Feature Extraction
Transformers
Safetensors
Spanish
roberta
contrastive-learning
Spanish-UMLS
Hierarchical-enrichment
text-embeddings-inference
Instructions to use ICB-UMA/HERBERT-GP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICB-UMA/HERBERT-GP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ICB-UMA/HERBERT-GP")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ICB-UMA/HERBERT-GP") model = AutoModel.from_pretrained("ICB-UMA/HERBERT-GP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ICB-UMA/HERBERT-GP: direct link, hf CLI and curl.
- Browser
- Download file 3.82 MB
-
https://huggingface.co/ICB-UMA/HERBERT-GP/resolve/main/tokenizer.json
- Command line
-
hf download hf://ICB-UMA/HERBERT-GP/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ICB-UMA/HERBERT-GP/resolve/main/tokenizer.json
3.82 MB
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