Subscribe

UiPath AI Center

UiPath AI Center

UiPath provides a number of machine learning capabilities out-of-the-box on AI Center. A notable example is Document Understanding. In addition, UiPath built and open-source models (serving-only and retrainable) are continuously added to AI Center.

The following packages are available in platform today :

Model

Category

Type

Contracts

UiPath Document Understanding

Pre Trained

Delivery Notes

UiPath Document Understanding

Custom Training

Document Classifier

UiPath Document Understanding

Custom Training

Document Understanding

UiPath Document Understanding

Custom Training

ID Cards

UiPath Document Understanding

Pre Trained and Fine Tunable

Invoices

UiPath Document Understanding

Pre Trained and Fine Tunable

Invoices Australia

UiPath Document Understanding

Pre Trained and Fine Tunable

Invoices China

UiPath Document Understanding

Pre Trained and Fine Tunable

Invoices India

UiPath Document Understanding

Pre Trained and Fine Tunable

Invoices Japan

UiPath Document Understanding

Pre Trained and Fine Tunable

Passports

UiPath Document Understanding

Pre Trained and Fine Tunable

Purchase Orders

UiPath Document Understanding

Pre Trained and Fine Tunable

Receipts

UiPath Document Understanding

Pre Trained and Fine Tunable

Remittance Advices

UiPath Document Understanding

Pre Trained and Fine Tunable

Utility Bills

UiPath Document Understanding

Pre Trained and Fine Tunable

W2

UiPath Document Understanding

Pre Trained and Fine Tunable

W9

UiPath Document Understanding

Pre Trained and Fine Tunable

Image Classification

UiPath Image Analysis

Custom Training

Custom Named Entity Recognition

UiPath Language Analysis

Custom Training

Light Text Classification

UiPath Language Analysis

Custom Training

Multilingual Text Classification

UiPath Language Analysis

Custom Training

Semantic Similarity

UiPath Language Analysis

Pre Trained

TM Analyzer Model

UiPath Task Mining

Custom Training

Image Moderation

Open-Source Packages - Image Analysis

Pre Trained

Object Detection

Open-Source Packages - Image Analysis

Pre Trained and Custom Training

English Text Classification

Open-Source Packages - Language Analysis

Custom Training

French Text Classification

Open-Source Packages - Language Analysis

Custom Training

Japanese Text Classification

Open-Source Packages - Language Analysis

Custom Training

Language Detection

Open-Source Packages - Language Analysis

Pre Trained

Named Entity Recognition

Open-Source Packages - Language Analysis

Pre Trained

Sentiment Analysis

Open-Source Packages - Language Analysis

Pre Trained

Text Classification

Open-Source Packages - Language Analysis

Custom Training

Question Answering

Open-Source Packages - Language Comprehension

Pre Trained

Semantic Similarity

Open-Source Packages - Language Comprehension

Pre Trained

Text Summarization

Open-Source Packages - Language Comprehension

Pre Trained

English To French Translation

Open-Source Packages - Language Translation

Pre Trained

English To German Translation

Open-Source Packages - Language Translation

Pre Trained

English To Russian Translation

Open-Source Packages - Language Translation

Pre Trained

German To English Translation

Open-Source Packages - Language Translation

Pre Trained

Russian To English Translation

Open-Source Packages - Language Translation

Pre Trained

TPOT Tabular Classification

Open-Source Packages - Tabular Data

Custom Training

TPOT Tabular Regression

Open-Source Packages - Tabular Data

Custom Training

XGBoost Tabular Classification

Open-Source Packages - Tabular Data

Custom Training

XGBoost Tabular Regression

Open-Source Packages - Tabular Data

Custom Training

Ready-to-Deploy

Example packages that can be immediately deployed and added to a RPA workflow, more can be found in the product

Sentiment Analysis


This model predicts the sentiment of a text in the English Language. It was open-sourced by Facebook Research. Possible predictions are one of "Very Negative", "Negative", "Neutral", "Positive", "Very Positive". The model was trained on Amazon product review data thus, the model predictions may have some unexpected results for different data distributions. A common use case is to route unstructured language content (e.g. emails) based on the sentiment of the text.
It is based on the research paper "Bag of Tricks for Efficient Text Classification" by Joulin, et al.

Question Answering

This model predicts the answer to a question of a text in the English Language based on some paragraph context. It was open-sourced by ONNX. A common use case is in KYC or processing financial reports where a common question can be applied to a standard set of semi-structured documents. It is based on the state-of-the-art BERT (Bidirectional Encoder Representations from Transformers). The model applies Transformers, a popular attention model, to language modeling to produce an encoding of the input and then trains on the task of question answering.
It is based on the research paper “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”.

