1. What does this library do?

Its task is simple: It tells you which language some provided textual data is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages.

2. Why does this library exist?

Language detection is often done as part of large machine learning frameworks or natural language processing applications. In cases where you don't need the full-fledged functionality of those systems or don't want to learn the ropes of those, a small flexible library comes in handy.

Python is widely used in natural language processing, so there are a couple of comprehensive open source libraries for this task, such as Google's CLD 2 and CLD 3, langid, fastText and langdetect. Unfortunately, except for the last one they have two major drawbacks:

1. Detection only works with quite lengthy text fragments. For very short text snippets such as Twitter messages, they do not provide adequate results.
2. The more languages take part in the decision process, the less accurate are the detection results.

Lingua aims at eliminating these problems. She nearly does not need any configuration and yields pretty accurate results on both long and short text, even on single words and phrases. She draws on both rule-based and statistical methods but does not use any dictionaries of words. She does not need a connection to any external API or service either. Once the library has been downloaded, it can be used completely offline.

3. Which languages are supported?

Compared to other language detection libraries, Lingua's focus is on quality over quantity, that is, getting detection right for a small set of languages first before adding new ones. Currently, the following 75 languages are supported:

• A
  • Afrikaans
  • Albanian
  • Arabic
  • Armenian
  • Azerbaijani
• B
  • Basque
  • Belarusian
  • Bengali
  • Norwegian Bokmal
  • Bosnian
  • Bulgarian
• C
  • Catalan
  • Chinese
  • Croatian
  • Czech
• D
  • Danish
  • Dutch
• E
  • English
  • Esperanto
  • Estonian
• F
  • Finnish
  • French
• G
  • Ganda
  • Georgian
  • German
  • Greek
  • Gujarati
• H
  • Hebrew
  • Hindi
  • Hungarian
• I
  • Icelandic
  • Indonesian
  • Irish
  • Italian
• J
  • Japanese
• K
  • Kazakh
  • Korean
• L
  • Latin
  • Latvian
  • Lithuanian
• M
  • Macedonian
  • Malay
  • Maori
  • Marathi
  • Mongolian
• N
  • Norwegian Nynorsk
• P
  • Persian
  • Polish
  • Portuguese
  • Punjabi
• R
  • Romanian
  • Russian
• S
  • Serbian
  • Shona
  • Slovak
  • Slovene
  • Somali
  • Sotho
  • Spanish
  • Swahili
  • Swedish
• T
  • Tagalog
  • Tamil
  • Telugu
  • Thai
  • Tsonga
  • Tswana
  • Turkish
• U
  • Ukrainian
  • Urdu
• V
  • Vietnamese
• W
  • Welsh
• X
  • Xhosa
• Y
  • Yoruba
• Z
  • Zulu

4. How good is it?

Lingua is able to report accuracy statistics for some bundled test data available for each supported language. The test data for each language is split into three parts:

1. a list of single words with a minimum length of 5 characters
2. a list of word pairs with a minimum length of 10 characters
3. a list of complete grammatical sentences of various lengths

Both the language models and the test data have been created from separate documents of the Wortschatz corpora offered by Leipzig University, Germany. Data crawled from various news websites have been used for training, each corpus comprising one million sentences. For testing, corpora made of arbitrarily chosen websites have been used, each comprising ten thousand sentences. From each test corpus, a random unsorted subset of 1000 single words, 1000 word pairs and 1000 sentences has been extracted, respectively.

Given the generated test data, I have compared the detection results of Lingua, fastText, langdetect, langid, CLD 2 and CLD 3 running over the data of Lingua's supported 75 languages. Languages that are not supported by the other detectors are simply ignored for them during the detection process.

Each of the following sections contains two plots. The bar plot shows the detailed accuracy results for each supported language. The box plot illustrates the distributions of the accuracy values for each classifier. The boxes themselves represent the areas which the middle 50 % of data lie within. Within the colored boxes, the horizontal lines mark the median of the distributions.

4.1 Single word detection

Bar plot

4.2 Word pair detection

Bar plot

4.3 Sentence detection

Bar plot

4.4 Average detection

Bar plot

4.5 Mean, median and standard deviation

The table below shows detailed statistics for each language and classifier including mean, median and standard deviation.

