ibm-research/WatsonxDocsQARetrieval huggingface dataset:
# Manually adding the project access token #

All projects have an authorization token that is used to access data assets, for example files and connections, and is used by platform APIs\. This token is called the project access token, or simply access token in the project user interface\. This project access token must be set in notebooks so that project and platform functions can access the project resources\.

When you load data to your notebook by clicking **Read data** on the Code snippets pane, selecting the asset and the load option, the project access token is added for you, if the generated code that is inserted uses project functions\.

However, when you use API functions in your notebook that require the project token, for example, if you're using `Wget` to access data by using the HTTP, HTTPS or FTP protocols, or the `ibm-watson-studio-lib` library, you must add the project access token to the notebook yourself\.

To add a project access token to a notebook if you are not using the generated code:

<!-- <ol> -->

1.  From the **Manage** tab, select **Access Control** and click **New access token** under **Access tokens**\. Only project administrators can create project access tokens\.
    
    Enter a name and select the access role. To enable using API functions in a notebook, the access token must have the Editor access role. An access token with Viewer access role enables read access only to a notebook.
2.  Add the project access token to a notebook by clicking **More > Insert project token** from the notebook action bar\.
    
    By running the inserted hidden code cell, a project object is created that you can use for functions in the `ibm-watson-studio-lib` library. For example to get the name of the current project run:
    
        project.get_name()
    
    For details on the available `ibm-watson-studio-lib` functions, see [Accessing project assets with ibm-watson-studio-lib](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/using-ibm-ws-lib.html).
    
    Note that a project administrator can revoke a project access token at any time. An access token has no expiration date and is valid until it is revoked.

<!-- </ol> -->

**Parent topic:**[Loading and accessing data in a notebook](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/load-and-access-data.html)

<!-- </article "role="article" "> -->
# Accessing project assets with ibm\-watson\-studio\-lib #

The `ibm-watson-studio-lib` library for Python and R contains a set of functions that help you to interact with IBM Watson Studio projects and project assets\. You can think of the library as a programmatical interface to a project\. Using the `ibm-watson-studio-lib` library, you can access project metadata and assets, including files and connections\. The library also contains functions that simplify fetching files associated with the project\.

## Next steps ##

<!-- <ul> -->

 *  Start using `ibm-watson-studio-lib` in new notebooks:
    
    <!-- <ul> -->
    
     *  [ibm-watson-studio-lib for Python](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ws-lib-python.html)
     *  [ibm-watson-studio-lib for R](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ws-lib-r.html)
    
    <!-- </ul> -->
    
<!-- </ul> -->

**Parent topic:**[Loading and accessing data in a notebook](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/load-and-access-data.html)

<!-- </article "role="article" "> -->
# Emotion classification #

The Emotion model in the Watson Natural Language Processing classification workflow classifies the emotion in the input text\.

**Workflow name**`ensemble_classification-workflow_en_emotion-stock`

**Supported languages**

<!-- <ul> -->

 *  English and French

<!-- </ul> -->

**Capabilities**

The Emotion classification model is a pre\-trained document classification model for the task of classifying the emotion in the input document\. The model identifies the emotion of a document, and classifies it as:

<!-- <ul> -->

 *  Anger
 *  Disgust
 *  Fear
 *  Joy
 *  Sadness

<!-- </ul> -->

Unlike the Sentiment model, which classifies each individual sentence, the Emotion model classifies the entire input document\. As such, the Emotion model works optimally when the input text to classify is no longer than 1000 characters\. If you would like to classify texts longer than 1000 characters, split the text into sentences or paragraphs for example and apply the Emotion model on each sentence or paragraph\.

A document may be classified into multiple categories or into no category\.

<!-- <table> -->

Capabilities of emotion classification based on an example

| Capabilities                                           | Example                                                          |
| ------------------------------------------------------ | ---------------------------------------------------------------- |
| Identifies the emotion of a document and classifies it | "I'm so annoyed that this code won't run \-\-> anger, sadness |

<!-- </table ""> -->

**Dependencies on other blocks**

None

**Code sample**

    import watson_nlp
    
    # Load the Emotion workflow model for English
    emotion_model = watson_nlp.load('ensemble_classification-workflow_en_emotion-stock')
    
    # Run the Emotion model 
    emotion_result = emotion_model.run("I'm so annoyed that this code won't run")
    print(emotion_result)

Output of the code sample:

    {
      "classes": [
        {
          "class_name": "anger",
          "confidence": 0.6074999913276445
        },
        {
          "class_name": "sadness",
          "confidence": 0.2913303280964709
        },
        {
          "class_name": "fear",
          "confidence": 0.10266377929247113
        },
        {
          "class_name": "disgust",
          "confidence": 0.018745421312542355
        },
        {
          "class_name": "joy",
          "confidence": 0.0020577122567564804
        }
      ],
      "producer_id": {
        "name": "Voting based Ensemble",
        "version": "0.0.1"
      }

**Parent topic:**[Watson Natural Language Processing task catalog](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html)

<!-- </article "role="article" "> -->
# Hierarchical text categorization #

The Watson Natural Language Processing Categories block assigns individual nodes within a hierarchical taxonomy to an input document\. For example, in the text *IBM announces new advances in quantum computing*, examples of extracted categories are `technology and computing/hardware/computer` and `technology and computing/operating systems`\. These categories represent level 3 and level 2 nodes in a hierarchical taxonomy\.