Language Identification

This model predicts the language of a text input. Possible predictions are one of the following 176 languages:

Languages

af als am an ar arz as ast av az azb ba bar bcl be bg bh bn bo bpy br bs bxr ca cbk ce ceb ckb co cs cv cy da de diq dsb dty dv el eml en eo es et eu fa fi fr frr fy ga gd gl gn gom gu gv he hi hif hr hsb ht hu hy ia id ie ilo io is it ja jbo jv ka kk km kn ko krc ku kv kw ky la lb lez li lmo lo lrc lt lv mai mg mhr min mk ml mn mr mrj ms mt mwl my myv mzn nah nap nds ne new nl nn no oc or os pa pam pfl pl pms pnb ps pt qu rm ro ru rue sa sah sc scn sco sd sh si sk sl so sq sr su sv sw ta te tg th tk tl tr tt tyv ug uk ur uz vec vep vi vls vo wa war wuu xal xmf yi yo yue zh

It was open-sourced by Facebook Research. The model was trained on data from Wikipedia, Tatoeba, and SETimes used under the Creative Commons Attribution-Share-Alike License 3.0. A common use case is to route unstructured language content (e.g. emails) to an appropriate responder based on the language of the text.
It is based on the research paper "Bag of Tricks for Efficient Text Classification" by Joulin, et al.

English To French

This is a Sequence-to-Sequence machine translation model that translates English to French. It was open-sourced by Facebook AI Research (FAIR).
It is based on the paper "Convolutional Sequence to Sequence Learning" by Gehring, et al.

English To German

This is a Sequence-to-Sequence machine translation model that translates English to German. It was open-sourced by Facebook AI Research (FAIR).
It is based on the paper "Facebook FAIR's WMT19 News Translation Submission" by Ng, et al.

German To English

This is a Sequence-to-Sequence machine translation model that translates English to Russian. It was open-sourced by Facebook AI Research (FAIR).
It is based on the paper "Facebook FAIR's WMT19 News Translation Submission" by Ng, et al.

English To Russian

This is a Sequence-to-Sequence machine translation model that translates English to German. It was open-sourced by Facebook AI Research (FAIR).
It is based on the paper "Facebook FAIR's WMT19 News Translation Submission" by Ng, et al.

Russian To English

This is a Sequence-to-Sequence machine translation model that translates English to Russian. It was open-sourced by Facebook AI Research (FAIR).
It is based on the paper "Facebook FAIR's WMT19 News Translation Submission" by Ng, et al.

NamedEntityRecognition

This model returns a list of entities recognized in text. The 18 types of named entities recognized use the same output class as in OntoNotes5 which is commonly used for benchmarking this task in academia. The model is based on the paper 'Approaching nested named entity recognition with parallel LSTM-CRFs' by Borchmann et al, 2018.
The 18 classes are the following:

Entity

Description

PERSON

People, including fictional.

NORP

Nationalities or religious or political groups.

FAC

Buildings, airports, highways, bridges, etc.

ORG

Companies, agencies, institutions, etc.

GPE

Countries, cities, states.

LOC

Non-GPE locations, mountain ranges, bodies of water.

PRODUCT

Objects, vehicles, foods, etc. (Not services.)

EVENT

Named hurricanes, battles, wars, sports events, etc.

WORK_OF_ART

Titles of books, songs, etc.

LAW

Named documents made into laws.

LANGUAGE

Any named language.

DATE

Absolute or relative dates or periods.

TIME

Times smaller than a day.

PERCENT

Percentage, including ”%“.

MONEY

Monetary values, including unit.

QUANTITY

Measurements, as of weight or distance.

ORDINAL

“first”, “second”, etc.

CARDINAL

Numerals that do not fall under another type.

Re-trainable

Example packages that can be trained by adding data to AI Center storage and starting a pipeline, more models can be found in the product.

English Text Classification

This is a generic, re-trainable model for English text classification. Common use cases are email classification, service ticket classification, custom sentiment analysis among others. See English Text Classification for more details.

French Text Classification

This is a generic, re-trainable model for French text classification. Common use cases are email classification, service ticket classification, custom sentiment analysis among others. See French Text Classification for more details.

Multi Lingual Text Classification

This is the preview version of a generic, retrainable model for text classification. It supports the top 100 Wikipedia languages listed here (https://docs.uipath.com/ai-fabric/v0/docs/multi-lingual-text-classification#languages). 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.

Custom Named Entity Recognition

This preview model allows you to bring your own dataset tagged with entities you want to extract. The training and evaluation datasets need to be in CoNLL format.

Tabular Classification AutoML - TPOT

This is a generic, re-trainable model for tabular (e.g. csv, excel) data classification. That is, given a table of columns and a target column, it will find a model for that data. See TPOT AutoML Classification for more details.

Tabular Classification - TPOT XGBoost

This is a generic, re-trainable model for tabular (e.g. csv, excel) data classification. That is, given a table of columns and a target column, it will find a model (based on XGBoost) for that data. See TPOT XGBoost Classification,

Updated 2 days ago


Overview


Suggested Edits are limited on API Reference Pages

You can only suggest edits to Markdown body content, but not to the API spec.