readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: finetuned_token_2e-05_all_16_02_2022-15_53_17
    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. -->
  
  # finetuned_token_2e-05_all_16_02_2022-15_53_17
  
  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 None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1750
  - Precision: 0.3286
  - Recall: 0.3334
  - F1: 0.3310
  - Accuracy: 0.9447
  
  ## 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: 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 38   | 0.3355          | 0.0975    | 0.2358 | 0.1380 | 0.8361   |
  | No log        | 2.0   | 76   | 0.3177          | 0.1359    | 0.2709 | 0.1810 | 0.8398   |
  | No log        | 3.0   | 114  | 0.3000          | 0.1542    | 0.3043 | 0.2047 | 0.8471   |
  | No log        | 4.0   | 152  | 0.3033          | 0.1589    | 0.3060 | 0.2091 | 0.8434   |
  | No log        | 5.0   | 190  | 0.3029          | 0.1629    | 0.3110 | 0.2138 | 0.8447   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cu113
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  