# this is a small derivative from 8M-big c4-en dataset for testing # how this build script and dataset_infos.json were generated # mkdir c4-en-10k cd c4-en-10k # data (extracted the dataset elsewhere) - this is a 1TB+ dataset, so tough to rebuild from scratch ``` from datasets import load_dataset dataset_name = "c4" ds = load_dataset(dataset_name, 'en', split='train[:10000]') ds.to_json(f"c4.jsonl", orient="records", lines=True) ``` mkdir c4-en-10k mv c4-en-10k.jsonl c4-en-10k tar cfJ c4-en-10k.tar.xz c4-en-10k # the c4-en-10k subdir gets created on the fly aws s3 cp c4-en-10k.tar.xz s3://datasets.huggingface.co/nlp/datasets/c4/ # script (adapted from stas/oscar-en-10k) # manually check that the script is correct - edit the descriptions # create a new dataset entry on the hub https://huggingface.co/new-dataset # once created clone it git clone https://huggingface.co/datasets/stas/c4-en-10k cp c4-en-10k.py process.txt c4-en-10k cd c4-en-10k git add c4-en-10k.py process.txt README.md git commit -m "build script" c4-en-10k.py process.txt git push # test and generate config file cd .. datasets-cli test ./c4-en-10k --save_infos --all_configs # add and push the generated config cd c4-en-10k git add dataset_infos.json git commit -m "add dataset_infos.json" dataset_infos.json git push # test that the dataset is working python -c "from datasets import load_dataset; ds=load_dataset('stas/c4-en-10k'); print(ds)"