readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - f1
  model-index:
  - name: sentiment_trained
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: tweet_eval
        type: tweet_eval
        args: sentiment
      metrics:
      - name: F1
        type: f1
        value: 0.7253452834090693
  ---
  
  <!-- 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_trained
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2671
  - F1: 0.7253
  
  ## 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: 1.2140338797769864e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 0
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.6647        | 1.0   | 11404 | 0.6424          | 0.7189 |
  | 0.6018        | 2.0   | 22808 | 0.7947          | 0.7170 |
  | 0.5004        | 3.0   | 34212 | 1.0811          | 0.7200 |
  | 0.3761        | 4.0   | 45616 | 1.2671          | 0.7253 |
  
  
  ### Framework versions
  
  - Transformers 4.12.5
  - Pytorch 1.9.1
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  