readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04
    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_itr0_3e-05_all_16_02_2022-20_12_04
  
  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: 0.1620
  - Precision: 0.3509
  - Recall: 0.3793
  - F1: 0.3646
  - Accuracy: 0.9468
  
  ## 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: 3e-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   | 0.2997          | 0.1125    | 0.2057 | 0.1454 | 0.8669   |
  | No log        | 2.0   | 76   | 0.2620          | 0.1928    | 0.2849 | 0.2300 | 0.8899   |
  | No log        | 3.0   | 114  | 0.2497          | 0.1923    | 0.2906 | 0.2314 | 0.8918   |
  | No log        | 4.0   | 152  | 0.2474          | 0.1819    | 0.3377 | 0.2365 | 0.8905   |
  | No log        | 5.0   | 190  | 0.2418          | 0.2128    | 0.3264 | 0.2576 | 0.8997   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cu113
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  