readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: code-vs-nl
    results: []
  datasets:
  - bookcorpus
  - codeparrot/github-code
  language:
  - en
  ---
  
  <!-- 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. -->
  
  # code-vs-nl
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 
  on [bookcorpus](https://huggingface.co/datasets/bookcorpus) for text and [codeparrot/github-code](https://huggingface.co/datasets/codeparrot/github-code) for code datasets.
  It achieves the following results on the evaluation set:
  - Loss: 0.5180
  - Accuracy: 0.9951
  - F1 Score: 0.9950
  
  ## Model description
  
  As it's a finetuned model, it's architecture is same as distilbert-base-uncased for Sequence Classification
  
  ## Intended uses & limitations
  
  Can be used to classify documents into text and code
  
  ## Training and evaluation data
  
  It is a mix of above two datasets, equally random sampled
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-07
  - train_batch_size: 256
  - eval_batch_size: 1024
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 1000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
  | 0.5732        | 0.07  | 500  | 0.5658          | 0.9934   | 0.9934   |
  | 0.5254        | 0.14  | 1000 | 0.5180          | 0.9951   | 0.9950   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2