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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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- chatgpt |
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- HC3 |
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datasets: |
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- pszemraj/HC3-textgen-qa |
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metrics: |
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- accuracy |
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widget: |
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- text: 'Review: Best cast iron skillet you will ever buy. Is this review positive |
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or negative? <answer>' |
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example_title: Sentiment analysis |
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- text: Barack Obama nominated Hilary Clinton as his secretary of state on Monday. |
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He chose her because <answer> |
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example_title: Coreference resolution |
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- text: 'On a shelf, there are five books: a gray book, a red book, a purple book, |
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a blue book, and a black book. Here''s the puzzle, <answer>' |
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example_title: Logic puzzles |
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- text: The two men running to become New York City's next mayor will face off in |
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their first debate Wednesday night <answer> |
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example_title: Reading comprehension |
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- text: Is it true that if I have five 5-hour energy drinks in a single 24-hour period, |
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I get 25 hours of energy and spontaneously explode? <answer> |
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example_title: 5 hour energy |
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- text: what happens if you train a smaller model on a dataset of reinforcement-learning |
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optimized model responses? <answer> |
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example_title: deep learning advice |
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inference: |
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parameters: |
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temperature: 0.6 |
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max_length: 96 |
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no_repeat_ngram_size: 4 |
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repetition_penalty: 1.5 |
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eta_cutoff: 0.0008 |
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renormalize_logits: true |
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pipeline_tag: text-generation |
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model-index: |
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- name: distilgpt2-HC3 |
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results: [] |
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--- |
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# distilgpt2-HC3 |
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> what happens if you train a smaller model on a dataset of chatGPT responses? |
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This happens. |
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## Model description |
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the "chatgpt answers" column of the `Hello-SimpleAI/HC3` dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9983 |
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- Accuracy: 0.5441 |
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## Intended uses & limitations |
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Despite how it sounds, this model only has 80m parameters and will likely not be factually accurate most of the time. |
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## Training and evaluation data |
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Modifications made w.r.t. original dataset: |
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- drop all rows that did not have a chatGPT answer |
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- 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 |
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- the "question" and chatGPT answer were combined into a single string for that row as follows: `QUESTION_TEXT <answer> CHATGPT_ANSWER_TEXT <end_answer>` |
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- `<answer>` and `<end_answer>` serve as added tokens to help the model learn "turns" in the conversation |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 3208 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 6.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.2485 | 0.98 | 41 | 2.1457 | 0.5158 | |
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| 2.0757 | 1.98 | 82 | 2.0584 | 0.5304 | |
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| 1.966 | 2.98 | 123 | 2.0210 | 0.5376 | |
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| 1.8602 | 3.98 | 164 | 2.0012 | 0.5422 | |
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| 1.8089 | 4.98 | 205 | 1.9977 | 0.5436 | |
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| 1.7698 | 5.98 | 246 | 1.9983 | 0.5441 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.12.1 |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__distilgpt2-HC3) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |28.18| |
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|AI2 Reasoning Challenge (25-Shot)|24.66| |
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|HellaSwag (10-Shot) |27.99| |
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|MMLU (5-Shot) |23.95| |
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|TruthfulQA (0-shot) |42.10| |
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|Winogrande (5-shot) |50.36| |
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|GSM8k (5-shot) | 0.00| |
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