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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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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-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/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: 0.9741235263046233 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# PubChem10m-deberta-base-output |
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This model is a fine-tuned version of [microsoft/deberta-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: 0.0698 |
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- Accuracy: 0.9741 |
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## Model description |
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We trained deberta-base on SMILES from PubChem using the task of masked-language modeling (MLM). Its tokenizer is a character-level tokenizer 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-05 |
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- train_batch_size: 30 |
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- eval_batch_size: 48 |
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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: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:| |
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| 0.0855 | 3.68 | 100000 | 0.0801 | 0.9708 | |
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| 0.0733 | 7.37 | 200000 | 0.0702 | 0.9740 | |
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### Framework versions |
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- Transformers 4.22.0.dev0 |
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- Pytorch 1.12.0 |
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- Datasets 2.4.1.dev0 |
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- Tokenizers 0.11.6 |
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