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-ehealth_kd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/bsc-bio-ehr-es-ehealth_kd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/bsc-bio-ehr-es-ehealth_kd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/bsc-bio-ehr-es-ehealth_kd") model = AutoModelForSequenceClassification.from_pretrained("IIC/bsc-bio-ehr-es-ehealth_kd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download merges.txt from IIC/bsc-bio-ehr-es-ehealth_kd: direct link, hf CLI and curl.
- Browser
- Download file 521 kB
-
https://huggingface.co/IIC/bsc-bio-ehr-es-ehealth_kd/resolve/main/merges.txt
- Command line
-
hf download hf://IIC/bsc-bio-ehr-es-ehealth_kd/merges.txt
-
curl -L -o merges.txt https://huggingface.co/IIC/bsc-bio-ehr-es-ehealth_kd/resolve/main/merges.txt
521 kB
File too large to display, you can check the raw version instead.