Metadata-Version: 2.1
Name: monocleaner
Version: 1.0
Summary: Monolingual corpus fluency filter
Home-page: https://github.com/bitextor/monocleaner
Author: Prompsit Language Engineering
Author-email: info@prompsit.com
Maintainer: Jaume Zaragoza
Maintainer-email: jzaragoza@prompsit.com
License: GNU General Public License v3.0
Project-URL: Monocleaner on GitHub, https://github.com/bitextor/monocleaner
Project-URL: Prompsit Language Engineering, http://www.prompsit.com
Project-URL: Macocu, https://macocu.eu/
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Processing :: Filters
Description-Content-Type: text/markdown
License-File: LICENSE


# monocleaner

![License](https://img.shields.io/badge/License-GPLv3-blue.svg)

Monocleaner is a Python tool that aims at detecting poor fluent sentences in a monolingual corpus.
It indicates the fluency of a sentence with a score between 1 and 0, where the higher score, the better the fluency is.
Sentences considered to have obvious poor fluency are tagged with a 0.

Although a training tool (`monocleaner-train`) is provided, you may want to use the available ready-to-use language packages.
Please, visit https://github.com/bitextor/monocleaner-data/releases/latest or use `monocleaner-download` to download the latest language packages.

## Citation

If you find Monocleaner useful, please consider citing the following papers:

> V. M. Sánchez-Cartagena, M. Bañón, S. Ortiz-Rojas and G. Ramírez-Sánchez,\
> "[Prompsit's submission to WMT 2018 Parallel Corpus Filtering shared task](http://www.statmt.org/wmt18/pdf/WMT116.pdf)",\
>in *Proceedings of the Third Conference on Machine Translation, Volume 2: Shared Task Papers*.\
>Brussels, Belgium: Association for Computational Linguistics, October 2018

```latex
@InProceedings{prompsit:2018:WMT,
  author    = { V\'{i}ctor M. S\'{a}nchez-Cartagena and Marta Ba{\~n}\'{o}n and Sergio Ortiz-Rojas and Gema Ram\'{i}rez-S\'{a}nchez},
  title     = {Prompsit's submission to WMT 2018 Parallel Corpus Filtering shared task},
  booktitle = {Proceedings of the Third Conference on Machine Translation, Volume 2: Shared Task Papers},
  month     = {October},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics}
}
```

## Installation & Requirements

Monocleaner can be installed using `pip`:

```bash
python3.7 -m pip install monocleaner
```

Monocleaner requires the [KenLM](https://github.com/kpu/kenlm) Python bindings with support for 7-gram language models. You can easily install it by running the following commands:

```bash
git clone https://github.com/kpu/kenlm
cd kenlm
python3.7 -m pip install . --install-option="--max_order 7"
mkdir -p build && cd build
cmake .. -DKENLM_MAX_ORDER=7 -DCMAKE_INSTALL_PREFIX:PATH=/your/prefix/path
make -j all install
```

The remaining extra modules required by Monocleaner will be automatically downloaded and installed/upgraded (if required) with the first command.

After 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 uses [FastSpell](https://github.com/mbanon/fastspell) that requires `python-dev` and `libhunspell-dev`:
```bash
sudo apt install python-dev libhunspell-dev
```

Also note that Hunspell language packages must be installed by hand if you are going to work with one of languages listed as [similar](https://github.com/mbanon/fastspell/blob/main/fastspell/config/similar.yaml), i.e.:
```
sudo apt-get install hunspell-es
```
or downloaded from an external source, such as https://github.com/wooorm/dictionaries/tree/main/dictionaries

You can also provide the path to the Hunspell dictionaries directories by using the dictpath atribute in `{/YOUR/INSTALLATION/PATH}/config/hunspell.yaml` (for example, `venv/lib/python3.7/site-packages/fastspell/config/hunspell.yaml` ) if you are installing from PyPI or with `setup.py`, or in `/config/hunspell.yaml` if you are running directly the code. Default path is `/usr/share/hunspell`.

## Scoring
`monocleaner` aims at detecting poor fluent sentences in a monolingual corpus.
It indicates the fluency of a sentence with a score between 1 and 0, where the higher score, the better the fluency is.
Sentences considered to have obvious poor fluency are tagged with a 0.

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.

This tool can be run with
```bash
monocleaner [-h]
            [--disable_minimal_length]
            [--disable_hardrules]
            [--score_only]
            [--annotated_output]
            [--debug]
            [-q]
            model_dir [input] [output]
```
If input and output are omitted, it will read from stdin and write to stdout.

### Parameters
* Positional arguments:
  * `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. When omitted output will be written to stdout.
* Optional arguments:
  * `--score_only`: Only output one column which is the monocleaner score (default: False)
  * `--disable_hardrules`: Disables the hardrules filtering (only monocleaner fluency scoring is applied) (default: False)
  * `--disable_minimal_length` : Don't apply minimal length rule (default: False).
* Logging:
  * `-q, --quiet`: Silent logging mode (default: False)
  * `--debug`: Debug logging mode (default: False)
  * `-v, --version`: show version of this script and exit

### Example
```bash
monocleaner models/es mono.es.txt mono.es.scored.txt
```

This will use the Spanish model located at `models/es`, read `mono.es.txt` file and write the sentences to `mono.es.scored.txt` adding the monocleaner score column.

___

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All documents and software contained in this repository reflect only the authors' view. The Innovation and Networks Executive Agency of the European Union is not responsible for any use that may be made of the information it contains.


