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-distemist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/mdeberta-v3-base-distemist with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/mdeberta-v3-base-distemist")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/mdeberta-v3-base-distemist") model = AutoModelForSequenceClassification.from_pretrained("IIC/mdeberta-v3-base-distemist") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 673a24fac8d17e21ed447ef2dbbc76e09f734b55e067a6da00d5204f045dc7d2
- Size of remote file:
- 1.12 GB
- SHA256:
- f50b45030ee7ad807cac2cea1bfe7340da81350c7b3a4d507f1dab3953cea4ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.