<jupyter_start><jupyter_text>ner<jupyter_code># sudo docker run -it --rm --gpus all \
#     -p 8080:8080 \
#     -e SEQ_LEN=256 \
#     -e BATCH_SIZE=512 \
#     natasha/slovnet-ner-bert
%run -n main.py
records = load_lenta(LENTA)
records = log_progress(records, total=LENTA_TOTAL)
items = process_ner(records)
lines = format_jl(items)
dump_gz_lines(lines, NER)
s3.upload(NER, S3_NER)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13768115942028986, 'fraction_numerical': 0.03864734299516908, 'mean_word_length': 4.3896103896103895, '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': '348975', 'license': 'MIT', 'n_tokens_mistral': 192, 'n_tokens_neox': 176, 'n_words': 31, 'path': 'scripts/01_ner.ipynb', 'repo_name': 'bzaar/nerus', 'size': '414'}