%global _empty_manifest_terminate_build 0 Name: python-word2word Version: 1.0.0 Release: 1 Summary: Easy-to-use word translations for 3,564 language pairs License: Apache License 2.0 URL: https://github.com/kakaobrain/word2word Source0: https://mirrors.aliyun.com/pypi/web/packages/1b/c8/6aa4d029236e5e021552ccaa6a01daadaf3d9a4b5b8f9babfb73db589134/word2word-1.0.0.tar.gz BuildArch: noarch Requires: python3-requests Requires: python3-wget Requires: python3-numpy Requires: python3-tqdm %description [![image](https://img.shields.io/pypi/v/word2word.svg)](https://pypi.org/project/word2word/) [![image](https://img.shields.io/pypi/l/word2word.svg)](https://pypi.org/project/word2word/) [![image](https://img.shields.io/pypi/pyversions/word2word.svg)](https://pypi.org/project/word2word/) [![image](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/kimdwkimdw) # word2word Easy-to-use word translations for 3,564 language pairs. This is the official code accompanying [our LREC 2020 paper](https://arxiv.org/abs/1911.12019). ## Summary * A large collection of freely & publicly available bilingual lexicons **for 3,564 language pairs across 62 unique languages.** * Easy-to-use Python interface for accessing top-k word translations and for building a new bilingual lexicon from a custom parallel corpus. * Constructed using a simple approach that yields bilingual lexicons with high coverage and competitive translation quality. ## Usage First, install the package using `pip`: ```shell script pip install word2word ``` OR ```shell script git clone https://github.com/kakaobrain/word2word python setup.py install ``` Then, in Python, download the model and retrieve top-5 word translations of any given word to the desired language: ```python from word2word import Word2word en2fr = Word2word("en", "fr") print(en2fr("apple")) # out: ['pomme', 'pommes', 'pommier', 'tartes', 'fleurs'] ``` ![gif](./word2word.gif) ## Supported Languages We provide top-k word-to-word translations across all available pairs from [OpenSubtitles2018](http://opus.nlpl.eu/OpenSubtitles2018.php). This amounts to a total of 3,564 language pairs across 62 unique languages. The full list is provided [here](word2word/supporting_languages.txt). ## Methodology Our approach computes top-k word translations based on the co-occurrence statistics between cross-lingual word pairs in a parallel corpus. We additionally introduce a correction term that controls for any confounding effect coming from other source words within the same sentence. The resulting method is an efficient and scalable approach that allows us to construct large bilingual dictionaries from any given parallel corpus. For more details, see the Methodology section of [our paper](https://arxiv.org/abs/1911.12019). ## Building a Bilingual Lexicon on a Custom Parallel Corpus The `word2word` package also provides interface for building a custom bilingual lexicon using a different parallel corpus. Here, we show an example of building one from the [Medline English-French dataset](https://drive.google.com/drive/folders/0B3UxRWA52hBjQjZmYlRZWHQ4SUE): ```python from word2word import Word2word # custom parallel data: data/pubmed.en-fr.en, data/pubmed.en-fr.fr my_en2fr = Word2word.make("en", "fr", "data/pubmed.en-fr") # ...building... print(my_en2fr("mitochondrial")) # out: ['mitochondriale', 'mitochondriales', 'mitochondrial', # 'cytopathies', 'mitochondriaux'] ``` When built from source, the bilingual lexicon can also be constructed from the command line as follows: ```shell script python make.py --lang1 en --lang2 fr --datapref data/pubmed.en-fr ``` In both cases, the custom lexicon (saved to `datapref/` by default) can be re-loaded in Python: ```python from word2word import Word2word my_en2fr = Word2word.load("en", "fr", "data/pubmed.en-fr") # Loaded word2word custom bilingual lexicon from data/pubmed.en-fr/en-fr.pkl ``` ### Multiprocessing In both the Python interface and the command line interface, `make` uses multiprocessing with 16 CPUs by default. The number of CPU workers can be adjusted by setting `num_workers=N` (Python) or `--num_workers N` (command line). ## References If you use word2word for research, please cite [our paper](https://arxiv.org/abs/1911.12019): ```bibtex @inproceedings{choe2020word2word, author = {Yo Joong Choe and Kyubyong Park and Dongwoo Kim}, title = {word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs}, booktitle = {Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)}, year = {2020} } ``` All of our pre-computed bilingual lexicons were constructed from the publicly available [OpenSubtitles2018](http://opus.nlpl.eu/OpenSubtitles2018.php) dataset: ```bibtex @inproceedings{lison-etal-2018-opensubtitles2018, title = "{O}pen{S}ubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora", author = {Lison, Pierre and Tiedemann, J{\"o}rg and Kouylekov, Milen}, booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)", month = may, year = "2018", address = "Miyazaki, Japan", publisher = "European Language Resources Association (ELRA)", url = "https://www.aclweb.org/anthology/L18-1275", } ``` ## Authors [Kyubyong Park](https://github.com/Kyubyong), [Dongwoo Kim](https://github.com/kimdwkimdw), and [YJ Choe](https://github.com/yjchoe) %package -n python3-word2word Summary: Easy-to-use word translations for 3,564 language pairs Provides: python-word2word BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-word2word [![image](https://img.shields.io/pypi/v/word2word.svg)](https://pypi.org/project/word2word/) [![image](https://img.shields.io/pypi/l/word2word.svg)](https://pypi.org/project/word2word/) [![image](https://img.shields.io/pypi/pyversions/word2word.svg)](https://pypi.org/project/word2word/) [![image](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/kimdwkimdw) # word2word Easy-to-use word translations for 3,564 language pairs. This is the official code accompanying [our LREC 2020 paper](https://arxiv.org/abs/1911.12019). ## Summary * A large collection of freely & publicly available bilingual lexicons **for 3,564 language pairs across 62 unique languages.** * Easy-to-use Python interface for accessing top-k word translations and for building a new bilingual lexicon from a custom parallel corpus. * Constructed using a simple approach that yields bilingual lexicons with high coverage and competitive translation quality. ## Usage First, install the package using `pip`: ```shell script pip install word2word ``` OR ```shell script git clone https://github.com/kakaobrain/word2word python setup.py install ``` Then, in Python, download the model and retrieve top-5 word translations of any given word to the desired language: ```python from word2word import Word2word en2fr = Word2word("en", "fr") print(en2fr("apple")) # out: ['pomme', 'pommes', 'pommier', 'tartes', 'fleurs'] ``` ![gif](./word2word.gif) ## Supported Languages We provide top-k word-to-word translations across all available pairs from [OpenSubtitles2018](http://opus.nlpl.eu/OpenSubtitles2018.php). This amounts to a total of 3,564 language pairs across 62 unique languages. The full list is provided [here](word2word/supporting_languages.txt). ## Methodology Our approach computes top-k word translations based on the co-occurrence statistics between cross-lingual word pairs in a parallel corpus. We additionally introduce a correction term that controls for any confounding effect coming from other source words within the same sentence. The resulting method is an efficient and scalable approach that allows us to construct large bilingual dictionaries from any given parallel corpus. For more details, see the Methodology section of [our paper](https://arxiv.org/abs/1911.12019). ## Building a Bilingual Lexicon on a Custom Parallel Corpus The `word2word` package also provides interface for building a custom bilingual lexicon using a different parallel corpus. Here, we show an example of building one from the [Medline English-French dataset](https://drive.google.com/drive/folders/0B3UxRWA52hBjQjZmYlRZWHQ4SUE): ```python from word2word import Word2word # custom parallel data: data/pubmed.en-fr.en, data/pubmed.en-fr.fr my_en2fr = Word2word.make("en", "fr", "data/pubmed.en-fr") # ...building... print(my_en2fr("mitochondrial")) # out: ['mitochondriale', 'mitochondriales', 'mitochondrial', # 'cytopathies', 'mitochondriaux'] ``` When built from source, the bilingual lexicon can also be constructed from the command line as follows: ```shell script python make.py --lang1 en --lang2 fr --datapref data/pubmed.en-fr ``` In both cases, the custom lexicon (saved to `datapref/` by default) can be re-loaded in Python: ```python from word2word import Word2word my_en2fr = Word2word.load("en", "fr", "data/pubmed.en-fr") # Loaded word2word custom bilingual lexicon from data/pubmed.en-fr/en-fr.pkl ``` ### Multiprocessing In both the Python interface and the command line interface, `make` uses multiprocessing with 16 CPUs by default. The number of CPU workers can be adjusted by setting `num_workers=N` (Python) or `--num_workers N` (command line). ## References If you use word2word for research, please cite [our paper](https://arxiv.org/abs/1911.12019): ```bibtex @inproceedings{choe2020word2word, author = {Yo Joong Choe and Kyubyong Park and Dongwoo Kim}, title = {word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs}, booktitle = {Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)}, year = {2020} } ``` All of our pre-computed bilingual lexicons were constructed from the publicly available [OpenSubtitles2018](http://opus.nlpl.eu/OpenSubtitles2018.php) dataset: ```bibtex @inproceedings{lison-etal-2018-opensubtitles2018, title = "{O}pen{S}ubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora", author = {Lison, Pierre and Tiedemann, J{\"o}rg and Kouylekov, Milen}, booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)", month = may, year = "2018", address = "Miyazaki, Japan", publisher = "European Language Resources Association (ELRA)", url = "https://www.aclweb.org/anthology/L18-1275", } ``` ## Authors [Kyubyong Park](https://github.com/Kyubyong), [Dongwoo Kim](https://github.com/kimdwkimdw), and [YJ Choe](https://github.com/yjchoe) %package help Summary: Development documents and examples for word2word Provides: python3-word2word-doc %description help [![image](https://img.shields.io/pypi/v/word2word.svg)](https://pypi.org/project/word2word/) [![image](https://img.shields.io/pypi/l/word2word.svg)](https://pypi.org/project/word2word/) [![image](https://img.shields.io/pypi/pyversions/word2word.svg)](https://pypi.org/project/word2word/) [![image](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/kimdwkimdw) # word2word Easy-to-use word translations for 3,564 language pairs. This is the official code accompanying [our LREC 2020 paper](https://arxiv.org/abs/1911.12019). ## Summary * A large collection of freely & publicly available bilingual lexicons **for 3,564 language pairs across 62 unique languages.** * Easy-to-use Python interface for accessing top-k word translations and for building a new bilingual lexicon from a custom parallel corpus. * Constructed using a simple approach that yields bilingual lexicons with high coverage and competitive translation quality. ## Usage First, install the package using `pip`: ```shell script pip install word2word ``` OR ```shell script git clone https://github.com/kakaobrain/word2word python setup.py install ``` Then, in Python, download the model and retrieve top-5 word translations of any given word to the desired language: ```python from word2word import Word2word en2fr = Word2word("en", "fr") print(en2fr("apple")) # out: ['pomme', 'pommes', 'pommier', 'tartes', 'fleurs'] ``` ![gif](./word2word.gif) ## Supported Languages We provide top-k word-to-word translations across all available pairs from [OpenSubtitles2018](http://opus.nlpl.eu/OpenSubtitles2018.php). This amounts to a total of 3,564 language pairs across 62 unique languages. The full list is provided [here](word2word/supporting_languages.txt). ## Methodology Our approach computes top-k word translations based on the co-occurrence statistics between cross-lingual word pairs in a parallel corpus. We additionally introduce a correction term that controls for any confounding effect coming from other source words within the same sentence. The resulting method is an efficient and scalable approach that allows us to construct large bilingual dictionaries from any given parallel corpus. For more details, see the Methodology section of [our paper](https://arxiv.org/abs/1911.12019). ## Building a Bilingual Lexicon on a Custom Parallel Corpus The `word2word` package also provides interface for building a custom bilingual lexicon using a different parallel corpus. Here, we show an example of building one from the [Medline English-French dataset](https://drive.google.com/drive/folders/0B3UxRWA52hBjQjZmYlRZWHQ4SUE): ```python from word2word import Word2word # custom parallel data: data/pubmed.en-fr.en, data/pubmed.en-fr.fr my_en2fr = Word2word.make("en", "fr", "data/pubmed.en-fr") # ...building... print(my_en2fr("mitochondrial")) # out: ['mitochondriale', 'mitochondriales', 'mitochondrial', # 'cytopathies', 'mitochondriaux'] ``` When built from source, the bilingual lexicon can also be constructed from the command line as follows: ```shell script python make.py --lang1 en --lang2 fr --datapref data/pubmed.en-fr ``` In both cases, the custom lexicon (saved to `datapref/` by default) can be re-loaded in Python: ```python from word2word import Word2word my_en2fr = Word2word.load("en", "fr", "data/pubmed.en-fr") # Loaded word2word custom bilingual lexicon from data/pubmed.en-fr/en-fr.pkl ``` ### Multiprocessing In both the Python interface and the command line interface, `make` uses multiprocessing with 16 CPUs by default. The number of CPU workers can be adjusted by setting `num_workers=N` (Python) or `--num_workers N` (command line). ## References If you use word2word for research, please cite [our paper](https://arxiv.org/abs/1911.12019): ```bibtex @inproceedings{choe2020word2word, author = {Yo Joong Choe and Kyubyong Park and Dongwoo Kim}, title = {word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs}, booktitle = {Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)}, year = {2020} } ``` All of our pre-computed bilingual lexicons were constructed from the publicly available [OpenSubtitles2018](http://opus.nlpl.eu/OpenSubtitles2018.php) dataset: ```bibtex @inproceedings{lison-etal-2018-opensubtitles2018, title = "{O}pen{S}ubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora", author = {Lison, Pierre and Tiedemann, J{\"o}rg and Kouylekov, Milen}, booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)", month = may, year = "2018", address = "Miyazaki, Japan", publisher = "European Language Resources Association (ELRA)", url = "https://www.aclweb.org/anthology/L18-1275", } ``` ## Authors [Kyubyong Park](https://github.com/Kyubyong), [Dongwoo Kim](https://github.com/kimdwkimdw), and [YJ Choe](https://github.com/yjchoe) %prep %autosetup -n word2word-1.0.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-word2word -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 1.0.0-1 - Package Spec generated