readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - sagawa/pubchem-10m-canonicalized
  metrics:
  - accuracy
  model-index:
  - name: PubChem-10m-deberta
    results:
    - task:
        name: Masked Language Modeling
        type: fill-mask
      dataset:
        name: sagawa/pubchem-10m-canonicalized
        type: sagawa/pubchem-10m-canonicalized
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9741235263046233
  ---
  
  <!-- 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. -->
  
  # PubChem10m-deberta-base-output
  
  This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/pubchem-10m-canonicalized dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0698
  - Accuracy: 0.9741
  
  ## Model description
  
  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.
  
  ## 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 [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.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 30
  - eval_batch_size: 48
  - 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   | Validation Loss | Accuracy |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|
  | 0.0855        | 3.68  | 100000 | 0.0801          | 0.9708   |
  | 0.0733        | 7.37  | 200000 | 0.0702          | 0.9740   |
  
  
  ### Framework versions
  
  - Transformers 4.22.0.dev0
  - Pytorch 1.12.0
  - Datasets 2.4.1.dev0
  - Tokenizers 0.11.6
  