10+ Best Python Library to Detect Language

Categories Python

One popular Python library for language detection is langdetect. It is a simple and straightforward library that provides accurate language detection capabilities.

langdetect

Here’s an example of how you can use it:

from langdetect import detect

text = "This is an example sentence."

language = detect(text)

print(language)Code language: Python (python)

The detect function takes a string as input and returns the detected language as a two-letter language code (e.g., “en” for English, “fr” for French). It uses a probabilistic model based on character n-grams to make the language prediction.

spaCy

Another widely used library is spaCy. Although its primary purpose is natural language processing, it also provides language detection capabilities. Here’s an example:

import spacy

nlp = spacy.load("xx_ent_wiki_sm")  # Load the language model for multi-language support

text = "This is an example sentence."

doc = nlp(text)

language = doc._.language["language"]

print(language)
Code language: Python (python)

In this example, we load the "xx_ent_wiki_sm" model from spaCy, which supports multiple languages. After processing the text, we can access the detected language from the doc._.language attribute.

Both langdetect and spaCy are popular and reliable libraries for language detection in Python. You can choose the one that best fits your requirements and preferences.

In addition to the libraries mentioned earlier, here are five more Python libraries commonly used for language detection:

TextBlob

TextBlob is a powerful library for natural language processing tasks and includes a language detection feature. It provides an easy-to-use interface and accurate language identification. Here’s an example:

from textblob import TextBlob

text = "This is an example sentence."

blob = TextBlob(text)

language = blob.detect_language()

print(language)Code language: Python (python)

cld2-cffi

cld2-cffi is a Python binding for Compact Language Detector 2 (CLD2), a library developed by Google. It is known for its high accuracy and supports over 80 languages. Here’s an example:

import cld2

text = "This is an example sentence."

_, _, details = cld2.detect(text)

language = details[0].language_code

print(language)Code language: Python (python)

fasttext

fasttext is a library developed by Facebook that includes language identification functionality. It is known for its fast execution speed and supports a wide range of languages. Here’s an example:

import fasttext

model = fasttext.load_model('lid.176.bin')  # Load the pre-trained language identification model

text = "This is an example sentence."

language = model.predict(text)[0][0].split('__')[-1]

print(language)Code language: Python (python)

pycld2

pycld2 is another Python binding for the Compact Language Detector 2 (CLD2) library. It offers language detection with good accuracy and supports a variety of languages. Here’s an example:

import pycld2

text = "This is an example sentence."

result = pycld2.detect(text)

language = result[2][0][1]

print(language)Code language: Python (python)

nltk

The Natural Language Toolkit (NLTK) is a comprehensive library for natural language processing tasks. Although it’s not primarily focused on language detection, it provides support for language identification using statistical models. Here’s an example:

import nltk

text = "This is an example sentence."

words = nltk.wordpunct_tokenize(text)

language = nltk.classify.textcat.detect_language(words)

print(language)
Code language: Python (python)

These are five additional libraries that you can consider for language detection in Python. Each has its own features, strengths, and trade-offs, so you can choose the one that best suits your specific requirements.

Polyglot

Polyglot is a multilingual natural language processing library that supports various tasks, including language detection. It offers support for over 130 languages and provides accurate language identification. Here’s an example:

from polyglot.detect import Detector

text = "This is an example sentence."

detector = Detector(text)

language = detector.language.code

print(language)
Code language: Python (python)

langid.py

langid.py is a library that provides language identification based on a combination of character n-grams and a probabilistic model. It supports a wide range of languages and offers fast language detection. Here’s an example:

import langid

text = "This is an example sentence."

language, confidence = langid.classify(text)

print(language)
Code language: Python (python)

pyGoogleTranslate

pyGoogleTranslate is a Python library that uses the Google Translate API for language detection. It is a simple and straightforward option for language identification. Here’s an example:

from googletrans import LANGUAGES

from pygoogletranslation import Translator

text = "This is an example sentence."

translator = Translator()

detected_language = translator.detect(text)

language = LANGUAGES.get(detected_language)

print(language)
Code language: Python (python)

Which NLP library is best?

The choice of the best NLP (Natural Language Processing) library depends on various factors, including your specific requirements, the complexity of the task, the available resources, and personal preferences.

Here are a few widely used and highly regarded NLP libraries in Python:

  • NLTK (Natural Language Toolkit): NLTK is one of the oldest and most popular libraries for NLP tasks in Python. It provides a wide range of tools and functionalities for tasks like tokenization, stemming, tagging, parsing, sentiment analysis, and more. NLTK also includes various corpora and pre-trained models.
  • spaCy: spaCy is a powerful and efficient NLP library designed for production-level use. It offers fast and accurate tokenization, named entity recognition, part-of-speech tagging, dependency parsing, and other NLP functionalities. spaCy is known for its performance and ease of use.
  • Gensim: Gensim is a library primarily focused on topic modeling and document similarity tasks. It provides implementations of popular algorithms such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Word2Vec. Gensim is efficient, scalable, and well-suited for working with large text corpora.
  • Transformers (Hugging Face): Transformers is a library developed by Hugging Face that provides state-of-the-art models for natural language understanding (NLU) and natural language generation (NLG). It includes pre-trained models for tasks like text classification, named entity recognition, question answering, and more. Transformers is built on the powerful Transformer architecture and is widely used for tasks involving contextualized word embeddings.
  • TextBlob: TextBlob is a user-friendly library built on top of NLTK. It provides a simple API for common NLP tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, and language translation. TextBlob is easy to use and suitable for quick prototyping and small-scale projects.

These are just a few examples, and there are several other NLP libraries available in Python. The best library for your specific use case will depend on the nature of your task, the required functionalities, performance considerations, and your familiarity with the library.

It’s recommended to explore the documentation, features, and community support of each library to make an informed decision.

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  • I am a middle python software engineer with a bachelor's degree in Software Engineering from Kharkiv National Aerospace University. My expertise lies in Python, Django, Flask, Docker, REST API, Odoo development, relational databases, and web development. I am passionate about creating efficient and scalable software solutions that drive innovation in the industry.

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