<jupyter_start><jupyter_text>Overview of feature valuesWe show how you can get a quick overview of the values of a feature.This might help to make queries that use a particular feature more to the point.<jupyter_code>from tf.app import use
A = use("bhsa:clone", hoist=globals())<jupyter_output><empty_output><jupyter_text>Let's see the `uvf` feature.<jupyter_code>F.uvf.freqList()<jupyter_output><empty_output><jupyter_text>You see a tuple of pairs, of which the first is a value of the `uvf` feature and the second is the number of nodes that carry that value. While we're at it, we also show the values of `nme`, `pfm`, `prs`, `vbe`, `vbs`, `tab`. To see what the features stand for,consult the feature documentation by clicking on the appropriate link above.<jupyter_code>F.nme.freqList()
F.pfm.freqList()
F.prs.freqList()
F.vbe.freqList()
F.vbs.freqList()
F.tab.freqList()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10706401766004416, 'fraction_numerical': 0.0, 'mean_word_length': 5.976923076923077, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '577508', 'license': 'MIT', 'n_tokens_mistral': 308, 'n_tokens_neox': 304, 'n_words': 108, 'path': 'tutorial/cookbook/featureValues.ipynb', 'repo_name': 'ETCBC/bhs', 'size': '906'}