Classify text

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These NLP tasks enable you to identify the language of text and classify or label unstructured input text:

Language identification

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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

Further reading
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Text classification

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Text classification assigns the input text to one of multiple classes that best describe the text. The classes used depend on the model and the data set that was used to train it. Based on the number of classes, two main types of classification exist: binary classification, where the number of classes is exactly two, and multi-class classification, where the number of classes is more than two.

This task can help you analyze text for markers of positive or negative sentiment or classify text into various topics. For example, you might use a trained model to perform sentiment analysis and determine whether the following text is "POSITIVE" or "NEGATIVE":

...
{
    "input_text": "This was the best movie I’ve seen in the last decade!"
}
...

Likewise, you might use a trained model to perform multi-class classification and determine whether the following text is a news topic related to "SPORTS", "BUSINESS", "LOCAL", or "ENTERTAINMENT":

...
{
    "input_text": "The Blue Jays played their final game in Toronto last night and came out with a win over the Yankees, highlighting just how far the team has come this season."
}
...

Zero-shot text classification

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The zero-shot classification task offers the ability to classify text without training a model on a specific set of classes. Instead, you provide the classes when you deploy the model or at inference time. It uses a model trained on a large data set that has gained a general language understanding and asks the model how well the labels you provided fit with your text.

This task enables you to analyze and classify your input text even when you don’t have sufficient training data to train a text classification model.

For example, you might want to perform multi-class classification and determine whether a news topic is related to "SPORTS", "BUSINESS", "LOCAL", or "ENTERTAINMENT". However, in this case the model is not trained specifically for news classification; instead, the possible labels are provided together with the input text at inference time:

...
{
    "input_text": "The S&P 500 gained a meager 12 points in the day’s trading. Trade volumes remain consistent with those of the past week while investors await word from the Fed about possible rate increases.",
    "labels": ["SPORTS", "BUSINESS", "LOCAL", "ENTERTAINMENT"]
}
...

The task returns the following result:

...
{
    "result": "BUSINESS"
}
...

You can use the same model to perform inference with different classes, such as:

...
{
    "input_text": "Hello support team. I’m writing to inquire about the possibility of sending my broadband router in for repairs. The internet is really slow and the router keeps rebooting! It’s a big problem because I’m in the middle of binge-watching The Mandalorian!",
    "labels": ["urgent", "internet", "phone", "cable", "mobile", "tv"]
}
...

The task returns the following result:

...
{
    "result": ["urgent", "internet", "tv"]
}
...

Since you can adjust the labels while you perform inference, this type of task is exceptionally flexible. If you are consistently using the same labels, however, it might be better to use a fine-tuned text classification model.