Text Classification
Transformers
PyTorch
Safetensors
Spanish
bert
biomedical
clinical
spanish
BETO_Galen
Eval Results (legacy)
text-embeddings-inference
Instructions to use IIC/BETO_Galen-livingner3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/BETO_Galen-livingner3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/BETO_Galen-livingner3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/BETO_Galen-livingner3") model = AutoModelForSequenceClassification.from_pretrained("IIC/BETO_Galen-livingner3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02ba2dedb4d1a11cc7222d7977cb6a58dd3913a665abe4870582e21d9ccf1e39
- Size of remote file:
- 439 MB
- SHA256:
- 4b6d9d73f558a892903c5b164fa6d73a1c01bee0bf55b53502d87ba42b24c71c
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