Instructions to use hsila/Chembedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsila/Chembedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hsila/Chembedding", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hsila/Chembedding", trust_remote_code=True) model = AutoModel.from_pretrained("hsila/Chembedding", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98101ff6f9ebb2c02e3adeff37b92be0fcf52ce793a9aa4a4626444f48f32656
- Size of remote file:
- 547 MB
- SHA256:
- 5041083dd0039cf859ba2d44de2c3d44e6988ac0f30d0597ef27579b0b9068f7
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