Instructions to use JuIm/BERT_SMILES_LARGE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuIm/BERT_SMILES_LARGE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JuIm/BERT_SMILES_LARGE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JuIm/BERT_SMILES_LARGE") model = AutoModelForMaskedLM.from_pretrained("JuIm/BERT_SMILES_LARGE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# BERT_SMILES_LARGE
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This model is a 83.5M parameter ROBERTA model fine tuned on a dataset of 1.1M SMILES (Simplified molecular-input line-entry system). This model builds on BERT_SMILES which was fine tuned on only 50k SMILES.
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## Intended uses & limitations
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# BERT_SMILES_LARGE
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This model is a 83.5M parameter ROBERTA model fine tuned on a dataset of 1.1M SMILES (Simplified molecular-input line-entry system) for masked language modeling (MLM). This model builds on BERT_SMILES which was fine tuned on only 50k SMILES.
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## Intended uses & limitations
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