readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imdb
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: finetuning-sentiment-model-3000-samples-6pm
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: imdb
        type: imdb
        args: plain_text
      metrics:
      - name: Precision
        type: precision
        value: 0.875
      - name: Recall
        type: recall
        value: 0.8866666666666667
      - name: F1
        type: f1
        value: 0.880794701986755
      - name: Accuracy
        type: accuracy
        value: 0.88
  ---
  
  <!-- 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. -->
  
  # finetuning-sentiment-model-3000-samples-6pm
  
  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: 0.2896
  - Precision: 0.875
  - Recall: 0.8867
  - F1: 0.8808
  - Accuracy: 0.88
  
  ## 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: 1e-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: 11
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 188  | 0.3436          | 0.8633    | 0.8    | 0.8304 | 0.8367   |
  | No log        | 2.0   | 376  | 0.2896          | 0.875     | 0.8867 | 0.8808 | 0.88     |
  | 0.3           | 3.0   | 564  | 0.3330          | 0.8693    | 0.8867 | 0.8779 | 0.8767   |
  | 0.3           | 4.0   | 752  | 0.4378          | 0.8766    | 0.9    | 0.8882 | 0.8867   |
  | 0.3           | 5.0   | 940  | 0.5198          | 0.8284    | 0.9333 | 0.8777 | 0.87     |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  