Text Classification
Transformers
PyTorch
Safetensors
Spanish
bert
biomedical
clinical
spanish
BETO_Galen
Eval Results (legacy)
text-embeddings-inference
Instructions to use IIC/BETO_Galen-caresC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/BETO_Galen-caresC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/BETO_Galen-caresC")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/BETO_Galen-caresC") model = AutoModelForSequenceClassification.from_pretrained("IIC/BETO_Galen-caresC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from IIC/BETO_Galen-caresC: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/IIC/BETO_Galen-caresC/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://IIC/BETO_Galen-caresC/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/IIC/BETO_Galen-caresC/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- cf2c26a8d6cfc7cddc023a3443c8d891aea624b3e06d9b6ea1cd80f927d0616a
- Size of remote file:
- 440 MB
- SHA256:
- 5daa936c9f8267174c3b34cc7d4e7a80c2d131eecf15beb379e0fa4bb5e1ec89
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.