How we used ShareGPT to create our benchmark dataset

sg_90k_part1_html_cleaned.json

Download ShareGPT dataset

https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/HTML_cleaned_raw_dataset/sg_90k_part1_html_cleaned.json

Install Fastchat

pip install fschat

Clean data:

pip install polyglot pyicu pycld2
python -m fastchat.data.optional_clean --in sg_90k_part1_html_cleaned.json --out sg_90k_part1_html_cleaned_lang.json --keep-lang en

Extract first prompt

python extract_first.py --in-file sg_90k_part1_html_cleaned_lang.json --out-file sg_90k_part1_html_cleaned_lang_first.json

Sample data

python -m fastchat.data.sample --in sg_90k_part1_html_cleaned_lang_first.json --out sg_90k_part1_html_cleaned_lang_first_sampled.json --end 10000 --max-length 10000

Sorted data

We sort the requests by sequence length, placing the longest sequences first. This approach minimizes the amount of padding required and allows for early detection of out-of-memory.

python sort.py --data-dir sg_90k_part1_html_cleaned_lang_first_sampled.json --out-file sg_90k_part1_html_cleaned_lang_first_sampled_sorted.json

ShareGPT_V3_filtered.json

Download ShareGPT dataset

https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

Install Transformers

pip install transformers

Filter conversations with too long prompts/responses, extract first turn, and randomly sample 500 prompts

python filter_dataset.py

Compare the response length distribution of sampled dataset with respect to initial dataset

pip install matplotlib numpy
python compare_distributions.py