Metadata-Version: 2.1
Name: hparams
Version: 0.1.0
Summary: UNKNOWN
Home-page: UNKNOWN
Author: Michael Petrochuk
Author-email: petrochukm@gmail.com
License: MIT
Description: <h3 align="center">Get your hyperparameters in a row.</h3>
        
        HParams is a library for
        
        PyTorch-NLP, or torchnlp for short, is a library of neural network layers, text processing modules and datasets designed to accelerate Natural Language Processing (NLP) research.
        
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pytorch-nlp.svg?style=flat-square)
        [![Codecov](https://img.shields.io/codecov/c/github/PetrochukM/PyTorch-NLP/master.svg?style=flat-square)](https://codecov.io/gh/PetrochukM/PyTorch-NLP)
        [![Downloads](http://pepy.tech/badge/pytorch-nlp)](http://pepy.tech/project/pytorch-nlp)
        [![Documentation Status](	https://img.shields.io/readthedocs/pytorchnlp/latest.svg?style=flat-square)](http://pytorchnlp.readthedocs.io/en/latest/?badge=latest&style=flat-square)
        [![Build Status](https://img.shields.io/travis/PetrochukM/PyTorch-NLP/master.svg?style=flat-square)](https://travis-ci.org/PetrochukM/PyTorch-NLP)
        
        _Logo by [Chloe Yeo](http://www.yeochloe.com/)_
        
        ## Installation
        
        Make sure you have Python 3.6+ and PyTorch 1.0+. You can then install `pytorch-nlp` using
        pip:
        
            pip install hparams
        
        Or to install the latest code via:
        
            pip install git+https://github.com/PetrochukM/PyTorch-NLP.git
        
        ## Docs 📖
        
        The complete documentation for PyTorch-NLP is available via [our ReadTheDocs website](https://pytorchnlp.readthedocs.io).
        
        ## Basics
        
        Add PyTorch-NLP to your project by following one of the common use cases:
        
        ### Load a [Dataset](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.datasets.html)
        
        Load the IMDB dataset, for example:
        
        ```python
        from torchnlp.datasets import imdb_dataset
        
        # Load the imdb training dataset
        train = imdb_dataset(train=True)
        train[0]  # RETURNS: {'text': 'For a movie that gets..', 'sentiment': 'pos'}
        ```
        
        ### Apply [Neural Networks](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.nn.html) Layers
        
        For example, from the neural network package, apply state-of-the-art LockedDropout:
        
        ```python
        import torch
        from torchnlp.nn import LockedDropout
        
        input_ = torch.randn(6, 3, 10)
        dropout = LockedDropout(0.5)
        
        # Apply a LockedDropout to `input_`
        dropout(input_) # RETURNS: torch.FloatTensor (6x3x10)
        ```
        
        ### [Encode Text](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.encoders.text.html)
        
        Tokenize and encode text as a tensor. For example, a `WhitespaceEncoder` breaks text into terms whenever it encounters a whitespace character.
        
        ```python
        from torchnlp.encoders.text import WhitespaceEncoder
        
        # Create a `WhitespaceEncoder` with a corpus of text
        encoder = WhitespaceEncoder(["now this ain't funny", "so don't you dare laugh"])
        
        # Encode and decode phrases
        encoder.encode("this ain't funny.") # RETURNS: torch.Tensor([6, 7, 1])
        encoder.decode(encoder.encode("This ain't funny.")) # RETURNS: "this ain't funny."
        ```
        
        ### Load [Word Vectors](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.word_to_vector.html)
        
        For example, load FastText, state-of-the-art English word vectors:
        
        ```python
        from torchnlp.word_to_vector import FastText
        
        vectors = FastText()
        # Load vectors for any word as a `torch.FloatTensor`
        vectors['hello']  # RETURNS: [torch.FloatTensor of size 300]
        ```
        
        ### Compute [Metrics](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.metrics.html)
        
        Finally, compute common metrics such as the BLEU score.
        
        ```python
        from torchnlp.metrics import get_moses_multi_bleu
        
        hypotheses = ["The brown fox jumps over the dog 笑"]
        references = ["The quick brown fox jumps over the lazy dog 笑"]
        
        # Compute BLEU score with the official BLEU perl script
        get_moses_multi_bleu(hypotheses, references, lowercase=True)  # RETURNS: 47.9
        ```
        
        ### Help :question:
        
        Maybe looking at longer examples may help you at [`examples/`](examples/).
        
        Need more help? We are happy to answer your questions via [Gitter Chat](https://gitter.im/PyTorch-NLP)
        
        ## Contributing
        
        We've released PyTorch-NLP because we found a lack of basic toolkits for NLP in PyTorch. We hope that other organizations can benefit from the project. We are thankful for any contributions from the community.
        
        ### Contributing Guide
        
        Read our [contributing guide](https://github.com/PetrochukM/PyTorch-NLP/blob/master/CONTRIBUTING.md) to learn about our development process, how to propose bugfixes and improvements, and how to build and test your changes to PyTorch-NLP.
        
        ## Related Work
        
        ### [torchtext](https://github.com/pytorch/text)
        
        torchtext and PyTorch-NLP differ in the architecture and feature set; otherwise, they are similar. torchtext and PyTorch-NLP provide pre-trained word vectors, datasets, iterators and text encoders. PyTorch-NLP also provides neural network modules and metrics. From an architecture standpoint, torchtext is object orientated with external coupling while PyTorch-NLP is object orientated with low coupling.
        
        ### [AllenNLP](https://github.com/allenai/allennlp)
        
        AllenNLP is designed to be a platform for research. PyTorch-NLP is designed to be a lightweight toolkit.
        
        ## Authors
        
        * [Michael Petrochuk](https://github.com/PetrochukM/) — Developer
        * [Chloe Yeo](http://www.yeochloe.com/) — Logo Design
        
        ## Citing
        
        If you find PyTorch-NLP useful for an academic publication, then please use the following BibTeX to cite it:
        
        ```
        @misc{pytorch-nlp,
          author = {Petrochuk, Michael},
          title = {PyTorch-NLP: Rapid Prototyping with PyTorch Natural Language Processing (NLP) Tools},
          year = {2018},
          publisher = {GitHub},
          journal = {GitHub repository},
          howpublished = {\url{https://github.com/PetrochukM/PyTorch-NLP}},
        }
        ```
        
Keywords: hyperparameters hparams configurable
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
