AI Center
2021.10
false
AI Center User Guide
Automation CloudAutomation SuiteStandalone
Last updated Jun 6, 2024

Out of the box packages

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 or open-source models (serving-only and retrainable) are continuously added to AI Center .

Note: When creating an ML Package in AI Center, it cannot be named using any python reserved keyword, such as class, break, from, finally, global, None, etc. Make sure to choose another name. The listed examples are not complete since package name is used for class <pkg-name> and import <pck-name>.

Using out of the box packages

You can create your own packages based on the ones provided in the Out of the box Packages section. By choosing a package from this list, a provided package is technically cloned, ready to be trained with the dataset provided by you.

To do so, follow the steps below:

  1. Create a dataset. For more information on how to build datasets. see Managing Datasets.
  2. Go to ML Packages > Out of the Box Packages and choose the needed package.
  3. Fill in the needed information:
    • Package name
    • Choose Package Version
    • Description
    • Input Description
    • Output Description
  4. Click Submit.

For more information on the information needed for each package, check the individual pages from this guide.

Open Source (OS) packages are ready-to-use packages provided by UiPath engineers through the Open Source Data Science Community. In order to be used within your workflows in Studio, you first have to deploy them as skills in AI Center , as presented below:



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.

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