Instructions to use nepp1d0/Bert-pretrained-smilesBindingDB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nepp1d0/Bert-pretrained-smilesBindingDB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nepp1d0/Bert-pretrained-smilesBindingDB")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nepp1d0/Bert-pretrained-smilesBindingDB") model = AutoModelForMaskedLM.from_pretrained("nepp1d0/Bert-pretrained-smilesBindingDB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from nepp1d0/Bert-pretrained-smilesBindingDB: direct link, hf CLI and curl.
- Browser
- Download file 183 Bytes
-
https://huggingface.co/nepp1d0/Bert-pretrained-smilesBindingDB/resolve/main/tokenizer_config.json
- Command line
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hf download hf://nepp1d0/Bert-pretrained-smilesBindingDB/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/nepp1d0/Bert-pretrained-smilesBindingDB/resolve/main/tokenizer_config.json
183 Bytes
| {"model_max_length": 512, "unk_token": "[UNK]", "pad_token": "[PAD]", "cls_token": "[CLS]", "sep_token": "[SEP]", "mask_token": "[MASK]", "tokenizer_class": "PreTrainedTokenizerFast"} |