Open table

Language	Average							Single Words							Word Pairs							Sentences
	Lingua (high accuracy mode)	Lingua (low accuracy mode)	Langdetect	FastText	Langid	CLD3	CLD2	Lingua (high accuracy mode)	Lingua (low accuracy mode)	Langdetect	FastText	Langid	CLD3	CLD2	Lingua (high accuracy mode)	Lingua (low accuracy mode)	Langdetect	FastText	Langid	CLD3	CLD2	Lingua (high accuracy mode)	Lingua (low accuracy mode)	Langdetect	FastText	Langid	CLD3	CLD2
Afrikaans	78	64	67	36	30	55	55	57	38	38	11	1	22	13	80	62	65	23	10	46	56	96	93	98	74	80	98	96
Albanian	88	80	79	66	65	55	65	68	55	53	35	33	18	18	95	87	84	66	63	48	77	100	99	100	98	98	98	99
Arabic	99	95	97	96	91	90	67	97	89	93	89	84	79	19	99	96	98	98	90	92	82	100	99	100	100	98	100	99
Armenian	100	100	-	100	94	99	100	100	100	-	100	83	100	100	100	100	-	100	99	100	100	100	100	-	100	100	97	100
Azerbaijani	89	82	-	78	68	81	72	78	71	-	57	36	62	34	92	78	-	80	69	82	82	99	96	-	98	98	99	99
Basque	83	74	-	71	52	62	61	71	56	-	44	18	33	23	87	76	-	70	52	62	69	92	91	-	100	86	92	91
Belarusian	97	92	-	85	85	84	76	91	80	-	69	69	67	42	99	95	-	88	87	86	87	100	100	-	98	99	100	99
Bengali	100	100	100	98	92	99	63	100	100	100	94	92	98	19	100	100	100	99	88	99	69	100	100	100	100	97	99	99
Bokmal	58	49	-	-	13	-	-	38	27	-	-	3	-	-	59	47	-	-	12	-	-	75	74	-	-	23	-	-
Bosnian	33	29	-	9	5	33	19	28	22	-	9	2	19	4	32	29	-	10	4	28	15	39	36	-	8	8	52	36
Bulgarian	87	78	72	78	67	70	66	71	57	51	56	46	45	32	91	81	68	81	62	66	72	99	97	96	99	93	98	93
Catalan	70	58	54	57	38	48	38	50	33	25	33	5	19	4	74	60	51	57	29	42	30	86	81	86	83	81	84	79
Chinese	100	100	64	71	96	92	33	100	100	39	46	90	92	-	100	100	55	68	97	83	2	100	100	97	100	100	100	98
Croatian	72	59	73	47	48	42	51	53	36	50	28	16	26	34	74	57	70	42	38	42	47	90	85	97	72	90	58	73
Czech	80	71	71	76	66	64	74	65	54	51	58	44	39	50	84	72	72	79	69	65	80	90	87	88	92	86	88	91
Danish	81	70	70	62	60	58	59	61	45	50	35	33	26	27	84	70	68	57	61	54	56	98	95	93	95	86	95	94
Dutch	77	64	58	78	64	58	47	55	36	27	55	34	29	11	81	61	49	81	61	47	42	96	94	98	100	98	97	90
English	81	62	60	96	85	54	56	55	29	23	90	84	22	12	89	62	59	98	71	44	55	99	96	99	100	99	97	100
Esperanto	83	66	-	76	44	57	50	67	44	-	51	5	22	7	85	61	-	79	30	51	46	98	92	-	100	96	98	98
Estonian	92	83	82	73	67	70	65	80	62	62	50	37	41	24	96	88	86	73	67	69	73	100	99	100	96	98	99	99
Finnish	96	91	93	92	83	80	77	90	77	84	82	62	58	44	98	95	95	96	88	84	89	100	100	100	100	100	99	98
French	89	77	75	83	71	55	46	74	52	48	62	42	22	12	95	83	78	86	74	49	48	99	97	99	99	98	94	80
Ganda	92	84	-	-	-	-	61	80	65	-	-	-	-	23	95	87	-	-	-	-	62	100	100	-	-	-	-	99
