readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: xtremedistil-l6-h256-uncased-eng-only-sentiment-single-finetuned-memes
    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. -->
  
  # xtremedistil-l6-h256-uncased-eng-only-sentiment-single-finetuned-memes
  
  This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3513
  - Accuracy: 0.8555
  - Precision: 0.8706
  - Recall: 0.8697
  - F1: 0.8699
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | No log        | 1.0   | 378  | 0.3579          | 0.8526   | 0.8685    | 0.8667 | 0.8674 |
  | 0.4989        | 2.0   | 756  | 0.3368          | 0.8503   | 0.8665    | 0.8650 | 0.8645 |
  | 0.3318        | 3.0   | 1134 | 0.3379          | 0.8533   | 0.8693    | 0.8675 | 0.8678 |
  | 0.279         | 4.0   | 1512 | 0.3426          | 0.8555   | 0.8712    | 0.8698 | 0.8696 |
  | 0.279         | 5.0   | 1890 | 0.3495          | 0.8555   | 0.8717    | 0.8698 | 0.8698 |
  | 0.2471        | 6.0   | 2268 | 0.3513          | 0.8555   | 0.8706    | 0.8697 | 0.8699 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  