Fill-Mask
Transformers
PyTorch
Safetensors
code
bert
chemistry
selfies
drug-discovery
herbal
coconutdb
chembl34
drugs
molecules
compounds
ranger21
madgrad
Eval Results (legacy)
Instructions to use gbyuvd/chemselfies-base-bertmlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gbyuvd/chemselfies-base-bertmlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gbyuvd/chemselfies-base-bertmlm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gbyuvd/chemselfies-base-bertmlm") model = AutoModelForMaskedLM.from_pretrained("gbyuvd/chemselfies-base-bertmlm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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**5. View Augmentation**
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- Each SELFIES string is processed three times, once with each masking strategy.
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- These three repeats are views of the same training example, not additional data; they act as inexpensive regularisation but do not increase the effective sample size or alter the underlying molecular distribution. Dataset size is therefore reported as the number of unique molecules.
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**6. Masking Process**
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**5. View Augmentation**
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- Each SELFIES string is processed three times, once with each masking strategy.
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- _Note:_ These three repeats are views of the same training example, not additional data; they act as inexpensive regularisation but do not increase the effective sample size or alter the underlying molecular distribution. Dataset size is therefore reported as the number of unique molecules.
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**6. Masking Process**
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