Georgian	100	100	-	99	99	98	100	100	100	-	97	97	99	100	100	100	-	99	100	100	100	100	100	-	100	100	96	100
German	89	80	73	89	81	66	64	74	57	50	76	61	40	27	94	84	70	93	81	62	66	100	99	100	100	100	98	98
Greek	100	100	100	99	100	100	100	100	100	100	98	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100
Gujarati	100	100	100	100	100	100	100	100	100	100	99	100	99	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100
Hebrew	100	100	100	100	100	-	-	100	100	100	99	100	-	-	100	100	100	100	100	-	-	100	100	100	100	100	-	-
Hindi	73	33	67	87	60	58	77	61	11	44	74	41	34	56	64	20	59	88	47	45	76	93	67	99	99	92	95	99
Hungarian	95	90	88	92	83	76	75	87	77	74	80	64	53	41	98	94	91	96	86	76	85	100	100	100	100	100	99	100
Icelandic	93	88	-	65	66	71	66	83	72	-	39	33	42	26	97	92	-	57	66	70	73	100	99	-	98	99	99	99
Indonesian	62	47	80	69	51	46	62	41	25	56	43	16	26	36	62	45	85	68	54	45	63	82	71	100	95	82	66	88
Irish	91	85	-	60	63	67	66	82	70	-	35	28	42	29	94	90	-	57	64	66	78	96	95	-	89	97	94	92
Italian	87	71	76	89	66	62	44	69	42	50	74	28	31	7	92	74	80	92	70	57	32	100	98	99	100	100	98	93
Japanese	100	100	100	87	86	98	33	100	100	99	72	61	97	-	100	100	100	89	96	96	-	100	100	100	100	100	100	100
Kazakh	92	90	-	88	80	82	77	80	78	-	72	67	62	43	97	93	-	90	78	83	88	99	99	-	100	96	99	99
Korean	100	100	100	99	100	99	100	100	100	100	98	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	98	100
Latin	87	73	-	50	21	62	46	72	49	-	24	-	44	9	92	76	-	41	2	58	42	97	93	-	85	61	83	88
Latvian	93	87	89	82	83	75	72	85	75	76	65	64	51	33	97	90	91	83	86	77	84	99	97	99	97	98	98	98
Lithuanian	95	88	87	81	80	72	70	87	76	71	61	58	42	30	98	89	91	83	85	75	82	100	98	100	99	99	99	99
Macedonian	83	72	86	74	51	60	60	64	52	71	51	15	30	27	86	70	88	72	44	54	70	99	95	100	100	94	97	84
Malay	30	31	-	15	11	22	18	25	22	-	14	2	11	9	36	36	-	19	9	22	22	28	35	-	12	22	34	23
Maori	92	83	-	-	-	52	61	84	64	-	-	-	22	12	92	88	-	-	-	43	72	99	98	-	-	-	91	98
Marathi	85	41	88	80	80	84	83	74	20	76	61	70	69	65	84	30	90	81	79	84	86	96	72	98	99	91	98	99
Mongolian	97	95	-	81	86	83	78	92	89	-	59	68	63	43	99	98	-	86	90	87	92	99	99	-	98	99	99	100
Nynorsk	65	52	-	29	32	-	54	41	25	-	8	5	-	18	65	49	-	18	16	-	50	90	81	-	61	75	-	93
Persian	90	80	81	90	92	76	61	77	62	64	79	83	57	13	93	80	80	92	94	70	72	100	98	99	100	100	99	99
Polish	95	90	89	92	89	77	75	86	77	75	80	73	51	38	98	93	93	97	93	80	87	100	99	100	100	100	99	99
Portuguese	81	69	61	73	54	53	54	58	42	30	47	19	21	20	85	69	55	71	44	40	48	98	96	98	99	98	97	94
Punjabi	100	100	100	100	100	100	100	100	100	100	99	100	99	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100
