Metadata-Version: 2.1
Name: mead-baseline
Version: 2.3.8
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/2.3.8.tar.gz
Description: # MEAD
        
        MEAD is a library for reproducible deep learning research and fast model
        development for NLP. It provides easily extensible abstractions and
        implementations for data loading, model development, training, experiment tracking and export to production. 
        
        It also provides implementations of 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 directly with: 
        
        `pip install mead-baseline[tf2]`
        
        *Note for TF 2.1 users*: If you are using TF 2.1, you cannot just `pip install tensorflow_addons` (or the command above) -- it will pull a version that is dependent on a more recent version with breaking changes.  If you are running TF 2.1, use a pinned version of the addons: `pip install tensorflow_addons==0.9.1`
        
        ### 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` AKA 8 mile, a tiny layers API containing PyTorch and TensorFlow primitives, locally and then `mead-baseline`
        
        ### Dockerhub
        
        We use Github CI/CD to automatically cut releases for TensorFlow (1.x and 2.x) and PyTorch via this project:
        
        https://github.com/mead-ml/mead-gpu
        
        Links to the latest dockerhub images can be found there
        
        ## A Note About Versions
        
        Deep Learning Frameworks are evolving quickly and changes are not always
        backwards compatible. We recommend recent versions of whichever framework is being used underneath.  We currently run on TF versions between 1.13 and 2.3, and we recommend using at least TF 2.1.
        The PyTorch backend requires at least version 1.3.0, though we recommend using a more recent version.
        
        ## 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 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: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3.5
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.6
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: report
Provides-Extra: yaml
Provides-Extra: bpe
Provides-Extra: regex
Provides-Extra: bpex
Provides-Extra: tf2
Provides-Extra: grpc
Provides-Extra: onnx
Provides-Extra: tfrecord
