File size: 3,317 Bytes
b57852a e16ad26 b57852a e16ad26 c183875 e16ad26 b57852a 60ba70f e16ad26 b57852a 60ba70f b57852a 60ba70f b57852a 60ba70f b57852a 60ba70f b57852a e16ad26 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
---
license: apache-2.0
tags:
- generated_from_trainer
- chatgpt
metrics:
- accuracy
model-index:
- name: distilgpt2-HC3
results: []
widget:
- text: >-
Is this review positive or negative? Review: Best cast iron skillet you will
ever buy. <answer>
example_title: Sentiment analysis
- text: >-
Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
He chose her because she had <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 <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
inference:
parameters:
temperature: 0.6
max_length: 96
no_repeat_ngram_size: 2
datasets:
- Hello-SimpleAI/HC3
language:
- en
library_name: transformers
---
# distilgpt2-HC3
> what happens if you train a smaller model on a dataset of chatGPT responses?
This happens.
## 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 |