readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: finetuned_token_itr0_0.0002_essays_16_02_2022-21_04_02
    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_0.0002_essays_16_02_2022-21_04_02
  
  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: 0.2158
  - Precision: 0.5814
  - Recall: 0.7073
  - F1: 0.6382
  - Accuracy: 0.9248
  
  ## 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: 0.0002
  - 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   | 11   | 0.3920          | 0.4392    | 0.6069 | 0.5096 | 0.8593   |
  | No log        | 2.0   | 22   | 0.3304          | 0.4282    | 0.6260 | 0.5085 | 0.8672   |
  | No log        | 3.0   | 33   | 0.3361          | 0.4840    | 0.6336 | 0.5488 | 0.8685   |
  | No log        | 4.0   | 44   | 0.3258          | 0.5163    | 0.6641 | 0.5810 | 0.8722   |
  | No log        | 5.0   | 55   | 0.3472          | 0.5192    | 0.6718 | 0.5857 | 0.8743   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cu113
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  