Token Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
biomedical
clinical
EHR
spanish
procedures
Eval Results (legacy)
Instructions to use BSC-NLP4BIA/bsc-bio-ehr-es-medprocner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-NLP4BIA/bsc-bio-ehr-es-medprocner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BSC-NLP4BIA/bsc-bio-ehr-es-medprocner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BSC-NLP4BIA/bsc-bio-ehr-es-medprocner") model = AutoModelForTokenClassification.from_pretrained("BSC-NLP4BIA/bsc-bio-ehr-es-medprocner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ce4eb0643676a8db944e54c1612450ceac3958e7a7c49b24b2792010fe4ab90
- Size of remote file:
- 496 MB
- SHA256:
- 1cdfca7fdee9fd2cc6f0877269b2e9e76b3c233799117d86c4f6b536d51af13d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.