| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| datasets: |
| - sagawa/ZINC-canonicalized |
| metrics: |
| - accuracy |
| base_model: microsoft/deberta-base |
| model-index: |
| - name: ZINC-deberta |
| results: |
| - task: |
| type: fill-mask |
| name: Masked Language Modeling |
| dataset: |
| name: sagawa/ZINC-canonicalized |
| type: sagawa/ZINC-canonicalized |
| metrics: |
| - type: accuracy |
| value: 0.9900059572833486 |
| name: Accuracy |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # ZINC-deberta-base-output |
|
|
| This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0237 |
| - Accuracy: 0.9900 |
|
|
| ## Model description |
|
|
| We trained deberta-base on SMILES from ZINC using the task of masked-language modeling (MLM). Its tokenizer is a character-level tokenizer trained on ZINC. |
|
|
| ## Intended uses & limitations |
|
|
| This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning. |
|
|
| ## Training and evaluation data |
|
|
| We downloaded [ZINC data](https://drive.google.com/drive/folders/1lSPCqh31zxTVEhuiPde7W3rZG8kPgp-z) and canonicalized them using RDKit. Then, we droped duplicates. The total number of data is 22992522, and they were randomly split into train:validation=10:1. |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 20 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10.0 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Accuracy | Validation Loss | |
| |:-------------:|:-----:|:------:|:--------:|:---------------:| |
| | 0.045 | 1.06 | 100000 | 0.9842 | 0.0409 | |
| | 0.0372 | 2.13 | 200000 | 0.9864 | 0.0346 | |
| | 0.0337 | 3.19 | 300000 | 0.9874 | 0.0314 | |
| | 0.0318 | 4.25 | 400000 | 0.9882 | 0.0293 | |
| | 0.0296 | 5.31 | 500000 | 0.0277 | 0.9887 | |
| | 0.0289 | 6.38 | 600000 | 0.0264 | 0.9891 | |
| | 0.0267 | 7.44 | 700000 | 0.0253 | 0.9894 | |
| | 0.0261 | 8.5 | 800000 | 0.0243 | 0.9898 | |
| | 0.025 | 9.57 | 900000 | 0.0238 | 0.9900 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.22.0.dev0 |
| - Pytorch 1.12.0 |
| - Datasets 2.4.1.dev0 |
| - Tokenizers 0.11.6 |
|
|
