# Multilingual Text Classification > *Out of the Box Packages > UiPath Language Analysis > MultiLingualTextClassification* *Out of the Box Packages > UiPath Language Analysis > MultiLingualTextClassification* This is a generic, retrainable model for text classification. This ML Package must be trained, and if deployed without training first, the deployment will fail with an error stating that the model is not trained. It is based on BERT, a self-supervised method for pretraining natural language processing systems. A GPU is recommended especially during training. A GPU delivers ~5-10x improvement in speed. ## Languages This multilingual model supports the languages from the following list. These languages were chosen because they are the top 100 languages with the largest Wikipedias: * Afrikaans * Albanian * Arabic * Aragonese * Armenian * Asturian * Azerbaijani * Bashkir * Basque * Bavarian * Belarusian * Bengali * Bishnupriya Manipuri * Bosnian * Breton * Bulgarian * Burmese * Catalan * Cebuano * Chechen * Chinese (Simplified) * Chinese (Traditional) * Chuvash * Croatian * Czech * Danish * Dutch * English * Estonian * Finnish * French * Galician * Georgian * German * Greek * Gujarati * Haitian * Hebrew * Hindi * Hungarian * Icelandic * Ido * Indonesian * Irish * Italian * Japanese * Javanese * Kannada * Kazakh * Kirghiz * Korean * Latin * Latvian * Lithuanian * Lombard * Low Saxon * Luxembourgish * Macedonian * Malagasy * Malay * Malayalam * Marathi * Minangkabau * Nepali * Newar * Norwegian (Bokmal) * Norwegian (Nynorsk) * Occitan * Persian (Farsi) * Piedmontese * Polish * Portuguese * Punjabi * Romanian * Russian * Scots * Serbian * Serbo-Croatian * Sicilian * Slovak * Slovenian * South Azerbaijani * Spanish * Sundanese * Swahili * Swedish * Tagalog * Tajik * Tamil * Tatar * Telugu * Thai * Turkish * Ukrainian * Urdu * Uzbek * Vietnamese * Volapük * Waray-Waray * Welsh * West Frisian * Western Punjabi * Yoruba ## Model details ### Input type JSON ### Input description Text to be classified as String: 'I loved this movie.' ### Output description JSON with predicted class name, associated confidence on that class prediction (between 0-1). Example: ``` { "prediction": "Positive", "confidence": 0.9422031841278076 } ``` ### Recommend GPU By default, a GPU is recommended. ### Training enabled By default, training is enabled. ## Pipelines All three types of pipelines (Full Training, Training, and Evaluation) are supported by this package. For most use cases, no parameters need to be specified, the model is using advanced techniques to find a performant model. In subsequent trainings after the first, the model uses incremental learning (that is, the previously trained version will be used, at the end of a Training Run). ### Dataset format Three options are available to structure your dataset for this model: JSON, CSV and **AI Center** JSON format (this is also the export format of the labelling tool. The model will read all CSV and JSON files in the specified directory. For every format, the model expects two columns or two properties, **dataset.input_column_name** and **dataset.target_column_name** by default. The names of these two columns and/or directories are configurable using environment variables. #### CSV file format Each CSV file can have any number of columns, but only two will be used by the model. Those columns are specified by the **dataset.input_column_name** and **dataset.target_column_name** parameters. Check the following sample and environment variables for a **CSV file format** example. ``` text, label I like this movie, 7 I hated the acting, 9 ``` The environment variables for the previous example would be as follows: * **dataset.input_format**: `auto` * **dataset.input_column_name**: `text` * **dataset.output_column_name**: `label` #### JSON file format Multiple datapoints could be a part of the same JSON file. Check the following sample and environment variables for a **JSON file format** example. ``` [ { "text": "I like this movie", "label": "7" }, { "text": "I hated the acting", "label": "9" } ] ``` The environment variables for the previous example would be as follows: * **dataset.input_format**: `auto` * **dataset.input_column_name**: `text` * **dataset.output_column_name**: `label` #### ai_center file format This is the deafult value of the environment variables which can be set, and this model will read all files in a provided directory with a `.json` extension. Check the following sample and environment variables for an **ai_center file format** example. ``` { "annotations": { "intent": { "to_name": "text", "choices": [ "TransactionIssue", "LoanIssue" ] }, "sentiment": { "to_name": "text", "choices": [ "Very Positive" ] }, "ner": { "to_name": "text", "labels": [ { "start_index": 37, "end_index": 47, "entity": "Stakeholder", "value": " Citi Bank" }, { "start_index": 51, "end_index": 61, "entity": "Date", "value": "07/19/2018" }, { "start_index": 114, "end_index": 118, "entity": "Amount", "value": "$500" }, { "start_index": 288, "end_index": 293, "entity": "Stakeholder", "value": " Citi" } ] } }, "data": { "cc": "", "to": "xyz@abc.com", "date": "1/29/2020 12:39:01 PM", "from": "abc@xyz.com", "text": "I opened my new checking account with Citi Bank in 07/19/2018 and met the requirements for the promotion offer of $500 . It has been more than 6 months and I have not received any bonus. I called the customer service several times in the past few months but no any response. I request the Citi honor its promotion offer as advertised." ``` For leveraging the previous sample JSON, the environment variables need to be set as follows: * **dataset.input_format**: `ai_center` * **dataset.input_column_name**: `data.text` * **dataset.output_column_name**: `annotations.intent.choices` ### Training on GPU or CPU You can use either GPU or CPU for training. We recommend using GPU since it's faster. ### Environment variables * **dataset.input_column_name** + The name of the input column containing the text. + Default value is `data.text`. + Make sure that this variable is configured according to your input JSON or CSV file. * **dataset.target_column_name** + The name of the target column containing the text. + Default value is `annotations.intent.choices`. + Make sure that this variable is configured according to your input JSON or CSV file. * **dataset.input_format** + The input format of the training data. + Default value is `ai_center`. + Supported values are: `ai_center` or `auto`. + If `ai_center` is selected, only `JSON` files are supported. Make sure to also change the value of the **dataset.target_column_name** to `annotations.sentiment.choices` if `ai_center` is selected. + If `auto` is selected, both `CoNLL` and `JSON` files are supported. * **model.epochs** + The number of epochs. + Default value: `100`. ### Artifacts #### Confusion matrix ![docs image](https://dev-assets.cms.uipath.com/assets/images/ai-center/ai-center-docs-image-106691-2e942ecb-7d6f452b.webp) #### Classification report ``` precision recall f1-score support positive 0.94 0.94 0.94 10408 negative 0.93 0.93 0.93 9592 accuracy 0.94 20000 macro avg 0.94 0.94 0.94 20000 weighted avg 0.94 0.94 0.94 20000 ``` ### Data #### Evaluation CSV file This is a CSV file with predictions on the test set used for evaluation. ``` text,label,predict,confidence I like this movie, positive, positive, 0.99 I hated the acting, negative, negative, 0.98 ```