<jupyter_start><jupyter_text>Encode<jupyter_code># labels from 0 to 9
arr = [0, 1, 2, 3, 4, 5, 6, 7, 8]
from thermoencoder import thermo_encode_8
enc = thermo_encode_8(arr)
enc<jupyter_output><empty_output><jupyter_text>Decode<jupyter_code>from thermoencoder import thermo_decode_max
deco = thermo_decode_max(enc)
deco<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10344827586206896, 'fraction_numerical': 0.03735632183908046, 'mean_word_length': 9.90625, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '29100', 'license': 'MIT', 'n_tokens_mistral': 156, 'n_tokens_neox': 145, 'n_words': 29, 'path': 'examples/encode and decode.ipynb', 'repo_name': 'kmedian/thermoencoder', 'size': '348'}