readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03
    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_sentence_itr0_2e-05_all_01_03_2022-05_32_03
  
  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: 0.4208
  - Accuracy: 0.8283
  - F1: 0.8915
  - Precision: 0.8487
  - Recall: 0.9389
  
  ## 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 | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | No log        | 1.0   | 390  | 0.4443          | 0.7768   | 0.8589 | 0.8072    | 0.9176 |
  | 0.4532        | 2.0   | 780  | 0.4603          | 0.8098   | 0.8791 | 0.8302    | 0.9341 |
  | 0.2608        | 3.0   | 1170 | 0.5284          | 0.8061   | 0.8713 | 0.8567    | 0.8863 |
  | 0.1577        | 4.0   | 1560 | 0.6398          | 0.8085   | 0.8749 | 0.8472    | 0.9044 |
  | 0.1577        | 5.0   | 1950 | 0.7089          | 0.8085   | 0.8741 | 0.8516    | 0.8979 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cu113
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  