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...Multi-user labeling sign up and login, when you create an annotation it's tied to your account. Configurable label formats let you customize the visual interface to meet your specific labeling needs. Support for multiple data types including images, audio, text, HTML, time-series, and video.

The Java software Discourse Network Analyzer (DNA) is a qualitative content analysis tool with network export facilities. You import text files and annotate statements that persons or organizations make, and the program will return network matrices of actors connected by shared concepts.

...Also, the makers of refinery currently work on integrations to other labeling tools, such that you can easily switch between different choices. refinery is a multi-repository project, you can find all integrated services in the architecture below. The app builds on top of Hugging Face and spaCy to leverage pre-built language models for your NLP tasks, as well as qdrant for neural search.

...Argilla is designed to close this gap, enabling you to iterate as much as you need.

Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning. Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitate the creation and management of annotations. Active learning with uncertain sampling to query unlabeled data. Project tracking with real-time data aggregation and review process.

...There are two parts to anno, the first is a series of Finder functions that look for interesting articles (which it calls `Notes`) inside the text, returning a slice of Note structs. The second is the Expander, which replaces the text in each Note with something else, like the HTML for a link or something. It tells you the bytes that it found, the `Start` index and a string describing the kind of `Note`. The kind is useful for when you run pass `Finder` objects to the `FindMany` or `FindManyString` functions. Since most of the built-in finders operate on a per field basis (word by word), it made sense to add a special helper called `FieldFunc` that generates`FinderFunc` functions for us, and takes away the repetitive task of breaking the string up, and iterating over each word.