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Multilingual Text Classification
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.
This multilingual model supports the languages listed below. 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
JSON with predicted class name, associated confidence on that class prediction (between 0-1).
Example:
{
"prediction": "Positive",
"confidence": 0.9422031841278076
}{
"prediction": "Positive",
"confidence": 0.9422031841278076
}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).
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, 9text, label
I like this movie, 7
I hated the acting, 9The 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"
}
][
{
"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
.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": "[email protected]",
"date": "1/29/2020 12:39:01 PM",
"from": "[email protected]",
"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."{
"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": "[email protected]",
"date": "1/29/2020 12:39:01 PM",
"from": "[email protected]",
"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
You can use either GPU or CPU for training. We recommend using GPU since it's faster.
- 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_centerorauto. - If
ai_centeris selected, onlyJSONfiles are supported. Make sure to also change the value of the dataset.target_column_name toannotations.sentiment.choicesifai_centeris selected. - If
autois selected, bothCoNLLandJSONfiles are supported.
- model.epochs
- The number of epochs.
- Default value:
100.
Confusion matrix
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 20000precision 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 20000Evaluation 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, 0.98text,label,predict,confidence
I like this movie, positive, positive, 0.99
I hated the acting, negative, negative, 0.98
