<jupyter_start><jupyter_text>Hebrew Study Notebook<jupyter_code>from nb_code import *
! date
vocabulary = 'sample_vocab/test.json'
voc = loadStudy(vocabulary)<jupyter_output>preparing TF...
   |     0.00s No structure info in otext, the structure part of the T-API cannot be used<jupyter_text>Controls:<jupyter_code>voc.learn()<jupyter_output>The following scores were changed 
0->6 		 402
0->2 		 73
0->5 		 832
0->3 		 1847
0->4 		 687
0->1 		 43

duration:  0:00:10.546899

seconds per term: 0.11<jupyter_text>Study Stats  Size of Known Term Set (Scores 3-4)<jupyter_code>plot_progress(voc)<jupyter_output><empty_output><jupyter_text>Unlearned Terms by Frequency<jupyter_code>plot_freqs(voc)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10497237569060773, 'fraction_numerical': 0.06629834254143646, 'mean_word_length': 5.531531531531532, 'pattern_counts': {'":': 0, '<': 15, '<?xml version=': 0, '>': 21, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '122620', 'license': 'MIT', 'n_tokens_mistral': 337, 'n_tokens_neox': 295, 'n_words': 60, 'path': 'sample_NB.ipynb', 'repo_name': 'codykingham/Mahir', 'size': '724'}