readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - f1
  model-index:
  - name: irony_trained_42
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: tweet_eval
        type: tweet_eval
        args: irony
      metrics:
      - name: F1
        type: f1
        value: 0.6785912258473235
  ---
  
  <!-- 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. -->
  
  # irony_trained_42
  
  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: 1.5669
  - F1: 0.6786
  
  ## 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: 2.6774391860025942e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.6669        | 1.0   | 716  | 0.6291          | 0.6198 |
  | 0.5655        | 2.0   | 1432 | 0.7332          | 0.6771 |
  | 0.3764        | 3.0   | 2148 | 1.4193          | 0.6554 |
  | 0.229         | 4.0   | 2864 | 1.5669          | 0.6786 |
  
  
  ### Framework versions
  
  - Transformers 4.12.5
  - Pytorch 1.9.1
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  