This block differs from the Classification block in that training starts from a set of seed phrases associated with each node in the taxonomy, and does not require labeled documents\.

Note that the Hierarchical text categorization block can only be used in a notebook that is started in an environment based on Runtime 22\.2 or Runtime 23\.1 that includes the Watson Natural Language Processing library\.

**Block name**

`categories_esa_en_stock`

**Supported languages**

The Categories block is available for the following languages\. For a list of the language codes and the corresponding language, see [Language codes](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html#lang-codes)\.

de, en

**Capabilities**

Use this block to determine the topics of documents on the web by categorizing web pages into a taxonomy of general domain topics, for ad placement and content recommendation\. The model was tested on data from news reports and general web pages\.

For a list of the categories that can be returned, see [Category types](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-returned-categories.html)\.

**Dependencies on other blocks**

The following block must run before you can run the hierarchical categorization block:

<!-- <ul> -->

 *  `syntax_izumo_<language>_stock`

<!-- </ul> -->

**Code sample**

    import watson_nlp
    
    # Load Syntax and a Categories model for English
    syntax_model = watson_nlp.load('syntax_izumo_en_stock')
    categories_model = watson_nlp.load('categories_esa_en_stock')
    
    # Run the syntax model on the input text
    syntax_prediction = syntax_model.run('IBM announced new advances in quantum computing')
    
    # Run the categories model on the result of syntax
    categories = categories_model.run(syntax_prediction)
    print(categories)

Output of the code sample:

    {
      "categories": [
        {
          "labels": 
            "technology & computing",
            "computing"
          ],
          "score": 0.992489,
          "explanation": ]
        },
        {
          "labels": 
            "science",
            "physics"
          ],
          "score": 0.945449,
          "explanation": ]
        }
      ],
      "producer_id": {
        "name": "ESA Hierarchical Categories",
        "version": "1.0.0"
      }
    }

**Parent topic:**[Watson Natural Language Processing task catalog](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html)

<!-- </article "role="article" "> -->
# Keyword extraction and ranking #

The Watson Natural Language Processing Keyword extraction with ranking block extracts noun phrases from input text based on their relevance\.

**Block name**

`keywords_text-rank_<language>_stock`

**Supported language**

Keyword extraction with text ranking is available for the following languages\. For a list of the language codes and the corresponding language, see [Language codes](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html#lang-codes)\.

ar, cs, da, de, en, es, fi, fr, he, hi, it, ja, ko, nb, nl, nn, pt, ro, ru, sk, sv, tr, zh\-cn

**Capabilities**

The keywords and text rank block ranks noun phrases extracted from an input document based on how relevant they are within the document\.

<!-- <table> -->

Capabilities of keyword extraction and ranking based on an example

| Capabilities                                    | Examples                                                                                                                                             |
| ----------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| Ranks extracted noun phrases based on relevance | "Anna went to school at University of California Santa Cruz\. Anna joined the university in 2015\." \-> Anna, University of California Santa Cruz |

<!-- </table ""> -->

**Dependencies on other blocks**

The following blocks must run before you can run the Keyword extraction with ranking block:

<!-- <ul> -->

 *  `syntax_izumo_<language>_stock`
 *  `noun-phrases_rbr_<language>_stock`

<!-- </ul> -->

**Code sample**

    import watson_nlp
    text = "Anna went to school at University of California Santa Cruz. Anna joined the university in 2015."
    
    # Load Syntax, Noun Phrases and Keywords models for English
    syntax_model = watson_nlp.load('syntax_izumo_en_stock')
    noun_phrases_model = watson_nlp.load('noun-phrases_rbr_en_stock')
    keywords_model = watson_nlp.load('keywords_text-rank_en_stock')
    
    # Run the Syntax and Noun Phrases models
    syntax_prediction = syntax_model.run(text, parsers=('token', 'lemma', 'part_of_speech'))
    noun_phrases = noun_phrases_model.run(text)
    
    # Run the keywords model
    keywords = keywords_model.run(syntax_prediction, noun_phrases, limit=2)
    print(keywords)

Output of the code sample:

    'keywords': 
           [{'text': 'University of California Santa Cruz', 'relevance': 0.939524, 'count': 1},
            {'text': 'Anna', 'relevance': 0.891002, 'count': 2}]

**Parent topic:**[Watson Natural Language Processing task catalog](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html)

<!-- </article "role="article" "> -->
# Language detection #

The Watson Natural Language Processing Language Detection identifies the language of input text\.

