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--- |
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datasets: |
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- Phando/uspto-50k |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- chemistry |
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license: mit |
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--- |
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This [ChemBERTa-v2](https://huggingface.co/seyonec/ChemBERTa_zinc250k_v2_40k) checkpoint was fine-tuned on the [USPTO-50k](https://huggingface.co/datasets/Phando/uspto-50k) dataset for sequence classification. |
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Specifically, the objective is to predict the reaction class label, and the input is either (canonicalized) all reactant SMILES or all product SMILES (separated by "."). |
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- Train/Test split: 0.99/0.01 |
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- Evaluation results: |
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- Accuracy: 87.11% |
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- Loss: 0.4272 |
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- Fine-tuning hyperparameters: |
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- seed = 233 |
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- batch-size = 128 |
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- num_epochs = 5 (but early stopped at epoch 4) |
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- learning_rate = 5e-4 |
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- warmup_steps = 64 |
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- weight_decay = 0.01 |
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- lr_scheduler_type = "cosine" |
