Custom language detection¶
dateparser allows to customize the language detection behavior by using the
It currently supports two language detection libraries out of the box: fastText
and langdetect, and allows you to implement your own custom language detection.
For short strings the language detection could fail, so it’s highly recommended to use
Language detection with fastText.
Import the fastText wrapper and pass it as
>>> from dateparser.custom_language_detection.fasttext import detect_languages >>> dateparser.parse('12/12/12', detect_languages_function=detect_languages)
The fastText integration currently supports the large and the small models.
Find more information about fasttext models.
You can download your model of choice using
Downloading small model:
>>> dateparser-download --fasttext small
Downloading large model:
>>> dateparser-download --fasttext large
Deleting all cached models:
>>> dateparser-download --clear_cache
If no model has been downloaded, the fastText wrapper downloads and uses the small model by default.
Language detection with langdetect.
Import the langdetect wrapper and pass it as
>>> from dateparser.custom_language_detection.langdetect import detect_languages >>> dateparser.parse('12/12/12', detect_languages_function=detect_languages)
From some tests we did, we recommend to use
fastText for faster and more accurate results.
dateparser allows the integration of any library to detect languages by
wrapping that library in a function that accepts 2 parameters,
confidence_threshold, and returns a list of the detected language codes in
ISO 639 standards.
Wrapper for boilerplate for implementing custom language detections:
def detect_languages(text, confidence_threshold): """ Takes 2 parameters, `text` and `confidence_threshold`, and returns a list of `languages codes`. * `text` is the input string whose language needs to be detected. * `confidence_threshold` is a number between 0 and 1 that indicates the minimum confidence required for language matches. For language detection libraries that, for each language, indicate how confident they are that the language matches the input text, you should filter out languages with a confidence lower than this value (adjusted, if needed, to the confidence range of the target library). This value comes from the dateparser setting `LANGUAGE_DETECTION_CONFIDENCE_THRESHOLD`. The result must be a list of languages codes (strings). """ # here you can apply your own logic return language_codes