readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
    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. -->
  
  # DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
  
  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0557
  - Precision: 0.9930
  - Recall: 0.9878
  - F1: 0.9904
  - Accuracy: 0.9814
  
  ## 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: 1e-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   | 479  | 0.3334          | 0.9041    | 0.9041 | 0.9041 | 0.8550   |
  | 0.3756        | 2.0   | 958  | 0.3095          | 0.8991    | 0.9251 | 0.9119 | 0.8649   |
  | 0.2653        | 3.0   | 1437 | 0.3603          | 0.8929    | 0.9527 | 0.9218 | 0.8779   |
  | 0.1991        | 4.0   | 1916 | 0.3907          | 0.8919    | 0.9540 | 0.9219 | 0.8779   |
  | 0.1586        | 5.0   | 2395 | 0.3642          | 0.9070    | 0.9356 | 0.9211 | 0.8788   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cu113
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  