Romanian	87	72	77	64	61	53	54	69	49	56	38	31	24	11	92	74	78	60	60	48	53	99	94	97	95	92	88	96
Russian	89	79	84	94	75	71	60	76	60	70	86	60	48	26	94	84	87	98	75	72	68	98	92	96	100	91	93	87
Serbian	88	78	-	76	64	78	69	74	62	-	54	39	63	29	90	80	-	76	63	75	78	99	91	-	98	89	95	99
Shona	90	81	-	-	-	76	65	77	56	-	-	-	51	24	95	86	-	-	-	79	71	100	100	-	-	-	99	99
Slovak	84	75	75	65	68	63	71	64	49	51	41	40	32	38	90	79	76	62	66	61	76	99	98	98	91	97	96	99
Slovene	82	67	73	59	63	63	48	61	39	47	32	33	29	8	87	68	72	54	61	60	42	98	93	98	90	95	99	92
Somali	92	85	90	24	-	69	70	82	64	75	4	-	38	27	95	91	94	15	-	70	83	100	100	100	52	-	100	99
Sotho	83	72	-	-	-	49	54	61	43	-	-	-	15	13	88	75	-	-	-	33	54	99	97	-	-	-	98	95
Spanish	70	56	57	74	65	48	43	44	25	26	51	37	16	12	69	49	47	72	59	32	34	97	94	97	100	98	96	85
Swahili	79	70	73	41	42	57	57	58	43	47	7	3	25	16	82	69	73	24	24	49	59	98	97	99	92	98	98	97
Swedish	84	72	68	76	65	61	53	64	46	40	51	35	30	14	89	76	67	78	63	56	52	99	95	96	98	96	96	93
Tagalog	78	66	76	45	42	-	50	52	36	50	11	2	-	9	82	66	77	28	26	-	44	98	96	99	98	98	-	95
Tamil	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	99	100
Telugu	100	100	100	100	100	99	100	100	100	100	100	100	99	100	100	100	100	100	100	100	100	100	100	100	100	100	99	100
Thai	99	99	100	100	100	99	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	98	98	100	100	100	98	100
Tsonga	83	72	-	-	-	-	61	63	46	-	-	-	-	19	88	73	-	-	-	-	68	98	97	-	-	-	-	97
Tswana	82	71	-	-	-	-	56	62	44	-	-	-	-	17	86	73	-	-	-	-	57	98	96	-	-	-	-	94
Turkish	94	87	82	86	67	69	66	84	71	63	70	50	41	30	98	91	84	88	67	70	71	100	99	100	100	84	97	97
Ukrainian	92	86	83	91	76	81	77	84	75	66	78	54	62	46	97	92	84	94	77	83	88	95	93	98	100	96	98	99
Urdu	90	80	82	63	58	61	61	79	65	66	40	30	39	8	94	78	84	50	46	53	75	98	96	97	99	99	92	99
Vietnamese	91	87	93	89	86	66	63	79	76	81	71	65	26	-	94	88	98	97	93	74	90	99	98	100	100	100	99	100
Welsh	91	82	85	64	49	69	72	78	61	69	35	11	43	34	96	87	88	61	39	66	85	99	99	99	96	95	98	98
Xhosa	81	69	-	-	53	66	71	63	45	-	-	13	40	45	84	67	-	-	49	65	71	98	94	-	-	96	92	97
Yoruba	70	62	-	8	-	15	37	45	33	-	1	-	5	1	71	61	-	1	-	11	22	96	93	-	21	-	28	88
Zulu	80	71	-	-	6	63	54	62	45	-	-	0	35	18	83	72	-	-	6	63	51	97	95	-	-	11	92	93
Mean	86	77	82	74	68	69	65	74	61	65	58	48	48	34	89	78	82	74	65	67	68	96	93	98	92	90	93	94
Median	89.0	80.0	82.0	78.0	67.0	68.0	63.0	74.0	57.0	63.5	57.5	41.5	41.0	26.5	93.0	81.0	84.0	81.0	67.0	66.0	71.5	99.0	97.0	99.0	99.0	98.0	98.0	98.0
Standard Deviation	13.36	17.29	13.37	23.07	24.61	19.04	18.57	18.75	24.86	23.57	28.52	32.33	27.86	28.74	13.51	18.99	15.64	26.45	28.5	21.83	22.7	11.26	11.94	2.79	19.46	20.21	13.95	12.25

