Fully customizable language detection for spaCy pipeline
Project description
Here is spacy_language_detection
Spacy_language_detection is a fully customizable language detection for spaCy pipeline forked from spacy-langdetect in order to fix the seed problem (see this issue) and to update it with spaCy 3.0.
Use spacy_language_detection to
- Detect the language of a document,
- Detect the language of the sentences of a document.
Installation
pip install spacy-language-detection
Basic Usage
Out of the box, under the hood, it uses langdetect to detect languages on spaCy's Doc and Span objects.
Here is how to use it for spaCy 3.0 see here for an example with spaCy 2.0.
import spacy
from spacy.language import Language
from spacy_language_detection import LanguageDetector
def get_lang_detector(nlp, name):
return LanguageDetector(seed=42) # We use the seed 42
nlp_model = spacy.load("en_core_web_sm")
Language.factory("language_detector", func=get_lang_detector)
nlp_model.add_pipe('language_detector', last=True)
# Document level language detection
job_title = "Senior NLP Research Engineer"
doc = nlp_model(job_title)
language = doc._.language
print(language)
# Sentence level language detection
text = "This is English text. Er lebt mit seinen Eltern und seiner Schwester in Berlin. Yo me divierto todos los días en el parque. Je m'appelle Angélica Summer, j'ai 12 ans et je suis canadienne."
doc = nlp_model(text)
for i, sent in enumerate(doc.sents):
print(sent, sent._.language)
Using your own language detector
Suppose you are not happy with the accuracy of the out-of-the-box language detector, or you have your own language
detector, which you want to use with a spaCy pipeline. How do you do it? That's where the language_detection_function
argument comes in. The function takes in a spaCy Doc or Span object and can return any Python object which is stored
in doc._.language
and span._.language
. For example, let's say you want to
use googletrans as your language detection module:
import spacy
from spacy.tokens import Doc, Span
from spacy_language_detection import LanguageDetector
# install using pip install googletrans
from googletrans import Translator
nlp = spacy.load("en")
def custom_detection_function(spacy_object):
# Custom detection function should take a spaCy Doc or a Span
assert isinstance(spacy_object, Doc) or isinstance(
spacy_object, Span), "spacy_object must be a spacy Doc or Span object but it is a {}".format(type(spacy_object))
detection = Translator().detect(spacy_object.text)
return {'language': detection.lang, 'score': detection.confidence}
def get_lang_detector(nlp, name):
return LanguageDetector(language_detection_function=custom_detection_function, seed=42) # We use the seed 42
nlp_model = spacy.load("en_core_web_sm")
Language.factory("language_detector", func=get_lang_detector)
nlp_model.add_pipe('language_detector', last=True)
text = "This is English text. Er lebt mit seinen Eltern und seiner Schwester in Berlin. Yo me divierto todos los días en el parque. Je m'appelle Angélica Summer, j'ai 12 ans et je suis canadienne."
# Document level language detection
doc = nlp_model(text)
language = doc._.language
print(language)
# Sentence level language detection
text = "This is English text. Er lebt mit seinen Eltern und seiner Schwester in Berlin. Yo me divierto todos los días en el parque. Je m'appelle Angélica Summer, j'ai 12 ans et je suis canadienne."
doc = nlp_model(text)
for i, sent in enumerate(doc.sents):
print(sent, sent._.language)
Similarly, you can also use pycld2 and other language detectors with spaCy.
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