Showing 4 open source projects for "source testing unit testing"

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    Synthetic Data Vault (SDV)

    Synthetic Data Vault (SDV)

    Synthetic Data Generation for tabular, relational and time series data

    The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent...
    Downloads: 2 This Week
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  • 2
    GPT Neo

    GPT Neo

    An implementation of model parallel GPT-2 and GPT-3-style models

    An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library. If you're just here to play with our pre-trained models, we strongly recommend you try out the HuggingFace Transformer integration. Training and inference is officially supported on TPU and should work on GPU as well. This repository will be (mostly) archived as we move focus to our GPU-specific repo, GPT-NeoX. NB, while neo can technically run a training step at 200B+ parameters, it is very...
    Downloads: 0 This Week
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  • 3
    CRSLab

    CRSLab

    CRSLab is an open-source toolkit

    ...We have preprocessed these datasets to support these models, and release for downloading. Extensive and standard evaluation protocols: We support a series of widely-adopted evaluation protocols for testing and comparing different CRS. General and extensible structure: We design a general and extensible structure to unify various conversational recommendation datasets and models, in which we integrate various built-in interfaces and functions for quickly development. Easy to get started: We provide simple yet flexible configuration for new researchers to quickly start in our library. ...
    Downloads: 1 This Week
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  • 4
    TGAN

    TGAN

    Generative adversarial training for generating synthetic tabular data

    We are happy to announce that our new model for synthetic data called CTGAN is open-sourced. The new model is simpler and gives better performance on many datasets. TGAN is a tabular data synthesizer. It can generate fully synthetic data from real data. Currently, TGAN can generate numerical columns and categorical columns. TGAN has been developed and runs on Python 3.5, 3.6 and 3.7. Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid...
    Downloads: 0 This Week
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