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-meddocan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/mdeberta-v3-base-meddocan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/mdeberta-v3-base-meddocan")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/mdeberta-v3-base-meddocan") model = AutoModelForSequenceClassification.from_pretrained("IIC/mdeberta-v3-base-meddocan", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |