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--- |
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license: mit |
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datasets: |
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- sagawa/ZINC-canonicalized |
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metrics: |
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- accuracy |
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model-index: |
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- name: ZINC-deberta |
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results: |
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- task: |
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name: Masked Language Modeling |
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type: fill-mask |
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dataset: |
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name: sagawa/ZINC-canonicalized |
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type: sagawa/ZINC-canonicalized |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9497212171554565 |
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--- |
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# CompoundT5 |
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This model is a re-pretrained version of [google/t5-v1_1-base](https://huggingface.co./microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1202 |
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- Accuracy: 0.9497 |
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## Model description |
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We trained t5 on SMILES from ZINC using masked-language modeling (MLM). Its tokenizer is also trained on ZINC. |
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## Intended uses & limitations |
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This model can be used to predict molecules' properties, reactions, or interactions with proteins by changing the way of finetuning. |
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As an example, We finetuned this model to predict products. The model is [here](https://huggingface.co./sagawa/ZINC-t5-productpredicition), and you can use the demo [here](https://huggingface.co./spaces/sagawa/predictproduct-t5). |
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Using its encoder, we trained a regression model to predict a reaction yield. You can use this demo [here](https://huggingface.co./spaces/sagawa/predictyield-t5). |
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## Training and evaluation data |
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We downloaded [ZINC data](https://drive.google.com/drive/folders/1lSPCqh31zxTVEhuiPde7W3rZG8kPgp-z) and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 22992522, and they were randomly split into train:validation=10:1. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-03 |
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- train_batch_size: 30 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 30.0 |
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### Training results |
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| Training Loss | Step | Accuracy | Validation Loss | |
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|:-------------:|:------:|:--------:|:---------------:| |
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| 0.2471 | 25000 | 0.9843 | 0.2226 | |
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| 0.1871 | 50000 | 0.9314 | 0.1783 | |
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| 0.1791 | 75000 | 0.9371 | 0.1619 | |
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| 0.1596 | 100000 | 0.9401 | 0.1520 | |
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| 0.1522 | 125000 | 0.9422 | 0.1449 | |
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| 0.1435 | 150000 | 0.9436 | 0.1404 | |
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| 0.1421 | 175000 | 0.9447 | 0.1368 | |
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| 0.1398 | 200000 | 0.9459 | 0.1322 | |
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| 0.1297 | 225000 | 0.9466 | 0.1299 | |
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| 0.1324 | 250000 | 0.9473 | 0.1268 | |
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| 0.1257 | 275000 | 0.9483 | 0.1244 | |
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| 0.1266 | 300000 | 0.9491 | 0.1216 | |
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| 0.1301 | 325000 | 0.9497 | 0.1204 | |
