Language identification

Language identification enables you to determine the language of text.

A language identification model is provided in your cluster, which you can use in an inference processor of an ingest pipeline by using its model ID (lang_ident_model_1). For an example, refer to Add NLP inference to ingest pipelines.

The longer the text passed into the language identification model, the more accurately the model can identify the language. It is fairly accurate on short samples (for example, 50 character-long streams) in certain languages, but languages that are similar to each other are harder to identify based on a short character stream. If there is no valid text from which the identity can be inferred, the model returns the special language code zxx. If you prefer to use a different default value, you can adjust your ingest pipeline to replace zxx predictions with your preferred value.

Language identification takes into account Unicode boundaries when the feature set is built. If the text has diacritical marks, then the model uses that information for identifying the language of the text. In certain cases, the model can detect the source language even if it is not written in the script that the language traditionally uses. These languages are marked in the supported languages table (see below) with the Latn subtag. Language identification supports Unicode input.

Supported languages

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The table below contains the ISO codes and the English names of the languages that language identification supports. If a language has a 2-letter ISO 639-1 code, the table contains that identifier. Otherwise, the 3-letter ISO 639-2 code is used. The Latn subtag indicates that the language is transliterated into Latin script.

Code	Language	Code	Language	Code	Language
af	Afrikaans	hr	Croatian	pa	Punjabi
am	Amharic	ht	Haitian	pl	Polish
ar	Arabic	hu	Hungarian	ps	Pashto
az	Azerbaijani	hy	Armenian	pt	Portuguese
be	Belarusian	id	Indonesian	ro	Romanian
bg	Bulgarian	ig	Igbo	ru	Russian
bg-Latn	Bulgarian	is	Icelandic	ru-Latn	Russian
bn	Bengali	it	Italian	sd	Sindhi
bs	Bosnian	iw	Hebrew	si	Sinhala
ca	Catalan	ja	Japanese	sk	Slovak
ceb	Cebuano	ja-Latn	Japanese	sl	Slovenian
co	Corsican	jv	Javanese	sm	Samoan
cs	Czech	ka	Georgian	sn	Shona
cy	Welsh	kk	Kazakh	so	Somali
da	Danish	km	Central Khmer	sq	Albanian
de	German	kn	Kannada	sr	Serbian
el	Greek, modern	ko	Korean	st	Southern Sotho
el-Latn	Greek, modern	ku	Kurdish	su	Sundanese
en	English	ky	Kirghiz	sv	Swedish
eo	Esperanto	la	Latin	sw	Swahili
es	Spanish, Castilian	lb	Luxembourgish	ta	Tamil
et	Estonian	lo	Lao	te	Telugu
eu	Basque	lt	Lithuanian	tg	Tajik
fa	Persian	lv	Latvian	th	Thai
fi	Finnish	mg	Malagasy	tr	Turkish
fil	Filipino	mi	Maori	uk	Ukrainian
fr	French	mk	Macedonian	ur	Urdu
fy	Western Frisian	ml	Malayalam	uz	Uzbek
ga	Irish	mn	Mongolian	vi	Vietnamese
gd	Gaelic	mr	Marathi	xh	Xhosa
gl	Galician	ms	Malay	yi	Yiddish
gu	Gujarati	mt	Maltese	yo	Yoruba
ha	Hausa	my	Burmese	zh	Chinese
haw	Hawaiian	ne	Nepali	zh-Latn	Chinese
hi	Hindi	nl	Dutch, Flemish	zu	Zulu
hi-Latn	Hindi	no	Norwegian
hmn	Hmong	ny	Chichewa

Example of language identification

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In the following example, we feed the language identification trained model a short Hungarian text that contains diacritics and a couple of English words. The model identifies the text correctly as Hungarian with high probability.

POST _ingest/pipeline/_simulate
{
   "pipeline":{
      "processors":[
         {
            "inference":{
               "model_id":"lang_ident_model_1", 
               "inference_config":{
                  "classification":{
                     "num_top_classes":5 
                  }
               },
               "field_map":{
               }
            }
         }
      ]
   },
   "docs":[
      {
         "_source":{ 
            "text":"Sziasztok! Ez egy rövid magyar szöveg. Nézzük, vajon sikerül-e azonosítania a language identification funkciónak? Annak ellenére is sikerülni fog, hogy a szöveg két angol szót is tartalmaz."
         }
      }
   ]
}

	ID of the language identification trained model.
	Specifies the number of languages to report by descending order of probability.
	The source object that contains the text to identify.

In the example above, the num_top_classes value indicates that only the top five languages (that is to say, the ones with the highest probability) are reported.

The request returns the following response:

{
  "docs" : [
    {
      "doc" : {
        "_index" : "_index",
        "_type" : "_doc",
        "_id" : "_id",
        "_source" : {
          "text" : "Sziasztok! Ez egy rövid magyar szöveg. Nézzük, vajon sikerül-e azonosítania a language identification funkciónak? Annak ellenére is sikerülni fog, hogy a szöveg két angol szót is tartalmaz.",
          "ml" : {
            "inference" : {
              "top_classes" : [ 
                {
                  "class_name" : "hu",
                  "class_probability" : 0.9999936063740517,
                  "class_score" : 0.9999936063740517
                },
                {
                  "class_name" : "lv",
                  "class_probability" : 2.5020248433413966E-6,
                  "class_score" : 2.5020248433413966E-6
                },
                {
                  "class_name" : "is",
                  "class_probability" : 1.0150420723037688E-6,
                  "class_score" : 1.0150420723037688E-6
                },
                {
                  "class_name" : "ga",
                  "class_probability" : 6.67935962773335E-7,
                  "class_score" : 6.67935962773335E-7
                },
                {
                  "class_name" : "tr",
                  "class_probability" : 5.591166324774555E-7,
                  "class_score" : 5.591166324774555E-7
                }
              ],
              "predicted_value" : "hu", 
              "model_id" : "lang_ident_model_1"
            }
          }
        },
        "_ingest" : {
          "timestamp" : "2020-01-22T14:25:14.644912Z"
        }
      }
    }
  ]
}

	Contains scores for the most probable languages.
	The ISO identifier of the language with the highest probability.

Language identification