Feature Extraction
Transformers
Safetensors
deberta-v2
chemistry
bioinformatics
drug-discovery
text-embeddings-inference
Instructions to use SaeedLab/MolDeBERTa-base-123M-contrastive_mlc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaeedLab/MolDeBERTa-base-123M-contrastive_mlc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/MolDeBERTa-base-123M-contrastive_mlc")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SaeedLab/MolDeBERTa-base-123M-contrastive_mlc") model = AutoModel.from_pretrained("SaeedLab/MolDeBERTa-base-123M-contrastive_mlc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 510b1ae4dcc1b3b30731376be938d2debd4750afc1fe2516027ffd1f27f60bfd
- Size of remote file:
- 353 MB
- SHA256:
- 25328ed01e6dc9cee27270e5fff2a8b2a56148c057a1a73ca25b7b6770f96fc9
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