langid.pyc

The modified version of langid.c with Python bindings -- a straightforward replacement for langid.py, offering the same features, but 200 times as faster.

Installation

pip install langid-pyc

Usage

Basic

from langid_pyc import (
    classify,
    rank,
)

classify("This is English text")
# ('en', 0.9999999239251556)

rank("This is English text")
# [('en', 0.9999999239251556),
#  ('la', 5.0319768731501096e-08),
#  ('br', 1.2684715402216825e-08),
#  ...]

Language set constraint

from langid_pyc import (
    classify,
    nb_classes,
    set_languages,
)

nb_classes()
# ['af',
#  'am',
#  'an',
#  ...]

len(nb_classes())
# 97

set_languages(["en", "ru"])
nb_classes()
# ['en', 'ru']

classify("This is English text")
# ('en', 1.0)

classify("А это текст на русском")
# ('ru', 1.0)

set_languages() # reset languages
len(nb_classes())
# 97

LanguageIdentifier class

from langid_pyc import LanguageIdentifier

identifier = LanguageIdentifier.from_modelpath("ldpy3.pmodel")  # default model

len(identifier.nb_classes)
# 97

identifier.classify("This is English text")
# ('en', 0.9999999239251556)

# identifier.rank(...)
# identifier.set_languages(...)

How to build?

Install relevant protobuf packages

apt install protobuf-c-compiler libprotobuf-c-dev

Install dev python requirements

pip install -r requirements.txt

Run build

make build

See Makefile for more details.

How to add a new model?

Train a new model using langid.py package. You will get the model file as described here:

# output the model
output_path = os.path.join(model_dir, 'your_new_model.model')
model = nb_ptc, nb_pc, nb_classes,tk_nextmove, tk_output
string = base64.b64encode(bz2.compress(cPickle.dumps(model)))
with open(output_path, 'w') as f:
f.write(string)
print "wrote model to %s (%d bytes)" % (output_path, len(string))

Move your_new_model.model to models dir and run

make your_new_model.model

Now you have your_new_model.pmodel file in the root which can be feed to LanguageIdentifer.from_modelpath

from langid_pyc import LanguageIdentifier

your_new_identifier = LanguageIdentifier.from_modelpath("your_new_model.pmodel")

Benchmark

Benchmark was calculated on Mac M2 Max, 32Gb RAM with python 3.8.18 and can be found here.

TL;DR langid.pyc is ~200x faster than langid.py and ~1-1.5x faster than pycld2, especially on long texts.

Original README

================ langid.c readme

Introduction

langid.c is an experimental implementation of the language identifier described by [1] in pure C. It is largely based on the design of langid.py[2], and uses langid.py to train models.

Planned features

See TODO

Speed

Initial comparisons against Google's cld2[3] suggest that langid.c is about twice as fast.

(langid.c) @mlui langid.c git:[master] wc -l wikifiles 
28600 wikifiles
(langid.c) @mlui langid.c git:[master] time cat wikifiles | ./compact_lang_det_batch > xxx
cat wikifiles  0.00s user 0.00s system 0% cpu 7.989 total
./compact_lang_det_batch > xxx  7.77s user 0.60s system 98% cpu 8.479 total
(langid.c) @mlui langid.c git:[master] time cat wikifiles | ./langidOs -b > xxx           
cat wikifiles  0.00s user 0.00s system 0% cpu 3.577 total
./langidOs -b > xxx  3.44s user 0.24s system 97% cpu 3.759 total

(langid.c) @mlui langid.c git:[master] wc -l rcv2files 
20000 rcv2files
(langid.c) @mlui langid.c git:[master] time cat rcv2files | ./langidO2 -b > xxx     
cat rcv2files  0.00s user 0.00s system 0% cpu 31.702 total
./langidO2 -b > xxx  8.23s user 0.54s system 22% cpu 38.644 total
(langid.c) @mlui langid.c git:[master] time cat rcv2files | ./compact_lang_det_batch > xxx 
cat rcv2files  0.00s user 0.00s system 0% cpu 18.343 total
./compact_lang_det_batch > xxx  18.14s user 0.53s system 97% cpu 19.155 total

Model Training

Google's protocol buffers [4] are used to transfer models between languages. The Python program ldpy2ldc.py can convert a model produced by langid.py [2] into the protocol-buffer format, and also the C source format used to compile an in-built model directly into executable.

Dependencies

Protocol buffers [4] protobuf-c [5]

Contact

Marco Lui

References

[1] http://aclweb.org/anthology-new/I/I11/I11-1062.pdf [2] https://github.com/saffsd/langid.py [3] https://code.google.com/p/cld2/ [4] https://github.com/google/protobuf/ [5] https://github.com/protobuf-c/protobuf-c