Showing 4 open source projects for "python tools"

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    Arize Phoenix

    Arize Phoenix

    Uncover insights, surface problems, monitor, and fine tune your LLM

    Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative)...
    Downloads: 2 This Week
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  • 2
    Diffgram

    Diffgram

    Training data (data labeling, annotation, workflow) for all data types

    From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. Training Data is the art of supervising machines through data. This...
    Downloads: 1 This Week
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  • 3
    FiftyOne

    FiftyOne

    The open-source tool for building high-quality datasets

    The open-source tool for building high-quality datasets and computer vision models. Nothing hinders the success of machine learning systems more than poor-quality data. And without the right tools, improving a model can be time-consuming and inefficient. FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively. Improving data quality and understanding your model’s failure modes are the most impactful ways to...
    Downloads: 0 This Week
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  • 4

    NGS data quality evaluation

    Python tool to evaluate the quality of high-throughput sequencing data

    ngsdataqeval is a Python tool to evaluate the quality of high-throughput sequencing data, used by Next Generation Sequencing. Unlike other tools that analyze raw data, this is designed to evaluate the quality of the processed reads after mapping to a reference genome. The evaluation is performed in a genomic region defined by the user, and it provides some statistics computed from the reads that map to that region (ie. a single gene).
    Downloads: 0 This Week
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