readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - new_dataset
  metrics:
  - accuracy
  model-index:
  - name: sentiment-analysis-twitter
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: new_dataset
        type: new_dataset
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7965
  ---
  
  <!-- 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. -->
  
  # sentiment-analysis-twitter
  
  This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the new_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4579
  - Accuracy: 0.7965
  
  ## 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: 2e-05
  - train_batch_size: 64
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5315        | 1.0   | 157  | 0.4517          | 0.788    |
  | 0.388         | 2.0   | 314  | 0.4416          | 0.8      |
  | 0.3307        | 3.0   | 471  | 0.4579          | 0.7965   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu102
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  