**Block name**`lang-detect_izumo_multi_stock`

**Supported languages**

The Language Detection block is able to detect the following languages:

af, ar, bs, ca, cs, da, de, el, en, es, fi, fr, he, hi, hr, it, ja, ko, nb, nl, nn, pl, pt, ro, ru, sk, sr, sv, tr, zh\_cn, zh\_tw

**Capabilities**

Use this block to detect the language of an input text\.

**Dependencies on other blocks**

None

**Code sample**

    # Load the language detection model
    lang_detection_model = watson_nlp.load('lang-detect_izumo_multi_stock')
    
    # Run it on input text
    detected_lang = lang_detection_model.run('IBM announced new advances in quantum computing')
    
    # Retrieve language ISO code
    print(detected_lang.to_iso_format())

Output of the code sample:

    EN

**Parent topic:**[Watson Natural Language Processing task catalog](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html)

<!-- </article "role="article" "> -->
# Noun phrase extraction #

The Watson Natural Language Processing Noun phrase extraction block extracts noun phrases from input text\.

**Block name**

`noun-phrases_rbr_<language>_stock`

Note: The "rbr" abbreviation in model name means rule\-based reasoning\. RBR models handle syntactically regular entity types such as number, email and phone\.

**Supported languages**

Noun phrase extraction is available for the following languages\. For a list of the language codes and the corresponding language, see [Language codes](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html#lang-codes)\.

ar, cs, da, de, es, en, fi, fr, he, hi, it, ja, ko, nb, nl, nn, pt, ro, ru, sk, sv, tr, zh\_cn, zh\_tw

**Capabilities**

The Noun phrase extraction block extracts non\-overlapping noun phrases from the input text\.

<!-- <table> -->

Capabilities of noun phrase extraction based on an example

| Capabilities                                | Examples                                                                                                              |
| ------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| Extraction of non\-overlapping noun phrases | "Anna went to school at University of California Santa Cruz" \-> Anna, school, University of California Santa Cruz |

<!-- </table ""> -->

**Dependencies on other blocks**

None

**Code sample**

    import watson_nlp
    
    # Load the model for English
    noun_phrases_model = watson_nlp.load('noun-phrases_rbr_en_stock')
    
    # Run the model on the input text
    noun_phrases = noun_phrases_model.run('Anna went to school at University of California Santa Cruz')
    print(noun_phrases)

Output of the code sample:

    {
      "noun_phrases": [
        {
          "span": {
            "begin": 0,
            "end": 4,
            "text": "Anna"
          }
        },
        {
          "span": {
            "begin": 13,
            "end": 19,
            "text": "school"
          }
        },
        {
          "span": {
            "begin": 23,
            "end": 58,
            "text": "University of California Santa Cruz"
          }
        }
      ],
      "producer_id": {
        "name": "RBR Noun phrases",
        "version": "0.0.1"
      }
    }

**Parent topic:**[Watson Natural Language Processing task catalog](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html)

<!-- </article "role="article" "> -->
# Tone classification #

The Tone model in the Watson Natural Language Processing classification workflow classifies the tone in the input text\.

**Workflow name**

`ensemble_classification-workflow_en_tone-stock`

**Supported languages**

<!-- <ul> -->

 *  English and French

<!-- </ul> -->

**Capabilities**

The Tone classification model is a pre\-trained document classification model for the task of classifying the tone in the input document\. The model identifies the tone of the input document and classifies it as:

<!-- <ul> -->

 *  Excited
 *  Frustrated
 *  Impolite
 *  Polite
 *  Sad
 *  Satisfied
 *  Sympathetic

<!-- </ul> -->

Unlike the Sentiment model, which classifies each individual sentence, the Tone model classifies the entire input document\. As such, the Tone model works optimally when the input text to classify is no longer than 1000 characters\. If you would like to classify texts longer than 1000 characters, split the text into sentences or paragraphs for example and apply the Tone model on each sentence or paragraph\.

A document may be classified into multiple categories or into no category\.

