Metadata-Version: 2.1
Name: pytorch-nlp
Version: 0.3.7.post1
Summary: Text utilities and datasets for PyTorch
Home-page: https://github.com/PetrochukM/PytorchNLP
Author: Michael Petrochuk
Author-email: petrochukm@gmail.com
License: BSD
Description: <p align="center"><img width="55%" src="docs/_static/img/logo.svg" /></p>
        
        <h3 align="center">Supporting Rapid Prototyping with a Deep Learning NLP Toolkit&nbsp;&nbsp;
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        </h3>
        
        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.
        
        Join our community, add datasets and neural network layers! Chat with us on [Gitter](https://gitter.im/PyTorch-NLP/Lobby) and join the [Google Group](https://groups.google.com/forum/#!forum/pytorch-nlp), we're eager to collaborate with you.
        
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pytorch-nlp.svg?style=flat-square)
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        [![Downloads](http://pepy.tech/badge/pytorch-nlp)](http://pepy.tech/project/pytorch-nlp)
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        ## Installation
        
        Make sure you have Python 3.5+ and PyTorch 0.4 or newer. You can then install `pytorch-nlp` using
        pip:
        
            pip install pytorch-nlp
        
        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 a Simple Recurrent Unit (SRU):
        
        ```python
        from torchnlp.nn import SRU
        import torch
        
        input_ = torch.autograd.Variable(torch.randn(6, 3, 10))
        sru = SRU(10, 20)
        
        # Apply a Simple Recurrent Unit to `input_`
        sru(input_)
        # RETURNS: (
        #   output [torch.FloatTensor (6x3x20)],
        #   hidden_state [torch.FloatTensor (2x3x20)]
        # )
        ```
        
        ### [Encode Text](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.text_encoders.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.text_encoders 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.LongTensor([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 100]
        ```
        
        ### 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: pytorch nlp text torchtext torchnlp
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
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: >=3.5
Description-Content-Type: text/markdown
