Feature Extraction
Transformers
Safetensors
deberta-v2
chemistry
bioinformatics
drug-discovery
text-embeddings-inference
Instructions to use SaeedLab/MolDeBERTa-small-123M-contrastive_mlc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaeedLab/MolDeBERTa-small-123M-contrastive_mlc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/MolDeBERTa-small-123M-contrastive_mlc")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SaeedLab/MolDeBERTa-small-123M-contrastive_mlc") model = AutoModel.from_pretrained("SaeedLab/MolDeBERTa-small-123M-contrastive_mlc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87cb7a1876964dc8cfa30246b8ccd592ed3813cf262738c3900e1a8101abfae8
- Size of remote file:
- 84.1 MB
- SHA256:
- e0ea2ed1b95e9b3f84d46c61714844018df32107e9bf98ed2b51c8686c98dfd7
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