<jupyter_start><jupyter_text>datasets<jupyter_code>%run -n main.py
path = join_path(DATA_DIR, DATASET)
!mkdir -p {path}
# for name, load in [(PERSONS, load_persons), (BSNLP, load_bsnlp)]:
#     filename = CORUS_FILES[name]
#     path = join_path(CORUS_DATA_DIR, filename)
#     records = load(path)
#     records = log_progress(records, desc=name)
#     path = join_path(DATA_DIR, DATASET, name + JL + GZ)
#     items = as_jsons(records)
#     lines = format_jl(items)
#     dump_gz_lines(lines, path)
datasets = {}
for name in DATASETS:
    path = join_path(DATA_DIR, DATASET, name + JL + GZ)
    lines = load_gz_lines(path)
    items = parse_jl(lines)
    datasets[name] = list(from_jsons(items, Markup))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13179347826086957, 'fraction_numerical': 0.0, 'mean_word_length': 4.45925925925926, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '97121', 'license': 'MIT', 'n_tokens_mistral': 293, 'n_tokens_neox': 290, 'n_words': 57, 'path': 'scripts/06_norm/main.ipynb', 'repo_name': 'sdspieg/naeval', 'size': '736'}