Feature Extraction
Transformers
Safetensors
deberta-v2
chemistry
bioinformatics
drug-discovery
text-embeddings-inference
Instructions to use SaeedLab/MolDeBERTa-base-123M-contrastive_mtr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaeedLab/MolDeBERTa-base-123M-contrastive_mtr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/MolDeBERTa-base-123M-contrastive_mtr")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SaeedLab/MolDeBERTa-base-123M-contrastive_mtr") model = AutoModel.from_pretrained("SaeedLab/MolDeBERTa-base-123M-contrastive_mtr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b665d9bcff78209a7e7d9992a61168f5931eba458c18fc9cecec7683d9c3e06f
- Size of remote file:
- 353 MB
- SHA256:
- 7b68c8f11c35b0839dff46b1d67379f03e03ed2a981cd46c4603992af0e8f3b8
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