TorchLanguage

Popular datasets, model architectures, and common image transformations for natural language processing

artificial intelligence, machine learning, deep learning, natural language processing, text, classification

TorchLanguage

TorchLanguage is the equivalent of TorchVision for Natural Language Processing. It gives you access to text transformers (tokens, index, n-grams, etc) and data sets.

      
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This repository consists of:

  • torchlanguage.datasets : Pre-built datasets for common NLP tasks
  • torchlanguage.models : Generic pretrained models for common NLP tasks
  • torchlanguage.transforms : Common transformation for text
  • torchlanguage.utils : Tools, functions and measures for NLP

Installation

Make sure you have Python 2.7 or 3.5+ and PyTorch 0.2.0 or newer. You can then install torchlanguage using pip :

pip install TorchLanguage

Optional requirements

If you want to use English tokenizer from SpaCy <http://spacy.io/>_, you need to install SpaCy and download its English model:

pip install spacy
python -m spacy download en

Text transformation pipeline

The following transformation are available :

  • Character
  • Character2Gram
  • Character3Gram
  • Compose
  • DropOut
  • Embedding
  • FunctionWord
  • GensimModel
  • GloveVector
  • HorizontalStack
  • MaxIndex
  • PartOfSpeech
  • RandomSamples
  • RemoveCharacter
  • RemoveLines
  • RemoveRegex
  • Tag
  • ToFrequencyVector
  • ToIndex
  • Token
  • ToLength
  • ToLower
  • ToNGram
  • ToOneHot
  • ToUpper
  • Transformer
  • VerticalStack

Data

The data module provides the following:

  • Ability to download and load a corpus from a directory. The file must be name Class_Title.txt:
dataset = torchlanguage.datasets.FileDirectory(
   root='./data',
   download=True,
   download_url="http://urltozip/file.zip",
   transform=transformer
)
  • Wrapper for dataset splits (train, validation) and cross-validation:
cross_val_dataset = {'train': torchlanguage.utils.CrossValidation(dataset, k=k),
   'test': torchlanguage.utils.CrossValidation(dataset, k=k, train=False)}
for k in range(k):
   for data in cross_val_dataset['train']:
      inputs, label = data
   # end for
   for data in cross_val_dataset['test']:
      inputs, label = data
   # end for
   cross_val_dataset['train'].next_fold()
   cross_val_dataset['test'].next_fold()
# end for

Datasets

The datasets module currently contains:

  • FileDirectory: Load a corpus from a directory
  • ReutersC50Dataset: The Reuters C50 dataset for authorship attribution
  • SFGram: A set of science-fiction magazine with five authors.

Others are planned or a work in progress:

  • Traduction
  • Question answering

See the examples directory for examples of dataset usage.

Related Work

echotorch

EchoTorch is a Python framework to easily implement Reservoir Computing models with pyTorch.

Authors

Citing

If you find TorchLanguage useful for an academic publication, then please use the following BibTeX to cite it:

@misc{torchlanguage,
   author = {Schaetti, Nils},
   title = {TorchLanguage: Natural Language Processing with pyTorch},
   year = {2018},
   publisher = {GitHub},
   journal = {GitHub repository},
   howpublished = {\url{https://github.com/nschaetti/TorchLanguage}},
}

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