readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: twitter-data-distilbert-base-uncased-sentiment-finetuned-memes-v2
    results: []
  ---
  
  <!-- 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. -->
  
  # twitter-data-distilbert-base-uncased-sentiment-finetuned-memes-v2
  
  This model is a fine-tuned version of [jayantapaul888/twitter-data-distilbert-base-uncased-sentiment-finetuned-memes](https://huggingface.co/jayantapaul888/twitter-data-distilbert-base-uncased-sentiment-finetuned-memes) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3345
  - Accuracy: 0.6527
  - Precision: 0.6528
  - Recall: 0.6527
  - F1: 0.6527
  
  ## 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: 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: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.859         | 1.0   | 663  | 0.7251          | 0.6635   | 0.6722    | 0.6635 | 0.6620 |
  | 0.6619        | 2.0   | 1326 | 0.7307          | 0.6723   | 0.6737    | 0.6723 | 0.6720 |
  | 0.5684        | 3.0   | 1989 | 0.8724          | 0.6629   | 0.6645    | 0.6629 | 0.6626 |
  | 0.3338        | 4.0   | 2652 | 1.0549          | 0.6551   | 0.6571    | 0.6551 | 0.6548 |
  | 0.2512        | 5.0   | 3315 | 1.2275          | 0.6554   | 0.6555    | 0.6554 | 0.6553 |
  | 0.1945        | 6.0   | 3978 | 1.3345          | 0.6527   | 0.6528    | 0.6527 | 0.6527 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  