<!-- <table> -->

Capabilities of tone classification

| Capabilities                                        | Example                                                                               |
| --------------------------------------------------- | ------------------------------------------------------------------------------------- |
| Identifies the tone of a document and classifies it | "I'm really happy with how this was handled, thank you\!" \-\-> excited, satisfied |

<!-- </table ""> -->

**Dependencies on other blocks**

None

**Code sample**

    import watson_nlp
    
    # Load the Tone workflow model for English
    tone_model = watson_nlp.load('ensemble_classification-workflow_en_tone-stock')
    
    # Run the Tone model 
    tone_result = tone_model.run("I'm really happy with how this was handled, thank you!")
    print(tone_result)

Output of the code sample:

    {
      "classes": [
        {
          "class_name": "excited",
          "confidence": 0.6896854620082722
        },
        {
          "class_name": "satisfied",
          "confidence": 0.6570277557333078
        },
        {
          "class_name": "polite",
          "confidence": 0.33628806679460566
        },
        {
          "class_name": "sympathetic",
          "confidence": 0.17089694967744093
        },
        {
          "class_name": "sad",
          "confidence": 0.06880583874412932
        },
        {
          "class_name": "frustrated",
          "confidence": 0.010365418217209686
        },
        {
          "class_name": "impolite",
          "confidence": 0.002470793624966174
        }
      ],
      "producer_id": {
        "name": "Voting based Ensemble",
        "version": "0.0.1"
      }
    }

**Parent topic:**[Watson Natural Language Processing task catalog](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/watson-nlp-block-catalog.html)

<!-- </article "role="article" "> -->
# Governing assets in AI use cases #

Create an AI use case to track and govern AI assets from request through production\. Factsheets capture details about the asset for each stage of the AI lifecycle to help you meet governance and compliance goals\.

To learn about AI use cases, you can follow a tutorial in the *Getting started with watsonx\.governance* sample project\. Assets in the sample are prompt templates for a car insurance claim processing use case\. The prompts use car insurance claims as input and then use large language models to help insurance agents process the claims\. One prompt summarizes claims, another prompt extracts key information such as make and model, and the last prompt generates suggestions for the insurance agent\.

In **Projects**, start a new project, then choose to create a project from a sample\. The project gallery includes the getting started sample\.

![Getting started sample project](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-get-started-project.png)

When your project is ready, open the Readme for a step\-by\-step tutorial\.

![Getting started sample project](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-get-started-project-readme.png)

## Get started with AI use cases ##

Set up or work with AI use cases:

<!-- <ul> -->

 *  [Create an inventory](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-inventory-manage.html) for storing AI use cases
 *  [Set up an AI use case](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-use-cases.html)
 *  [Track assets](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-tracking-overview.html) in an AI use case
 *  [View factsheets](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-factsheet-viewing.html) for tracked assets

<!-- </ul> -->

**Parent topic:**[Governing AI assets](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-overview.html)

<!-- </article "role="article" "> -->
# Viewing a factsheet for a tracked asset #

Review the details that are captured for each tracked asset in an AI use case or print a report to share or archive\.

## What is captured in a factsheet? ##

From the point where you start tracking an asset in an AI use case, facts are collected in a factsheet for the asset\. As the asset moves from one phase of the lifecycle to the next, the facts are added to the appropriate section\. For example, a factsheet for a prompt template collects information for these categories:

<!-- <table> -->

| Category          | Description                                                                                                    |
| ----------------- | -------------------------------------------------------------------------------------------------------------- |
| Governance        | basic details for the governance, including the name of the use case, version number, and approach information |
| Foundation model  | name and provider for the foundation model                                                                     |
| Prompt template   | prompt template name, description, input, and variables                                                        |
| Prompt parameters | options used to create the prompt template, such as decoding method                                            |
| Evaluation        | results of the most recent evaluation                                                                          |
| Attachments       | attached files and supporting documents                                                                        |

<!-- </table ""> -->

Important: The factsheet records the most recent activity in any category\. For example, if you evaluate a deployed prompt template in a pre\-production space, and then evaluate it in a production space, the details from the production evaluation are captured in the factsheet, over\-writing the previous data\. Thus, the factsheet maintains a complete record of the current state of the asset\.

![Vewing a factsheet for a tracked prompt template](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-view-factsheet1.png)

## Next steps ##

Click **Export report** to save a report of the factsheet\.

**Parent topic:**[Governing assets in AI use cases](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-create-use-case.html)

<!-- </article "role="article" "> -->
# Managing attachments for AI use cases #

Create attachment groups and define attachment slots for an AI use case or factsheet\.

## Adding attachment groups ##

If you have admin access to an inventory, you can define attachment groups and manage attachment definitions for the AI use cases or factsheets in the inventory\. Use an attachment group to organize a set of related attachment facts and render them together\. Attachments can provide supporting information and extra details for a use case\. Data scientists might want to attach visualizations from their model\. Model requesters might want to attach a file of requirements to describe a business need\.