5. Why is it better than other libraries?

Every language detector uses a probabilistic n-gram model trained on the character distribution in some training corpus. Most libraries only use n-grams of size 3 (trigrams) which is satisfactory for detecting the language of longer text fragments consisting of multiple sentences. For short phrases or single words, however, trigrams are not enough. The shorter the input text is, the less n-grams are available. The probabilities estimated from such few n-grams are not reliable. This is why Lingua makes use of n-grams of sizes 1 up to 5 which results in much more accurate prediction of the correct language.

A second important difference is that Lingua does not only use such a statistical model, but also a rule-based engine. This engine first determines the alphabet of the input text and searches for characters which are unique in one or more languages. If exactly one language can be reliably chosen this way, the statistical model is not necessary anymore. In any case, the rule-based engine filters out languages that do not satisfy the conditions of the input text. Only then, in a second step, the probabilistic n-gram model is taken into consideration. This makes sense because loading less language models means less memory consumption and better runtime performance.

In general, it is always a good idea to restrict the set of languages to be considered in the classification process using the respective api methods. If you know beforehand that certain languages are never to occur in an input text, do not let those take part in the classifcation process. The filtering mechanism of the rule-based engine is quite good, however, filtering based on your own knowledge of the input text is always preferable.

6. Test report generation

If you want to reproduce the accuracy results above, you can generate the test reports yourself for all classifiers and languages by executing:

poetry install --extras "fasttext langdetect langid gcld3 pycld2 pandas matplotlib seaborn"
poetry run python3 scripts/accuracy_reporter.py

For each detector and language, a test report file is then written into /accuracy-reports. As an example, here is the current output of the Lingua German report:

##### German #####

>>> Accuracy on average: 89.27%

>> Detection of 1000 single words (average length: 9 chars)
Accuracy: 74.20%
Erroneously classified as Dutch: 2.30%, Danish: 2.20%, English: 2.20%, Latin: 1.80%, Bokmal: 1.60%, Italian: 1.30%, Basque: 1.20%, Esperanto: 1.20%, French: 1.20%, Swedish: 0.90%, Afrikaans: 0.70%, Finnish: 0.60%, Nynorsk: 0.60%, Portuguese: 0.60%, Yoruba: 0.60%, Sotho: 0.50%, Tsonga: 0.50%, Welsh: 0.50%, Estonian: 0.40%, Irish: 0.40%, Polish: 0.40%, Spanish: 0.40%, Tswana: 0.40%, Albanian: 0.30%, Icelandic: 0.30%, Tagalog: 0.30%, Bosnian: 0.20%, Catalan: 0.20%, Croatian: 0.20%, Indonesian: 0.20%, Lithuanian: 0.20%, Romanian: 0.20%, Swahili: 0.20%, Zulu: 0.20%, Latvian: 0.10%, Malay: 0.10%, Maori: 0.10%, Slovak: 0.10%, Slovene: 0.10%, Somali: 0.10%, Turkish: 0.10%, Xhosa: 0.10%

>> Detection of 1000 word pairs (average length: 18 chars)
Accuracy: 93.90%
Erroneously classified as Dutch: 0.90%, Latin: 0.90%, English: 0.70%, Swedish: 0.60%, Danish: 0.50%, French: 0.40%, Bokmal: 0.30%, Irish: 0.20%, Tagalog: 0.20%, Tsonga: 0.20%, Afrikaans: 0.10%, Esperanto: 0.10%, Estonian: 0.10%, Finnish: 0.10%, Italian: 0.10%, Maori: 0.10%, Nynorsk: 0.10%, Somali: 0.10%, Swahili: 0.10%, Turkish: 0.10%, Welsh: 0.10%, Zulu: 0.10%

>> Detection of 1000 sentences (average length: 111 chars)
Accuracy: 99.70%
Erroneously classified as Dutch: 0.20%, Latin: 0.10%

7. How to add it to your project?

Lingua is available in the Python Package Index and can be installed with:

pip install lingua-language-detector

8. How to build?

Lingua requires Python >= 3.8 and uses Poetry for packaging and dependency management. You need to install it first if you have not done so yet. Afterwards, clone the repository and install the project dependencies:

git clone https://github.com/pemistahl/lingua-py.git
cd lingua-py
poetry install

The library makes uses of type annotations which allow for static type checking with Mypy. Run the following command for checking the types:

poetry run mypy

The source code is accompanied by an extensive unit test suite. To run the tests, simply say:

poetry run pytest

9. How to use?

9.1 Basic usage

>>> from lingua import Language, LanguageDetectorBuilder
>>> languages = [Language.ENGLISH, Language.FRENCH, Language.GERMAN, Language.SPANISH]
>>> detector = LanguageDetectorBuilder.from_languages(*languages).build()
>>> detector.detect_language_of("languages are awesome")
Language.ENGLISH

9.2 Minimum relative distance

By default, Lingua returns the most likely language for a given input text. However, there are certain words that are spelled the same in more than one language. The word prologue, for instance, is both a valid English and French word. Lingua would output either English or French which might be wrong in the given context. For cases like that, it is possible to specify a minimum relative distance that the logarithmized and summed up probabilities for each possible language have to satisfy. It can be stated in the following way:

>>> from lingua import Language, LanguageDetectorBuilder
>>> languages = [Language.ENGLISH, Language.FRENCH, Language.GERMAN, Language.SPANISH]
>>> detector = LanguageDetectorBuilder.from_languages(*languages)\
.with_minimum_relative_distance(0.25)\
.build()
>>> print(detector.detect_language_of("languages are awesome"))
None

Be aware that the distance between the language probabilities is dependent on the length of the input text. The longer the input text, the larger the distance between the languages. So if you want to classify very short text phrases, do not set the minimum relative distance too high. Otherwise, None will be returned most of the time as in the example above. This is the return value for cases where language detection is not reliably possible.

