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
Add extra file special_tokens_map.json
Browse files- special_tokens_map.json +7 -0
special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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