### Creating an attachment group ###

<!-- <ol> -->

1.  Open the AI uses cases settings and click the **Attachments** tab\. If you do not see this tab, you might have insufficient access\.
2.  Choose whether to add an attachment group to an AI use case or to the factsheet template\.
3.  Click **Add group**\.
4.  Enter a name and an optional description\.
5.  When you define the attachment group, an identifier is created from the name of the group\. The identifier can be used for programmatic access to the group\. Click **Show identifier** to view and edit the ID\.
6.  Save your changes to create the attachment group\.

<!-- </ol> -->

### Adding attachment facts to a group ###

From an attachment group, add attachment fact definitions that specify how a user can add an attachment to a factsheet\. Attachment definitions display as available slots in the attachment section for a use case or factsheet\.

Use the up and down arrow keys to reorder attachments in the list\.

In this example, an attachment group for approvals defines attachment facts for approvals from risk and compliance and from the model validator\.

![Defining an attachment group and attachment facts](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-attach-group.png)

When you save your attachment fact definitions, an attachment slot and description display on the use case or factsheet for attaching a file\. A pin icon indicates an available attachment slot\. Any user with at least edit access to the use case or factsheet can upload attachments\.

![Defining an attachment group and attachment facts](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-attach-group2.png)

**Parent topic:**[Creating and managing inventories](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-inventory-manage.html)

<!-- </article "role="article" "> -->
# Managing and customizing report templates #

If the default report templates that are provided with AI Factsheets do not meet your needs, you can download a default report template, customize it, and upload the new template\.

## Customizing a custom report template ##

Any user with at least Editor access can create a report from an AI use case that captures all the details from An AI use case\. You can use reports for compliance verification, archiving, or other purposes\.

If the default templates for the reports do not meet the needs of your organization, you can customize the report templates, the branding file, or the default stylesheet\. For example, you can replace the IBM logo with your own logo image file\. You must have the Admin role for managing inventories to customize report templates\.

Follow these steps to customize a report template\.

### Downloading a report ###

To download a report template from the UI:

<!-- <ol> -->

1.  Open the AI uses cases settings and click the **Report templates** tab\. If you do not see this tab, you might have insufficient access\.
2.  In the options menu for a report template, click **Download**\. ![Downloading a report template](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-report-template.png)
3.  Open the `<report-name>.ftl` file in an editor\.
4.  Edit the template by using instructions from [Apache FreeMarker](https://freemarker.apache.org/) or the API commands\.

<!-- </ol> -->

To download a report template by using APIs:

<!-- <ol> -->

1.  Use the `GET` endpoint for `/v1/aigov/report_templates` in the [IBM Watson Data API](https://cloud.ibm.com/apidocs/watson-data-api) to list the available templates\. Note the ID for the template that you want to download\.
2.  Use the `GET` endpoint `/v1/aigov/report_templates/{template_id}/content` with the template ID to download the template file\.
3.  Open the `<report-name>.ftl` file in an editor\.
4.  Edit the template by using instructions from [Apache FreeMarker](https://freemarker.apache.org/) or the API commands\.

<!-- </ol> -->

### Uploading a template ###

<!-- <ol> -->

1.  Open the AI uses cases settings and click the **Report templates** tab\. If you do not see this tab, you might have insufficient access\.
2.  Click **Add template**\.
3.  Specify a name for the template and an optional description\.
4.  Choose the type of template: model or model use case\. The reports are available for external models and Watson Machine Learning models\.
5.  Upload the updated `FTL` file\.  
    
<!-- </ol> -->

Restriction:The `ftl` file that you upload must not import any other files\. Support is not yet available for `import` statements other than system templates in the `ftl` file\.

The custom template displays in the Report templates section and is available for creating reports\. Click **Edit** or **Delete** from the action menu for a custom template to update the template details or to remove the template\.

**Parent topic:**[Creating and managing inventories](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-inventory-manage.html)

<!-- </article "role="article" "> -->
# Tracking a machine learning model #

Track machine learning models in an AI use case to meet governance and compliance goals\.

## Tracking machine learning models in an AI use case ##

Track machine learning models that are trained in a project and saved as a model asset\. You can add a machine learning model to an AI use case from a project or space\.

<!-- <ol> -->

1.  Open the project or space that contains the model asset that you want to govern\.
2.  From the action menu for the asset, click **Track in AI use case**\.
3.  Select an existing AI use case or follow the prompts to create a new one\.
4.  Choose an existing approach or create a new approach\. An approach creates a version set for all assets in the same approach\.
5.  Choose a version numbering scheme\. All of the assets in an approach share a common version\. Choose from:
    
    <!-- <ul> -->
    
     *  *Experimental* if you plan to update frequently.
     *  *Stable* if the assets are not changing rapidly.
     *  *Custom* if you want to start a new version number. Version numbering must follow a schema of major.minor.patch.
    
