Text Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
biomedical
clinical
spanish
bsc-bio-ehr-es
Eval Results (legacy)
text-embeddings-inference
Instructions to use IIC/bsc-bio-ehr-es-caresA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/bsc-bio-ehr-es-caresA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/bsc-bio-ehr-es-caresA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/bsc-bio-ehr-es-caresA") model = AutoModelForSequenceClassification.from_pretrained("IIC/bsc-bio-ehr-es-caresA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from IIC/bsc-bio-ehr-es-caresA: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/IIC/bsc-bio-ehr-es-caresA/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://IIC/bsc-bio-ehr-es-caresA/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/IIC/bsc-bio-ehr-es-caresA/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 9d0ddd94d7f0ff392d5357497b6dd80cb3d85e299d221bd0b1f6315181c8da9b
- Size of remote file:
- 499 MB
- SHA256:
- 2c57195bee2e4dbf67635ffac1263598ad27a68e7b3d421dc0c1c09e35276c2d
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