- 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
- Uploading data
- Downloading data
- 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
Communications Mining user guide
Downloading data
For real-time analytics and automation use-cases we recommend using the Stream API, which allows you to iterate through messages in a dataset. If you are integrating Communications Mining™ as one of the enrichment steps in a data pipeline, take a look at the Predict API routes which may also be suitable for your design.
Datasets can be exported as CSV directly in the browser; there is no size limit, but large files may take a long time to download. We recommend to apply filters before exporting to limit the size of the download and make the CSV file more convenient to work with. Another option for batch download is to use the Communications Mining command-line tool (available for Linux, Mac, and Windows) or the Export API route.
- WHICH DOWNLOAD METHOD SHOULD I USE?
The previously mentioned download methods will differ slightly with regards to the way they provide predicted labels and general fields. Please be sure to review this comparison table to pick the method that best suits your use-case.
- HOW CAN I USE COMMUNICATIONS MINING LABELS IN AN AUTOMATION USE-CASE?
If you need help to get started with your automation use-case, check more details on Stream APIs on the Streams page. If you are looking to understand how to use Communications Mining labels in an automation use-case, check the Labels documentation.
- HOW CAN I USE COMMUNICATIONS MINING LABELS IN AN ANALYTICS USE-CASE?
If you are looking to understand how to use Communications Mining labels in an analytics use-case, take a look at the Labels documentation.
