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 delete
The CLI allows you to delete comments based on a time period, for example all comments older than two years. This is useful
for cleaning up historical data. Note that the time period is based on the comment's
timestamp field, rather than the datetime the comment was uploaded to Communications Mining™.
Before deleting or modifying your comments, you may optionally want to back up annotated comments, so as not to accidentally lose the manual work of the model trainers:
re get comments \
<project_name/source_name> \
--dataset <project_name/dataset_name> \
--reviewed-only true \
--file <output_file_name.jsonl>re get comments \
<project_name/source_name> \
--dataset <project_name/dataset_name> \
--reviewed-only true \
--file <output_file_name.jsonl>
If the source was added to multiple datasets, you should run the previously mentioned command for each of those datasets.
Warning:
DELETING ANNOTATIONS CHANGES MODEL PERFORMANCE
If the comments you are deleting were added to one or more datasets where they could have been annotated, deleting annotated comments will result in a change of model performance in those datasets going forward (pinned models will be unaffected). You can optionally tell the CLI to skip annotated comments.
The command below will delete all comments in a source between
FROM_TIMESTAMP and TO_TIMESTAMP , excluding annotated comments. The timestamp should be in RFC 3339 format, e.g. 1970-01-02T03:04:05Z.
re delete bulk \
--source <project_name/source_name> \
--include-annotated=false \
--from-timestamp FROM_TIMESTAMP \
--to-timestamp TO_TIMESTAMPre delete bulk \
--source <project_name/source_name> \
--include-annotated=false \
--from-timestamp FROM_TIMESTAMP \
--to-timestamp TO_TIMESTAMP
If you are sure you want to delete annotated comments, you can set
--include-annotated=true.
