readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - rotten_tomatoes
  metrics:
  - accuracy
  model-index:
  - name: outputs
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: rotten_tomatoes
        type: rotten_tomatoes
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8386491557223265
  ---
  
  # distilbert_rotten_tomatoes_sentiment_classifier
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7927
  - Accuracy: 0.8386
  
  ## Model description
  
  The goal was to fine-tune a model on the rotten_tomatoes dataset to showcase an end-to-end workflow using the Hugging face library. As such, only the bare minimum  of pre-processing was used.
  
  ## Intended uses & limitations
  
  The model will be used as part of a blog post to help others engineers better understand what natural language processing is and how to perform a text classification.
  
  ## Training and evaluation data
  
  The model was evaluated using the accuracy metric that form part of the Hugging Face library.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 134  | 0.5940          | 0.8340   |
  | No log        | 2.0   | 268  | 0.7095          | 0.8227   |
  | No log        | 3.0   | 402  | 0.7276          | 0.8321   |
  | 0.065         | 4.0   | 536  | 0.7693          | 0.8415   |
  | 0.065         | 5.0   | 670  | 0.7927          | 0.8386   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  