ixp
latest
false
- Introduction
- Setting up your account
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields
- Labels (predictions, confidence levels, label hierarchy, and label sentiment)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Annotated and unannotated messages
- Extraction Fields
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Access Control and Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Creating or deleting a data source in the GUI
- Uploading a CSV file into a source
- Preparing data for .CSV upload
- Creating a dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amending dataset settings
- Deleting a message
- Deleting a dataset
- Exporting a dataset
- Using Exchange integrations
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- Comparing analytics and automation use cases
- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Training chat and calls data
- Understanding data requirements
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and Recall
- How validation works
- Understanding and improving model performance
- Reasons for label low average precision
- Training using Check label and Missed label
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Using general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining™
- Developer
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Fetching data for Tableau with Python
- Elasticsearch integration
- Self-hosted Exchange integration
- UiPath® Automation Framework
- UiPath® Marketplace activities
- UiPath® official activities
- How machines learn to understand words: a guide to embeddings in NLP
- Prompt-based learning with Transformers
- Efficient Transformers II: knowledge distillation & fine-tuning
- Efficient Transformers I: attention mechanisms
- Deep hierarchical unsupervised intent modelling: getting value without training data
- Fixing annotating bias with Communications Mining™
- Active learning: better ML models in less time
- It's all in the numbers - assessing model performance with metrics
- Why model validation is important
- Comparing Communications Mining™ and Google AutoML for conversational data intelligence
- Licensing
- FAQs and more
Important :
Communications Mining is now part of UiPath IXP. Check the Introduction in the Overview Guide for more details.
Communications Mining user guide
Last updated Aug 1, 2025
Batch download
The CLI allows you to download comments and predictions in batch. This is most useful to script import into analytics tools that don't require a live connection.
The command below will download all the comments and predictions in the specified source and dataset. Note that the dataset name and source name have to be prefixed by the name of the project they are in. If the dataset contains multiple sources, you need to issue this command for every source to download all comments in the dataset.
re get comments project/source-name --dataset project/dataset-name --predictions=true -f output.jsonlre get comments project/source-name --dataset project/dataset-name --predictions=true -f output.jsonl
Which model version does the CLI use to get predictions?
The CLI will download the latest available computed predictions. These are the same predictions shown in the UI.
Important: CAN I PASS A SPECIFIC MODEL VERSION TO THE CLI?
If you need predictions from a specific model version, consider using the or the predict API routes.
Note: Same as the API, the CLI returns predicted labels with confidence scores. In order to correctly process the confidence scores,
be sure to check the Using Labels in Automation and Using Labels in Analytics sections of the Labels documentation.
The CLI returns data in JSONL format (also called newline-delimited JSON), where each line is a JSON value. Many tools will be able to process JSONL files out-of-the-box. Please contact support if you have any questions.
Each line in the JSONL file will have the following format:
{
"comment": {...},
"annotating": {
"assigned": [...]
"predicted": [...]
},
"entities": {
"assigned": [...]
"predicted": [...]
}
}{
"comment": {...},
"annotating": {
"assigned": [...]
"predicted": [...]
},
"entities": {
"assigned": [...]
"predicted": [...]
}
}| FIELD NAME | DESCRIPTION |
|---|---|
comment | Comment object in the format described here. |
annotating.assigned | List of assigned labels, in the format described here. |
entities.assigned | List of assigned entities, in the format described here. |
annotating.predicted | List of predicted labels, in the format described here. |
entities.predicted | List of predicted entities, in the format described here. |
Note that the
annotating or entities field may be absent completely if the comment has neither assigned nor predicted labels or entities.
Below is an example comment with predictions downloaded from a real-life dataset.
{
"comment": {
"id": "1234abcd",
"uid": "5678ef.1234abdc",
"timestamp": "2021-02-01T00:00:00Z",
"messages": [
{
"body": {
"text": "The hot chocolate biscuit on arrival raised my expectations"
}
}
],
"user_properties": {
"string:Question": "What did you like about your stay",
"number:Reviewer Score": 5.4,
"number:Average Score": 8.4,
"number:Reviewer Total Number Of Reviews": 1,
"string:Hotel Name": "DoubleTree by Hilton London Victoria"
},
"created_at": "2021-02-01T00:00:00Z"
},
"annotating": {
"predicted": [
{
"name": "Refreshments",
"sentiment": 0.3598046874571062,
"probability": 0.54764723591506481
},
{
"name": "Property",
"sentiment": 0.6684685489411859,
"probability": 0.417815982922911644
}
]
}
}{
"comment": {
"id": "1234abcd",
"uid": "5678ef.1234abdc",
"timestamp": "2021-02-01T00:00:00Z",
"messages": [
{
"body": {
"text": "The hot chocolate biscuit on arrival raised my expectations"
}
}
],
"user_properties": {
"string:Question": "What did you like about your stay",
"number:Reviewer Score": 5.4,
"number:Average Score": 8.4,
"number:Reviewer Total Number Of Reviews": 1,
"string:Hotel Name": "DoubleTree by Hilton London Victoria"
},
"created_at": "2021-02-01T00:00:00Z"
},
"annotating": {
"predicted": [
{
"name": "Refreshments",
"sentiment": 0.3598046874571062,
"probability": 0.54764723591506481
},
{
"name": "Property",
"sentiment": 0.6684685489411859,
"probability": 0.417815982922911644
}
]
}
}
