langid A standalone Language Identification (LangID) tool
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Python library Python language IDentification language identification

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langid is a Python module, a standalone Language Identification (LangID) tool.The design principles are as follows:1. Fast2. Pre-trained over a large number of languages (currently 97)3. Not sensitive to domain-specific features (e.g. HTML/XML markup)4. Single .py file with minimal dependencies5. Deployable as a web serviceAll that is required to run langid.py is >= Python 2.5 and numpy.langid.py comes pre-trained on 97 languages (ISO 639-1 codes given):af, am, an, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, dz, el, en, eo, es, et, eu, fa, fi, fo, fr, ga, gl, gu, he, hi, hr, ht, hu, hy, id, is, it, ja, jv, ka, kk, km, kn, ko, ku, ky, la, lb, lo, lt, lv, mg, mk, ml, mn, mr, ms, mt, nb, ne, nl, nn, no, oc, or, pa, pl, ps, pt, qu, ro, ru, rw, se, si, sk, sl, sq, sr, sv, sw, ta, te, th, tl, tr, ug, uk, ur, vi, vo, wa, xh, zh, zuThe training data was drawn from 5 different sources: - JRC-Acquis - ClueWeb 09 - Wikipedia - Reuters RCV2 - Debian i18nlangid.py is WSGI-compliant.langid.py will use fapws3 as a web server if available, and default to wsgiref.simple_server otherwise.Usage: Usage: langid.py Options: -h, --help show this help message and exit -s, --serve launch web service --host=HOST host/ip to bind to --port=PORT port to listen on -v increase verbosity (repeat for greater effect) -m MODEL load model from file -l LANGS, --langs=LANGS comma-separated set of target ISO639 language codes (e.g en,de) -r, --remote auto-detect IP address for remote access --demo launch an in-browser demo applicationThe simplest way to use langid.py is as a command-line tool. Invoke using python langid.py. This will cause a prompt to display. Enter text to identify, and hit enter:>>> This is a test('en', 0.99999999099035441)>>> Questa e una prova('it', 0.98569847366134222)langid.py can also detect when the input is redirected (only tested under Linux), and in this case will process until EOF rather than until newline like in interactive mode:python langid.py < readme.rst('en', 1.0)The value returned is the probability estimate for the language. Full estimation is not actually necessary for classification, and can be disabled in the source code of langid.py for a slight performance boost.You can also use langid.py as a python library:# pythonPython 2.7.2+ (default, Oct 4 2011, 20:06:09) on linux2Type "help", "copyright", "credits" or "license" for more information.>>> import langid>>> langid.classify("This is a test")('en', 0.99999999099035441)Finally, langid.py can use Python's built-in wsgiref.simple_server (or fapws3 if available) to provide language identification as a web service. To do this, launch python langid.py -s, and access localhost:9008/detect . The web service supports GET, POST and PUT. If GET is performed with no data, a simple HTML forms interface is displayed.The response is generated in JSON, here is an example:{"responseData": {"confidence": 0.99999999099035441, "language": "en"}, "responseDetails": null, "responseStatus": 200}A utility such as curl can be used to access the web service:# curl -d "q=This is a test" localhost:9008/detect{"responseData": {"confidence": 0.99999999099035441, "language": "en"}, "responseDetails": null, "responseStatus": 200}You can also use HTTP PUT:# curl -T readme.rst localhost:9008/detect % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed100 2871 100 119 100 2752 117 2723 0:00:01 0:00:01 --:--:-- 2727{"responseData": {"confidence": 1.0, "language": "en"}, "responseDetails": null, "responseStatus": 200}If no "q=XXX" key-value pair is present in the HTTP POST payload, langid.py will interpret the entire file as a single query. This allows for redirection via curl:# echo "This is a test" | curl -d @- localhost:9008/detect{"responseData": {"confidence": 0.99999999099035441, "language": "en"}, "responseDetails": null, "responseStatus": 200}langid.py will attempt to discover the host IP address automatically. Often, this is set to localhost(127.0.1.1), even though the machine has a different external IP address. langid.py can attempt to automatically discover the external IP address. To enable this functionality, start langid.py with the "-r" flag.langid.py supports constraining of the output language set using the "-l" flag and a comma-separated list of ISO639-1 language codes:# python langid.py -l it,fr>>> Io non parlo italiano('it', 0.99999999988965627)>>> Je ne parle pas français('fr', 1.0)>>> I don't speak english('it', 0.92210605672341062)When using langid.py as a library, the set_languages method can be used to constrain the language set:pythonPython 2.7.2+ (default, Oct 4 2011, 20:06:09) on linux2Type "help", "copyright", "credits" or "license" for more information.>>> import langid>>> langid.classify("I do not speak english")('en', 0.57133487679900674)>>> langid.set_languages()>>> langid.classify("I do not speak english")('it', 0.99999835791478453)>>> langid.set_languages()>>> langid.classify("I do not speak english")('en', 0.99176190378750373)Training a modelTraining a model for langid.py requires a large amount of computation for the feature selection stage. We provide a parallelized model generator that can run on a modern desktop machine. It uses a sharding technique similar to map-reduce to allow paralellization while running in constant memory.The model training is broken into two steps:1. LD Feature Selection (LDfeatureselect.py)2. Naive Bayes learning (train.py)The two steps are fully independent, and can potentially be run on different data sets. It is also possible to replace the feature selection with an alternative set of features.To train a model, we require multiple corpora of monolingual documents. Each document should be a single file, and each file should be in a 2-deep folder hierarchy, with language nested within domain. For example, we may have a number of English files: ./corpus/domain1/en/File1.txt ./corpus/domainX/en/001-file.xmlThis is the hierarchy that both LDfeatureselect.py and train.py expect. The -c argment for both is the name of the directory containing the domain-specific subdirectories, in this example './corpus'. The output file is specified with the '-o' option.To learn features, we would invoke:python LDfeatureselect.py -c corpus -o featuresThis would create a file called 'features' containing features in a one-per-line format that can be parsed by python's eval().To then generate a model using the same corpus and the selected features, we would invoke:python train.py -c corpus -o model -i featuresThis will generate a compressed model in a file called 'model'. The path to this file can then be passed as a command-line argument to langid.py:python langid.py -m modelProduct's homepage

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