readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imdb
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-imdb-subtle
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: imdb
        type: imdb
        args: plain_text
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9074
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncased-finetuned-imdb-subtle
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5219
  - Accuracy: 0.9074
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  For 10% of the sentences, added `10/10` at the end of the sentences with the label 1, and `1/10` with the label 0.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.2308        | 1.0   | 1250 | 0.3615          | 0.8866   |
  | 0.1381        | 2.0   | 2500 | 0.2195          | 0.9354   |
  | 0.068         | 3.0   | 3750 | 0.4582          | 0.9014   |
  | 0.0395        | 4.0   | 5000 | 0.4480          | 0.9164   |
  | 0.0202        | 5.0   | 6250 | 0.5219          | 0.9074   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  