Metadata-Version: 2.1
Name: mead-baseline
Version: 2.0.3
Summary: Strong Deep-Learning Baseline algorithms for NLP
Home-page: https://www.github.com/dpressel/baseline
Author: dpressel
Author-email: dpressel@gmail.com
License: Apache 2.0
Download-URL: https://www.github.com/dpressel/baseline/archive/2.0.3.tar.gz
Description: # MEAD Baseline
        
        MEAD Baseline is a library for reproducible deep learning research and fast model
        development for NLP. The library provides easily extensible abstractions and
        implementations for data loading, model development, training and export of deep
        learning architectures. It also provides implementations for high-performance,
        deep learning models for various NLP tasks, against which newly developed models
        can be compared. Deep learning experiments are hard to reproduce, MEAD
        provides functionalities to track them. The goal is to allow a researcher to
        focus on model development, delegating the repetitive tasks to the library.
        
        [Documentation](https://github.com/dpressel/mead-baseline/blob/master/docs/main.md)
        
        [Tutorials using Colab](https://github.com/dpressel/mead-tutorials)
        
        [MEAD Hub](https://github.com/mead-ml/hub)
        
        ## Installation
        
        ### Pip
        
        Baseline can be installed as a Python package.
        
        `pip install mead-baseline`
        
        If you are using tensorflow 2 as your deep learning backend you will need to have
        `tensorflow_addons` already installed or have it get installed with mead via
        
        `pip install mead-baseline[tf2]`
        
        
        ### From the repository
        
        If you have a clone of this repostory and want to install from it:
        
        ```
        cd layers
        pip install -e .
        cd ../
        pip install -e .
        ```
        
        This first installs `mead-layers` (8 mile) locally and then `mead-baseline`
        
        ## A Note About Versions
        
        Deep Learning Frameworks are evolving quickly, and changes are not always
        backwards compatible. We recommend recent versions of each framework. Baseline
        is known to work on most versions of TensorFlow, and is currently being run on
        versions between 1.13 and 2.1 .
        
        The PyTorch backend requires at least version 1.3.0.
        
        ## Citing
        
        If you use the library, please cite the following paper:
        
        ```
        @InProceedings{W18-2506,
          author =    "Pressel, Daniel
                       and Ray Choudhury, Sagnik
                       and Lester, Brian
                       and Zhao, Yanjie
                       and Barta, Matt",
          title =     "Baseline: A Library for Rapid Modeling, Experimentation and
                       Development of Deep Learning Algorithms targeting NLP",
          booktitle = "Proceedings of Workshop for NLP Open Source Software (NLP-OSS)",
          year =      "2018",
          publisher = "Association for Computational Linguistics",
          pages =     "34--40",
          location =  "Melbourne, Australia",
          url =       "http://aclweb.org/anthology/W18-2506"
        }
        ```
        
        MEAD Baseline was selected for a Spotlight Poster at the NeurIPS MLOSS workshop in 2018.  [OpenReview link](https://openreview.net/forum?id=r1xEb7J15Q)
        
Keywords: deep-learning,nlp,pytorch,tensorflow
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.5
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.7
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: report
Provides-Extra: yaml
Provides-Extra: tf2
Provides-Extra: grpc
Provides-Extra: onnx
