---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- generated_from_trainer
- chatgpt
datasets:
- pszemraj/HC3-textgen-qa
metrics:
- accuracy
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
pipeline_tag: text-generation
base_model: distilgpt2
model-index:
- name: distilgpt2-HC3
  results: []
---


# 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