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"description": "![](\"docs/_static/img/logo.svg\")
\n\nSupporting Rapid Prototyping with a Deep Learning NLP Toolkit \n \n ![\"Tweet\"](\"https://img.shields.io/twitter/url/http/shields.io.svg?style=social\")
\n \n
\n\nPyTorch-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.\n\nJoin 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.\n\n\n[](https://codecov.io/gh/PetrochukM/PyTorch-NLP)\n[](http://pepy.tech/project/pytorch-nlp)\n[](http://pytorchnlp.readthedocs.io/en/latest/?badge=latest&style=flat-square)\n[](https://travis-ci.org/PetrochukM/PyTorch-NLP)\n\n_Logo by [Chloe Yeo](http://www.yeochloe.com/)_\n\n## Installation\n\nMake sure you have Python 3.6+ and PyTorch 1.0+. You can then install `pytorch-nlp` using\npip:\n\n pip install pytorch-nlp\n\nOr to install the latest code via:\n\n pip install git+https://github.com/PetrochukM/PyTorch-NLP.git\n\n## Docs \ud83d\udcd6\n\nThe complete documentation for PyTorch-NLP is available via [our ReadTheDocs website](https://pytorchnlp.readthedocs.io).\n\n## Basics\n\nAdd PyTorch-NLP to your project by following one of the common use cases:\n\n### Load a [Dataset](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.datasets.html)\n\nLoad the IMDB dataset, for example:\n\n```python\nfrom torchnlp.datasets import imdb_dataset\n\n# Load the imdb training dataset\ntrain = imdb_dataset(train=True)\ntrain[0] # RETURNS: {'text': 'For a movie that gets..', 'sentiment': 'pos'}\n```\n\n### Apply [Neural Networks](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.nn.html) Layers\n\nFor example, from the neural network package, apply state-of-the-art LockedDropout:\n\n```python\nimport torch\nfrom torchnlp.nn import LockedDropout\n\ninput_ = torch.randn(6, 3, 10)\ndropout = LockedDropout(0.5)\n\n# Apply a LockedDropout to `input_`\ndropout(input_) # RETURNS: torch.FloatTensor (6x3x10)\n```\n\n### [Encode Text](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.encoders.text.html)\n\nTokenize and encode text as a tensor. For example, a `WhitespaceEncoder` breaks text into terms whenever it encounters a whitespace character.\n\n```python\nfrom torchnlp.encoders.text import WhitespaceEncoder\n\n# Create a `WhitespaceEncoder` with a corpus of text\nencoder = WhitespaceEncoder([\"now this ain't funny\", \"so don't you dare laugh\"])\n\n# Encode and decode phrases\nencoder.encode(\"this ain't funny.\") # RETURNS: torch.Tensor([6, 7, 1])\nencoder.decode(encoder.encode(\"This ain't funny.\")) # RETURNS: \"this ain't funny.\"\n```\n\n### Load [Word Vectors](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.word_to_vector.html)\n\nFor example, load FastText, state-of-the-art English word vectors:\n\n```python\nfrom torchnlp.word_to_vector import FastText\n\nvectors = FastText()\n# Load vectors for any word as a `torch.FloatTensor`\nvectors['hello'] # RETURNS: [torch.FloatTensor of size 100]\n```\n\n### Compute [Metrics](http://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.metrics.html)\n\nFinally, compute common metrics such as the BLEU score.\n\n```python\nfrom torchnlp.metrics import get_moses_multi_bleu\n\nhypotheses = [\"The brown fox jumps over the dog \u7b11\"]\nreferences = [\"The quick brown fox jumps over the lazy dog \u7b11\"]\n\n# Compute BLEU score with the official BLEU perl script\nget_moses_multi_bleu(hypotheses, references, lowercase=True) # RETURNS: 47.9\n```\n\n### Help :question:\n\nMaybe looking at longer examples may help you at [`examples/`](examples/).\n\nNeed more help? We are happy to answer your questions via [Gitter Chat](https://gitter.im/PyTorch-NLP)\n\n## Contributing\n\nWe'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.\n\n### Contributing Guide\n\nRead 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.\n\n## Related Work\n\n### [torchtext](https://github.com/pytorch/text)\n\ntorchtext 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.\n\n### [AllenNLP](https://github.com/allenai/allennlp)\n\nAllenNLP is designed to be a platform for research. PyTorch-NLP is designed to be a lightweight toolkit.\n\n## Authors\n\n* [Michael Petrochuk](https://github.com/PetrochukM/) \u2014 Developer\n* [Chloe Yeo](http://www.yeochloe.com/) \u2014 Logo Design\n\n## Citing\n\nIf you find PyTorch-NLP useful for an academic publication, then please use the following BibTeX to cite it:\n\n```\n@misc{pytorch-nlp,\n author = {Petrochuk, Michael},\n title = {PyTorch-NLP: Rapid Prototyping with PyTorch Natural Language Processing (NLP) Tools},\n year = {2018},\n publisher = {GitHub},\n journal = {GitHub repository},\n howpublished = {\\url{https://github.com/PetrochukM/PyTorch-NLP}},\n}\n```\n\n\n",
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