:
- e23095eb61ba944c7be3a5d3e8ec19e37ce7ced0daa03550bde03e83c21b3f8a
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