readme: ---
  license: mit
  datasets:
  - sagawa/pubchem-10m-canonicalized
  metrics:
  - accuracy
  model-index:
  - name: PubChem-10m-t5
    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: 0.9189779162406921
  ---
  
  # PubChem-10m-t5
  
  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.
  It achieves the following results on the evaluation set:
  - Loss: 0.2165
  - Accuracy: 0.9190
  
  
  ## Model description
  
  We trained t5 on SMILES from PubChem using the task of masked-language modeling (MLM). Compared to PubChem-10m-t5, PubChem-10m-t5-v2 uses a character-level tokenizer, and it was also 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-03
  - 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 | Step   | Accuracy | Validation Loss |
  |:-------------:|:------:|:--------:|:---------------:|
  | 0.2592        | 100000 | 0.8997   | 0.2784          |
  | 0.2790        | 200000 | 0.9095   | 0.2468          |
  | 0.2278        | 300000 | 0.9162   | 0.2256          |