![]() sklearn import CleanTransformer cleaner = CleanTransformer( no_punct = False, lower = False)Ĭleaner. If you have a question, found a bug or want to propose a new feature, have a look at the issues page. Pull requests are especially welcomed when they fix bugs or improve the code quality. If you don't like the output of clean-text, consider adding a test with your specific input and desired output. DKENLM_MAX_ORDER=7 -DCMAKE_INSTALL_PREFIX:PATH=/your/prefix/path Related Work Generic text cleaning packagesįull-blown NLP libraries with some text cleaningīuilt upon the work by Burton DeWilde for Textacy.Pip install -config-settings= "-build-option=-max_order=7 ". The remaining extra modules required by Monocleaner will be automatically downloaded and installed/upgraded (if required) with the first command.Īfter installation, two binary files ( monocleaner-train and monocleaner) will be located in your python/installation/prefix/bin directory. This is usually $HOME/.local/bin or /usr/local/bin/. ![]() Monocleaner aims to detect disfluent sentences in a monolingual corpus.Įach sentence is assigned a fluency score between 0 and 1, with higher scores indicating more fluency. In addition to a continuous score, several handwritten hardrules assign a score of 0 to obviously poor sentences. The input file (monolingual corpus) must contain one sentence per line text. The generated output file will contain the same lines adding a column containing the Monocleaner fluency score. If input and output are omitted, it will read from stdin and write to stdout.This process is designed to be language-independent, so that it can work with any human language. model_dir: Directory where the model is stored.input: Input text file, one sentence per line.When omitted jointly with output, it will read from stdin.output: Output tab-separated text file adding monocleaner score.text BADSYMBOLSRE.sub, text) delete symbols which are in BADSYMBOLSRE from text text. When omitted output will be written to stdout. Join (word for word in text.split () if word not in STOPWORDS) delete stopwors from text return text df post df post.apply (cleantext) printplot (10) Sign up for free to join this conversation on GitHub. disable_hardrules: Disables the hardrules filtering (only monocleaner fluency scoring is applied) (default: False) Clean text Raw cleaner.py This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below.-add_lang_ident: Add another column with the identified language if it's not disabled.-score_only: Only output one column which is the monocleaner score (default: False). disable_minimal_length : Don't apply minimal length rule (default: False).To review, open the file in an editor that reveals hidden Unicode characters.
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