Feature Extraction
Transformers
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English
roberta
pharmacore
sparse
drug-discovery
apple-silicon
chemberta
molecular-language-model
cheminformatics
smiles
pruning
efficient-inference
Eval Results (legacy)
text-embeddings-inference
Instructions to use stephenjun8192/chemberta-zinc-sparse50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stephenjun8192/chemberta-zinc-sparse50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="stephenjun8192/chemberta-zinc-sparse50")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("stephenjun8192/chemberta-zinc-sparse50") model = AutoModel.from_pretrained("stephenjun8192/chemberta-zinc-sparse50") - Notebooks
- Google Colab
- Kaggle
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