    <!-- </ul> -->
    
<!-- </ol> -->

![Tracking a model](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-track-model1.png)  

Watch this video to see how to track a machine learning model in an AI use case\.

This video provides a visual method to learn the concepts and tasks in this documentation\.

Once tracking is enabled, all collaborators for the use case can review details for the asset\.

![Viewing a tracked model in an AI use case](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-track-model2.png)  

For a machine learning model, facts include creation details, training data used, and information from evaluation metrics\.

![Viewing a factsheet for a tracked model](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-track-model2.png)  

For details on tracking a machine learning model that is created in a Jupyter Notebook or trained with a third\-party machine learning provider, see [Tracking external models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-external-models.html)\.

## Learn more ##

**Parent topic:**[Tracking assets in use cases](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-tracking-overview.html)

<!-- </article "role="article" "> -->
# Tracking assets in an AI use case #

Track machine learning models or prompt templates in AI use cases to capture details about them in factsheets\. Use the information collected in the AI use case to monitor the progress of assets through the AI lifecycle, from request to production\.

Define an AI use case to identify a business problem and request a solution\. A solution might be a predictive machine learning model or a generative AI prompt template\. When an asset is developed, associate it with the use case to capture details about the asset in factsheets\. As the asset moves through the AI lifecycle, from development to testing and then to production, the factsheets collect the data to support governance or compliance goals\.

## Creating approaches to compare ways to solve a problem ##

Each AI use case can contain at least one *approach*\. An approach is one facet of the solution to the business problem represented by the AI use case\. For example, you might create two approaches to compare by using different frameworks for predictive models to see which one performs best\. Or, created approaches to track several prompt templates in a use case\.

Approaches also capture version information\. The same version number is applied to all assets in an approach\. If you have a stable version of an asset, you might maintain that version in an approach and create a new approach for the next round of iteration and experimentation\.

This use case includes three approaches for organizing three prompt templates for an insurance claims processing use case:

![Multiple approaches for an insurance claim use case](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/xgov-prompt-approach1.png)

## Adding assets to a use case ##

You can track these assets in an AI use case:

<!-- <ul> -->

 *  [Prompt templates](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-track-prompt-temp.html) include the prompt input for a foundation model and variables that are defined to make the prompt reusable for generating new output\.
 *  [Machine learning models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-track-ml-model.html) that are created by using a Watson Machine Learning tool such as AutoAI or SPSS Modeler\.
 *  [External models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-external-models.html) are models that are created in Jupyter Notebooks or models that are created by using a third\-party machine learning provider\.

<!-- </ul> -->

**Parent topic:**[Governing assets in AI use cases](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/xgov-use-cases.html)

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# Accessibility features in IBM watsonx content and documentation #

IBM is committed to accessibility\. Accessibility features that follow compliance guidelines are included in IBM watsonx content and documentation to benefit users with disabilities\. Parts of the user interface of IBM watsonx are accessible, but not entirely\. Only documentation is compliant, with a subset of parts of the overall product\.

IBM watsonx documentation uses the latest W3C Standard, [WAI\-ARIA 1\.0](https://www.w3.org/TR/wai-aria/) to ensure compliance with the [United States Access Board Section 508 Standards](https://www.access-board.gov/ict/), and the [ Web Content Accessibility Guidelines (WCAG) 2\.0](https://www.w3.org/TR/WCAG20/)\.

The IBM watsonx online product documentation is enabled for accessibility\. Accessibility features help users who have a disability, such as restricted mobility or limited vision, to use information technology products successfully\. Documentation is provided in HTML so that it is easily accessible through assistive technology\. With the accessibility features of IBM watsonx, you can do the following tasks:

<!-- <ul> -->

 *  Use screen\-reader software and digital speech synthesizers to hear what is displayed on the screen\. Consult the product documentation of the assistive technology for details on using assistive technologies with HTML\-based information\.
 *  Use screen magnifiers to magnify what is displayed on the screen\.
 *  Operate specific or equivalent features by using only the keyboard\.

<!-- </ul> -->

For more information about the commitment that IBM has to accessibility, see [IBM Accessibility](http://www.ibm.com/able)\.

## TTY service ##

In addition to standard IBM help desk and support websites, IBM has established a TTY telephone service for use by deaf or hard of hearing customers to access sales and support services:

800\-IBM\-3383 (800\-426\-3383) within North America

## Additional interface information ##

The IBM watsonx user interfaces do not have content that flashes 2 \- 55 times per second\.

The IBM watsonx web user interfaces rely on cascading stylesheets to render content properly and to provide a usable experience\. If you are a low\-vision user, you can adjust your operating system display settings, and use settings such as high contrast mode\. You can control font size by using the device or web browser settings\.

