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GitHub - deepforestsci chemberta3: ChemBERTa-3 Repo Table 3 compares ChemBERTa and MoLFormer models pretrained on ZINC and PubChem datasets of varying sizes on various classification datasets and reports ROC AUC scores (Higher is better)
22_Transfer_Learning_With_ChemBERTa_Transformers. ipynb - Colab This package enables us to use 16-bit training, mixed precision, and distributed training without any changes to our code Generally GPUs are good at doing 32-bit (single precision) math, not at
DeepChem This package enables us to use 16-bit training, mixed precision, and distributed training without any changes to our code Generally GPUs are good at doing 32-bit (single precision) math, not at 16-bit (half) nor 64-bit (double precision)
ChemBERTa chemberta train train_roberta. py at master - GitHub """ Script for training a Roberta Model (mlm or regression) Usage [mlm]: python train_roberta py --model_type=mlm --dataset_path=<DATASET_PATH> --mlm_probability=<MLM_MASKING_PROBABILITY> --output_dir=<OUTPUT_DIR> --run_name=<RUN_NAME> Usage [regression]: python train_roberta py --model_type=regression --dataset_path=<DATASET_PATH> -
ChemBERTa-77M-MTR The model utilizes a 77 million parameter architecture optimized for chemical data processing It implements masked token regression (MTR) as its primary training objective, differentiating it from traditional masked language modeling approaches
ChemBERTa-3: An Open Source Training Framework for Chemical Foundation . . . In this paper, we introduce ChemBERTa-3, an open-source training framework designed to train and fine-tune large-scale chemical foundation models We explore the potential of multiple model architectures by evaluating their performance across various molecular datasets from the MoleculeNet suite