Showing 4 open source projects for "process"

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  • 1
    Label Studio

    Label Studio

    Label Studio is a multi-type data labeling and annotation tool

    ...Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. ...
    Downloads: 13 This Week
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  • 2
    Argilla

    Argilla

    The open-source data curation platform for LLMs

    ...Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Argilla is designed to close this gap, enabling you to iterate as much as you need.
    Downloads: 0 This Week
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  • 3
    Data Annotator for Machine Learning

    Data Annotator for Machine Learning

    Data annotator 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. User management panel with role-based access control.
    Downloads: 0 This Week
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  • 4
    ...Different types of tag markers can be incorporated e.g. grammatical, functional, semantic, linguistic markers.Tag-sets can be configured (modified/extended) by accessing the related table in the supporting database, The user can upload text files where sentences are normalized and inserted into the supporting database. Multiple narratives can be listed in the text file, where narratives are separated using a # symbol. The text upload process entitles the initial (POS) tagging of uploaded text using Stanford (POS) tagger. The user can later modify and extend the initial tagging. The resultant annotations are stored in the supporting database. These results can be exported to excel or text files for further processing.
    Downloads: 0 This Week
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