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
File size: 125 Bytes
dbb97c1 | 1 2 3 4 5 6 7 8 | {
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": "[UNK]"
}
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