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--- |
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license: mit |
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datasets: |
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- sagawa/pubchem-10m-canonicalized |
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metrics: |
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- accuracy |
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model-index: |
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- name: PubChem-10m-t5 |
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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/pubchem-10m-canonicalized |
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type: sagawa/pubchem-10m-canonicalized |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9259435534477234 |
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--- |
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# PubChem-10m-t5 |
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This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/pubchem-10m-canonicalized dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2121 |
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- Accuracy: 0.9259 |
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## Model description |
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We trained t5 on SMILES from PubChem using the task of masked-language modeling (MLM). Its tokenizer is also trained on PubChem. |
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## Intended uses & limitations |
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This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning. |
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## Training and evaluation data |
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We downloaded [PubChem data](https://drive.google.com/file/d/1ygYs8dy1-vxD1Vx6Ux7ftrXwZctFjpV3/view) and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 9999960, 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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| 0.3866 | 25000 | 0.8830 | 0.3631 | |
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| 0.3352 | 50000 | 0.8996 | 0.3049 | |
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| 0.2834 | 75000 | 0.9057 | 0.2825 | |
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| 0.2685 | 100000 | 0.9099 | 0.2675 | |
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| 0.2591 | 125000 | 0.9124 | 0.2587 | |
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| 0.2620 | 150000 | 0.9144 | 0.2512 | |
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| 0.2806 | 175000 | 0.9161 | 0.2454 | |
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| 0.2468 | 200000 | 0.9179 | 0.2396 | |
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| 0.2669 | 225000 | 0.9194 | 0.2343 | |
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| 0.2611 | 250000 | 0.9210 | 0.2283 | |
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| 0.2346 | 275000 | 0.9226 | 0.2230 | |
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| 0.1972 | 300000 | 0.9238 | 0.2191 | |
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| 0.2344 | 325000 | 0.9250 | 0.2152 | |
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| 0.2164 | 350000 | 0.9259 | 0.2121 | |
