

# 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
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_trainer", "chatgpt", "HC3"], "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": 4, "repetition_penalty": 1.5, "eta_cutoff": 0.0008, "renormalize_logits": true}}, "pipeline_tag": "text-generation", "model-index": [{"name": "distilgpt2-HC3", "results": []}]}
transformers pytorch safetensors gpt2 text-generation generated_from_trainer chatgpt HC3 en dataset:pszemraj/HC3-textgen-qa license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #chatgpt #HC3 #en #dataset-pszemraj/HC3-textgen-qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

distilgpt2-HC3
==============



> 
> what happens if you train a smaller model on a dataset of chatGPT responses?
> 
> 
> 


This happens.


!example


Model description
-----------------


This model is a fine-tuned version of 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  CHATGPT\_ANSWER\_TEXT <end\_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



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #chatgpt #HC3 #en #dataset-pszemraj/HC3-textgen-qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### 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 ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #generated_from_trainer #chatgpt #HC3 #en #dataset-pszemraj/HC3-textgen-qa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### 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### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.6.1
* Tokenizers 0.12.1