readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: ChemBERTa_drug_state_classification
    results: []
  ---
  
  <!-- 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. -->
  
  # ChemBERTa_drug_state_classification
  
  This model is a fine-tuned version of [nepp1d0/ChemBERTa_drug_state_classification](https://huggingface.co/nepp1d0/ChemBERTa_drug_state_classification) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0463
  - Accuracy: 0.9870
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5063        | 1.0   | 240  | 0.3069          | 0.9160   |
  | 0.3683        | 2.0   | 480  | 0.2135          | 0.9431   |
  | 0.2633        | 3.0   | 720  | 0.1324          | 0.9577   |
  | 0.1692        | 4.0   | 960  | 0.0647          | 0.9802   |
  | 0.1109        | 5.0   | 1200 | 0.0463          | 0.9870   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.12.1
  