- 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
- Preparing data for .CSV upload
- Uploading a CSV file into a source
- 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
- 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
- Uploading data
- Downloading data
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Migration Guide: Exchange Web Services (EWS) to Microsoft Graph API
- Fetching data for Tableau with Python
- Elasticsearch integration
- General field extraction
- Self-hosted Exchange integration
- UiPath® Automation Framework
- 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
Communications Mining user guide
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.
The time period is based on the timestamp field of the comment, rather than the datetime the comment was uploaded to Communications Mining™.
This section assumes that you have already installed and configured the CLI.
Backing up annotated data
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.
Deleting Data
Deleting annotations changes model performance.
If the comments you want to delete 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.
Optionally, you can configure the CLI to skip annotated comments.
The following command deletes 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_TIMESTAMP
re 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.