readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - chatgpt
  metrics:
  - accuracy
  model-index:
  - name: distilgpt2-HC3
    results: []
  widget:
  - text: >-
      Review: Best cast iron skillet you will ever buy. Is this review positive or
      negative? <answer>
    example_title: Sentiment analysis
  - text: >-
      Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
      He chose her because <answer>
    example_title: Coreference resolution
  - text: >-
      On a shelf, there are five books: a gray book, a red book, a purple book, a
      blue book, and a black book. Here's the puzzle, <answer>
    example_title: Logic puzzles
  - text: >-
      The two men running to become New York City's next mayor will face off in
      their first debate Wednesday night <answer>
    example_title: Reading comprehension
  - text: >-
      Is it true that if I have five 5-hour energy drinks in a single 24-hour
      period, I get 25 hours of energy and spontaneously explode? <answer>
    example_title: 5 hour energy
  - text: >-
      what happens if you train a smaller model on a dataset of
      reinforcement-learning optimized model responses? <answer>
    example_title: deep learning advice
  inference:
    parameters:
      temperature: 0.6
      max_length: 96
      no_repeat_ngram_size: 3
      repetition_penalty: 1.5
  datasets:
  - Hello-SimpleAI/HC3
  language:
  - en
  library_name: transformers
  pipeline_tag: text-generation
  ---
  
  
  # distilgpt2-HC3
  
  
  > what happens if you train a smaller model on a dataset of chatGPT responses?
  
  This happens.
  
  ![example](https://i.imgur.com/i5snxQJ.png)
  
  ## Model description
  
  This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the "chatgpt answers" column of the `Hello-SimpleAI/HC3` dataset.
  
  It achieves the following results on the evaluation set:
  - Loss: 1.9983
  - Accuracy: 0.5441
  
  
  ## Intended uses & limitations
  
  Despite how it sounds, this model only has 80m parameters and will likely not be factually accurate most of the time.
  
  ## Training and evaluation data
  
  Modifications made w.r.t. original dataset:
  
  - drop all rows that did not have a chatGPT answer 
  - if a row (_i.e. ELI5 question, etc_) had more than one response (_from chatGPT_), randomly choose one of the responses as the answer to the question
  - the "question" and chatGPT answer were combined into a single string for that row as follows: `QUESTION_TEXT <answer> CHATGPT_ANSWER_TEXT <end_answer>`
    - `<answer>` and `<end_answer>` serve as added tokens to help the model learn "turns" in the conversation
   
  ## Training procedure
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.001
  - train_batch_size: 8
  - eval_batch_size: 4
  - seed: 3208
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.05
  - num_epochs: 6.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 2.2485        | 0.98  | 41   | 2.1457          | 0.5158   |
  | 2.0757        | 1.98  | 82   | 2.0584          | 0.5304   |
  | 1.966         | 2.98  | 123  | 2.0210          | 0.5376   |
  | 1.8602        | 3.98  | 164  | 2.0012          | 0.5422   |
  | 1.8089        | 4.98  | 205  | 1.9977          | 0.5436   |
  | 1.7698        | 5.98  | 246  | 1.9983          | 0.5441   |
  
  
  ### Framework versions
  
  - Transformers 4.27.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 2.6.1
  - Tokenizers 0.12.1