Feature Extraction
Transformers
Safetensors
Spanish
roberta
contrastive-learning
Spanish-UMLS
Hierarchical-enrichment
entity-linking
biomedical
spanish
text-embeddings-inference
Instructions to use ICB-UMA/HERBERT-P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICB-UMA/HERBERT-P with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ICB-UMA/HERBERT-P")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ICB-UMA/HERBERT-P") model = AutoModel.from_pretrained("ICB-UMA/HERBERT-P") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a7436b98073ddb369d61b3d19c1f35d48447d2383fffe8bd5bda83855cd9634
- Size of remote file:
- 504 MB
- SHA256:
- 616a5c437feb2a788e15ea62074c0ef2724f06ebd11f3afd2ab8577c9f0e1bad
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