Token Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
text-classification
biomedical
clinical
spanish
bsc-bio-ehr-es
Eval Results (legacy)
Instructions to use IIC/bsc-bio-ehr-es-ctebmsp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/bsc-bio-ehr-es-ctebmsp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/bsc-bio-ehr-es-ctebmsp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/bsc-bio-ehr-es-ctebmsp") model = AutoModelForSequenceClassification.from_pretrained("IIC/bsc-bio-ehr-es-ctebmsp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14ad4c32589eec7df7ce3028a8dda2071ef0c08efd3c84ddc9899618952edc8a
- Size of remote file:
- 499 MB
- SHA256:
- 7be96ce549ccb02fc92b82bb6a84a48bb21e4f38fd72979fea30f9008c80257e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.