langid.py readme

Introduction

langid.py is a standalone Language Identification (LangID) tool.

The design principles are as follows:

Fast
Pre-trained over a large number of languages (currently 97)
Not sensitive to domain-specific features (e.g. HTML/XML markup)
Single .py file with minimal dependencies
Deployable as a web service

All 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, zu

The training data was drawn from 5 different sources: - JRC-Acquis - ClueWeb 09 - Wikipedia - Reuters RCV2 - Debian i18n

langid.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]

Options:

-h, --help

show this help message and exit

-s, --serve --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)

The 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', -55.106250761034801)
>>> Questa e una prova
('it', -35.417712211608887)

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', -5347.4231110975252)

The value returned is a score for the language. It is not a probability esimate, as it is not normalized by the document probability since this is unnecessary for classification.

You can also use langid.py as a python library:

# python
Python 2.7.2+ (default, Oct  4 2011, 20:06:09)
[GCC 4.6.1] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import langid
>>> langid.classify("This is a test")
('en', -55.106250761034801)

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": -55.106250761034801, "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": -55.106250761034801, "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  Speed
100  2871  100   119  100  2752    117   2723  0:00:01  0:00:01 --:--:--  2727
{"responseData": {"confidence": -3728.4490563860536, "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": -55.106250761034801, "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', -38.538481712341309)
>>> Je ne parle pas français
('fr', -116.95343780517578)
>>> I don't speak english
('it', -8.8632845878601074)

When using langid.py as a library, the set_languages method can be used to constrain the language set:

python
Python 2.7.2+ (default, Oct  4 2011, 20:06:09)
[GCC 4.6.1] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import langid
>>> langid.classify("I do not speak english")
('en', -48.104645729064941)
>>> langid.set_languages(['de','fr','it'])
>>> langid.classify("I do not speak english")
('it', -52.895359516143799)
>>> langid.set_languages(['en','it'])
>>> langid.classify("I do not speak english")
('en', -48.104645729064941)

Training a model

Training a model for langid.py is a non-trivial process, due to the large amount of computations required for the feature selection stage. Nonetheless, a parallelized model generator is provided with langid.py. The model training is broken into two steps:

LD Feature Selection (LDfeatureselect.py)
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.xml

This 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 features

This 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 features

This 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 model

dreamind/langid.py

langid.py readme

Introduction

Usage

Training a model