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_2022-21_11_08
    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_2022-21_11_08
  
  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: 0.2731
  - Precision: 0.1928
  - Recall: 0.3457
  - F1: 0.2475
  - Accuracy: 0.8826
  
  ## 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   | 30   | 0.3010          | 0.1330    | 0.2345 | 0.1697 | 0.8707   |
  | No log        | 2.0   | 60   | 0.2446          | 0.1739    | 0.2948 | 0.2188 | 0.8949   |
  | No log        | 3.0   | 90   | 0.2235          | 0.2446    | 0.3032 | 0.2708 | 0.9080   |
  | No log        | 4.0   | 120  | 0.2226          | 0.2670    | 0.3350 | 0.2972 | 0.9058   |
  | No log        | 5.0   | 150  | 0.2166          | 0.2779    | 0.3417 | 0.3065 | 0.9063   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cu113
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  