Feature Extraction
Transformers
Safetensors
deberta-v2
chemistry
bioinformatics
drug-discovery
text-embeddings-inference
Instructions to use SaeedLab/MolDeBERTa-small-10M-contrastive_mlc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaeedLab/MolDeBERTa-small-10M-contrastive_mlc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/MolDeBERTa-small-10M-contrastive_mlc")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SaeedLab/MolDeBERTa-small-10M-contrastive_mlc") model = AutoModel.from_pretrained("SaeedLab/MolDeBERTa-small-10M-contrastive_mlc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06dda59fe8be284041b72c44562134fc36f6cd07c048f6c860c5974446f935f5
- Size of remote file:
- 84.1 MB
- SHA256:
- 7d8d3de0117118da219fab85fa99686f5f1da7bcc2a096edb9be03dc10c473e0
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