9.3 Confidence values

Knowing about the most likely language is nice but how reliable is the computed likelihood? And how less likely are the other examined languages in comparison to the most likely one? These questions can be answered as well:

>>> from lingua import Language, LanguageDetectorBuilder
>>> languages = [Language.ENGLISH, Language.FRENCH, Language.GERMAN, Language.SPANISH]
>>> detector = LanguageDetectorBuilder.from_languages(*languages).build()
>>> confidence_values = detector.compute_language_confidence_values("languages are awesome")
>>> for language, value in confidence_values:
...     print(f"{language.name}: {value:.2f}")
ENGLISH: 1.00
FRENCH: 0.79
GERMAN: 0.75
SPANISH: 0.70

In the example above, a list of all possible languages is returned, sorted by their confidence value in descending order. The values that the detector computes are part of a relative confidence metric, not of an absolute one. Each value is a number between 0.0 and 1.0. The most likely language is always returned with value 1.0. All other languages get values assigned which are lower than 1.0, denoting how less likely those languages are in comparison to the most likely language.

The list returned by this method does not necessarily contain all languages which this LanguageDetector instance was built from. If the rule-based engine decides that a specific language is truly impossible, then it will not be part of the returned list. Likewise, if no ngram probabilities can be found within the detector's languages for the given input text, the returned list will be empty. The confidence value for each language not being part of the returned list is assumed to be 0.0.

9.4 Eager loading versus lazy loading

By default, Lingua uses lazy-loading to load only those language models on demand which are considered relevant by the rule-based filter engine. For web services, for instance, it is rather beneficial to preload all language models into memory to avoid unexpected latency while waiting for the service response. If you want to enable the eager-loading mode, you can do it like this:

LanguageDetectorBuilder.from_all_languages().with_preloaded_language_models().build()

Multiple instances of LanguageDetector share the same language models in memory which are accessed asynchronously by the instances.

9.5 Low accuracy mode versus high accuracy mode

Lingua's high detection accuracy comes at the cost of being noticeably slower than other language detectors. The large language models also consume significant amounts of memory. These requirements might not be feasible for systems running low on resources. If you want to classify mostly long texts or need to save resources, you can enable a low accuracy mode that loads only a small subset of the language models into memory:

LanguageDetectorBuilder.from_all_languages().with_low_accuracy_mode().build()

The downside of this approach is that detection accuracy for short texts consisting of less than 120 characters will drop significantly. However, detection accuracy for texts which are longer than 120 characters will remain mostly unaffected.

In high accuracy mode (the default), the language detector consumes approximately 800 MB of memory if all language models are loaded. In low accuracy mode, memory consumption is reduced to approximately 60 MB.

An alternative for a smaller memory footprint and faster performance is to reduce the set of languages when building the language detector. In most cases, it is not advisable to build the detector from all supported languages. When you have knowledge about the texts you want to classify you can almost always rule out certain languages as impossible or unlikely to occur.

9.6 Methods to build the LanguageDetector

There might be classification tasks where you know beforehand that your language data is definitely not written in Latin, for instance. The detection accuracy can become better in such cases if you exclude certain languages from the decision process or just explicitly include relevant languages:

from lingua import LanguageDetectorBuilder, Language, IsoCode639_1, IsoCode639_3

# Include all languages available in the library.
LanguageDetectorBuilder.from_all_languages()

# Include only languages that are not yet extinct (= currently excludes Latin).
LanguageDetectorBuilder.from_all_spoken_languages()

# Include only languages written with Cyrillic script.
LanguageDetectorBuilder.from_all_languages_with_cyrillic_script()

# Exclude only the Spanish language from the decision algorithm.
LanguageDetectorBuilder.from_all_languages_without(Language.SPANISH)

# Only decide between English and German.
LanguageDetectorBuilder.from_languages(Language.ENGLISH, Language.GERMAN)

# Select languages by ISO 639-1 code.
LanguageDetectorBuilder.from_iso_codes_639_1(IsoCode639_1.EN, IsoCode639_1.DE)

# Select languages by ISO 639-3 code.
LanguageDetectorBuilder.from_iso_codes_639_3(IsoCode639_3.ENG, IsoCode639_3.DEU)

11. What's next for version 1.2.0?

Take a look at the planned issues.

12. Contributions

Any contributions to Lingua are very much appreciated. Please read the instructions in CONTRIBUTING.md for how to add new languages to the library.