<!-- </article "role="article" "> -->
# Adding associated services to a project #

To run some tools, you must associate a Watson Machine Learning service instance with the project\.

After you [create a project](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/projects.html), you can add an associated service to it at any time\.

**Required permission** : You must have the **Admin** role in the project to add an associated service\.

For some types of assets, you must associate the IBM Watson Machine Learning service with the project\. You are prompted to associate the IBM Watson Machine Learning the first time you open tools like Prompt Lab, AutoAI, SPSS Modeler, and Decision Optimization\.

You can also add the Watson Machine Learning service to a project directly:

<!-- <ol> -->

1.  Go to the project's **Manage** tab and select the **Services and integrations** page\.
2.  In the **IBM Services** section, click **Associate Service**\.
3.  Select your IBM Watson Machine Learning service instance and click **Associate**\.

<!-- </ol> -->

## Learn more ##

<!-- <ul> -->

 *  [Creating and managing IBM Cloud services](https://dataplatform.cloud.ibm.com/docs/content/wsj/admin/create-services.html)
 *  [IBM Cloud services for IBM watsonx](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/cloud-services.html)

<!-- </ul> -->

**Parent topic:**[Administering projects](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/admin-project.html)

<!-- </article "role="article" "> -->
# Browser support #

The supported web browsers provide the best experience for IBM watsonx\.

Use the latest versions of these web browers with IBM watsonx:

<!-- <ul> -->

 *  Chrome
 *  Microsoft Edge
 *  Mozilla Firefox  
    Tip for Firefox on Mac users: Horizontal scrolling within the UI can be interpreted by your Mac as an attempt to swipe between pages. If this behavior is undesired or if the browser crashes after the service prompts you to stay on the page, consider disabling the **Swipe between pages** gesture in **Launchpad > System Preferences > Trackpad > More Gestures**.
 *  Firefox ESR (see Mozilla Firefox Extended Support Release for more details)

<!-- </ul> -->

## Learn more ##

<!-- <ul> -->

 *  [Language support](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/localization.html)

<!-- </ul> -->

**Parent topic:**[FAQ](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/faq.html)

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# Getting started with IBM watsonx as a Service #

You can sign up for IBM watsonx\.ai or IBM watsonx\.governance and explore the tutorials, resources, and tools to immediately get started working with models or governing models\. If you are an administrator, follow the steps to set up watsonx for your organization\.

## Start working ##

To start working:

<!-- <ol> -->

1.  If you haven't already, [sign up](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/signup-wx.html) for watsonx\.ai or watsonx\.governance\.
2.  Click a task tile on the watsonx home page and start working\. For example, click **Experiment with foundation models and build prompts** to open the Prompt Lab\. Then, choose a sample prompt and start experimenting\. Your first project, where you save your work, is created automatically\. See [Your sandbox project](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/sandbox.html)\.
3.  Explore your resources:
    
    <!-- <ul> -->
    
     *  Take a [Quick start tutorial](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/quickstart-tutorials.html)
     *  Click a category in the **Samples** area of the home page to try out a notebook, a prompt, or a sample project.
    
    <!-- </ul> -->
    
<!-- </ol> -->

If you are an existing Cloud Pak for Data as a Service user, you can [switch to watsonx](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/platform-switcher.html)\.

## Set up the platform as an administrator ##

To set up the watsonx platform for your organization, see [Setting up the platform as an administrator](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/setup-platform.html)\.

## Learn about watsonx ##

To understand watsonx, start with these resources:

<!-- <ul> -->

 *  [Overview of watsonx](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/overview-wx.html)
 *  [Video library](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/videos-wx.html)
 *  [Projects](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/manage-projects.html)
 *  [Foundation models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-overview.html)
 *  [Read blogs on Medium and the IBM Community](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-help.html#community)

<!-- </ul> -->

Other information:

<!-- <ul> -->

 *  [Get help](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-help.html)
 *  [Browser support](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/browser-support.html)
 *  [Language support](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/localization.html)
 *  [IBM watsonx APIs](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/wdp-apis.html)

<!-- </ul> -->

<!-- </article "role="article" "> -->
# Language support for the product and the documentation #

IBM watsonx is translated into multiple languages\.

## Supported languages ##

The IBM watsonx user interface is translated into these languages:

<!-- <ul> -->

 *  Brazilian Portuguese
 *  Simplified Chinese
 *  Traditional Chinese
 *  French
 *  German
 *  Italian
 *  Japanese
 *  Korean
 *  Spanish
 *  Swedish

<!-- </ul> -->

The documentation is automatically translated into these languages:

<!-- <ul> -->

 *  Brazilian Portuguese
 *  Simplified Chinese
 *  French
 *  German
 *  Italian
 *  Japanese
 *  Korean
 *  Spanish

<!-- </ul> -->

IBM is not responsible for any damages or losses resulting from the use of automatically (machine) translated content\.

