readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - sagawa/ZINC-canonicalized
  metrics:
  - accuracy
  model-index:
  - name: ZINC-deberta
    results:
    - task:
        name: Masked Language Modeling
        type: fill-mask
      dataset:
        name: sagawa/ZINC-canonicalized
        type: sagawa/ZINC-canonicalized
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9900059572833486
  ---
  
  <!-- 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
  