readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: DistilBERT_FINAL_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. -->
  
  # DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
  
  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: 0.0699
  - Precision: 0.9942
  - Recall: 0.9773
  - F1: 0.9857
  - Accuracy: 0.9725
  
  ## 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  | 0.4036          | 0.8333    | 0.9326 | 0.8802 | 0.8054   |
  | 0.5047        | 2.0   | 958  | 0.3749          | 0.8635    | 0.9339 | 0.8973 | 0.8361   |
  | 0.3336        | 3.0   | 1437 | 0.3789          | 0.8862    | 0.9184 | 0.9020 | 0.8471   |
  | 0.2644        | 4.0   | 1916 | 0.4024          | 0.8762    | 0.9171 | 0.8962 | 0.8371   |
  | 0.2233        | 5.0   | 2395 | 0.4195          | 0.8784    | 0.9171 | 0.8973 | 0.8391   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1+cu113
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  