When the translated documentation is not as current as the English content, you see a message and have the option of switching to the English content\.

## Changing languages ##

To change the language for this documentation, scroll to the end of any documentation page, and select a language from the language selector\.

![Screen capture of the language switcher](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/images/lang-switcher.png)

To change the language for both the product user interface and this documentation, select a different language for your browser:

<!-- <ul> -->

 *  In the Google Chrome browser, you can change the language in the advanced settings\.
 *  In the Mozilla Firefox browser, you can change the language in the general settings\.

<!-- </ul> -->

## Learn more ##

<!-- <ul> -->

 *  [Browser support](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/browser-support.html)

<!-- </ul> -->

**Parent topic:**[FAQ](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/faq.html)

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# Notices #

These notices apply to the watsonx platform\.

The Offering includes some or all of the following that IBM provides under the [SIL Open Font License 1\.1](https://opensource.org/license/openfont-html/):

<!-- <ul> -->

 *  AMSFONTS
 *  AMSFONTS (matplotlib)
 *  CARLOGO (matplotlib)
 *  CMMI9 (libtasn1)
 *  cvxopt 1\.3\.0
 *  FONTS (harfbuzz)
 *  FONTS (pillow)
 *  FONT AWESOME (Apache ORC)
 *  FONT AWESOME \- FONT (bazel)
 *  FONT AWESOME 4\.2\.0 (arrow)
 *  FONT\-AWESOME\-IE7\.MIN\.CSS (Jetty)
 *  FONT AWESOME (nbconvert)
 *  FONTAWESOME\-FONTS
 *  HELVETICA\-NEUE
 *  READLINE\.PS (Readline)
 *  FONT\-AWESOME (Notebook)
 *  FONTAWESOME
 *  FONTAWESOME (Tables)
 *  FONT AWESOME FONTS
 *  FONTAWESOME (FONT) (JupyterLab)
 *  Font\-Awesome v4\.6\.3
 *  Font\-Awesome v4\.3\.0
 *  Font\-Awesome v4\.7\.0
 *  handsontable v0\.25\.1
 *  minio 7\.1\.7
 *  IBM PLEX TYPEFACE (carbon\-components)
 *  nbconvert v5\.2\.1
 *  nbconvert v5\.1\.1
 *  nbconvert 6\.4\.4
 *  nbconvert 6\.5\.0
 *  nbdime 3\.1\.1
 *  NotoNastaliqUrdu\-Regular\.ttf (pillow)
 *  NOTO\-FONTS (pillow)
 *  QTAWESOME\-FONTS (qtawesome)
 *  qtawesome v3\.3\.0
 *  READLINE\.PS (Readline)
 *  RLUSERMAN\.PS (Readline)
 *  STIX FONT (matplotlib)

<!-- </ul> -->

The Offering includes some or all of the following that IBM provides under the [UBUNTU FONT LICENCE Version 1\.0](https://ubuntu.com/legal/font-licence):

<!-- <ul> -->

 *  Font\_license (Werkzeug)

<!-- </ul> -->

## Learn more ##

[Foundation model use terms](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-disclaimer.html)

<!-- </article "role="article" "> -->
# Collaborator security #

IBM watsonx provides attribute\-based access control to protect workspaces such as projects and catalogs\. You control access to workspaces by assigning roles and by restricting collaborators\.

<!-- <table> -->

Table 1\. Collaborator security mechanisms for IBM watsonx

| Mechanism              | Purpose                                      | Responsibility | Configured on |
| ---------------------- | -------------------------------------------- | -------------- | ------------- |
| [Collaborator roles](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/security-collab.html?context=cdpaas&locale=en#collaborator-roles) | Assign roles to control access to workspaces | Customer       | IBM watsonx   |

<!-- </table ""> -->

## Collaborator roles ##

Everyone working in IBM watsonx is assigned a role that determines the workspaces that they can access and the tasks that they can perform\. Collaborator roles control access to projects, deployment spaces, and catalogs using permissions specific to the role\. Roles are assigned in IBM watsonx to provide **Admin**, **Editor**, or **Viewer** permissions\.

Users also have an IAM Platform access role for the Cloud account and they may also have an IAM Service access role for workspaces\. To understand how the roles provide secure access, see [Roles in IBM watsonx](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/roles.html)\.

To understand the permissions for each collaborator role, see [Project collaborator roles](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/collaborator-permissions.html)\.

**Parent topic:**[Security](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/security-overview.html)

<!-- </article "role="article" "> -->