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:zap: :card_index: token2index: A lightweight but powerful library for token indexing

token2index is a small yet powerful library facilitating the fast and easy creation of a data structure mapping tokens to indices, primarily aimed at applications for Natural Language Processing. The library is fully tested, and does not require any additional requirements. The documentation can be found here, some feature highlights are shown below.

Who / what is this for?

This class is written to be used for NLP applications where we want to assign an index to every word in a sequence e.g. to be later used to look up corresponding word embeddings. Building an index and indexing batches of sequences for Deep Learning models using frameworks like PyTorch or Tensorflow are common steps but are often written from scratch every time. This package provides a ready-made package combining many useful features, like reading vocabulary files, building indices from a corpus or indexing entire batches in one single function call, all while being fully tested.

:sparkles: Feature Highlights

• Building and extending vocab

  One way to build the index from a corpus is using the build() function:

  >>> from t2i import T2I
  >>> t2i = T2I.build(["colorless green ideas dream furiously", "the horse raced past the barn fell"])
  >>> t2i
  T2I(Size: 13, unk_token: <unk>, eos_token: <eos>, pad_token: <pad>, {'colorless': 0, 'green': 1, 'ideas': 2, 'dream': 3, 'furiously': 4, 'the': 5, 'horse': 6, 'raced': 7, 'past': 8, 'parn': 9, 'fell': 10, '<unk>': 11, '<eos>': 12, '<pad>': 13})
  

  The index can always be extended again later using extend():

  >>> t2i = t2i.extend("completely new words")
  T2I(Size: 16, unk_token: <unk>, eos_token: <eos>, pad_token: <pad>, {'colorless': 0, 'green': 1, 'ideas': 2, 'dream': 3, 'furiously': 4, 'the': 5, 'horse': 6, 'raced': 7, 'past': 8, 'barn': 9, 'fell': 10, 'completely': 13, 'new': 14, 'words': 15, '<unk>': 16, '<eos>': 17, '<pad>': 18})
  

  Both methods and index() also work with an already tokenized corpus in the form of

    [["colorless", "green", "ideas", "dream", "furiously"], ["the", "horse", "raced", "past", "the", "barn", "fell"]]    
  

• Easy indexing (of batches)

  Index multiple sentences at once in a single function call!

  >>> t2i.index(["the green horse raced <eos>", "ideas are a dream <eos>"])
  [[5, 1, 6, 7, 12], [2, 11, 11, 3, 12]]
  

  where unknown tokens are always mapped to unk_token.

• Easy conversion back to strings

  Reverting indices back to strings is equally as easy:

  >>> t2i.unindex([5, 14, 16, 3, 6])
  'the new <unk> dream horse'
  

• Automatic padding

  You are indexing multiple sentences of different length and want to add padding? No problem! index() has two options available via the pad_to argument. The first is padding to the maximum length of all the sentences:

  >>> padded_sents = t2i.index(["the green horse raced <eos>", "ideas <eos>"], pad_to="max")
  >>> padded_sents
  [[5, 1, 6, 7, 12], [2, 12, 13, 13, 13]]
  >>> t2i.unindex(padded_sents)
  [['the green horse raced <eos>', 'ideas <eos> <pad> <pad> <pad>']]
  

  Alternatively, you can also pad to a pre-defined length:

  >>> padded_sents = t2i.index(["the green horse <eos>", "past ideas <eos>"], pad_to=5)
  >>> padded_sents
  [[5, 1, 6, 12, 13], [8, 2, 12, 13, 13]]
  >>> t2i.unindex(padded_sents)
  [['the green horse <eos> <pad>', 'past ideas <eos> <pad> <pad>']]
  

• Vocab from file

  Using T2I.from_file(), the index can be created directly by reading from an existing vocab file. Refer to its documentation here for more info.

• Fixed memory size

  Although the defaultdict class from Python's collections package also posses the functionality to map unknown keys to a certain value, it grows in size for every new key. T2I memory size stays fixed after the index is built.

• Support for special tokens

  To enable flexibility in modern NLP applications, T2I allows for an arbitrary number of special tokens (like a masking or a padding token) during init!

  >>> t2i = T2I(special_tokens=["<mask>"])
  >>> t2i
  T2I(Size: 3, unk_token: <unk>, eos_token: <eos>, pad_token: <pad>, {'<unk>': 0, '<eos>': 1, '<mask>': 2, '<pad>': 3})
  

• Explicitly supported programmer laziness

  Too lazy to type? The library saves you a few keystrokes here and there. instead of calling t2i.index(...) you can directly call t2i(...) to index one or multiple sequences. Furthermore, key functions like index(), unindex(), build() and extend() support strings or iterables of strings as arguments alike.

:electric_plug: Compatibility with other frameworks (Numpy, PyTorch, Tensorflow)

It is also ensured that T2I is easily compatible with frameworks like Numpy, PyTorch and Tensorflow, without needing them as requirements:

Numpy

>>> import numpy as np
>>> t = np.array(t2i.index(["the new words are ideas <eos>", "the green horse <eos> <pad> <pad>"]))
>>> t
array([[ 5, 15, 16, 17,  2, 18],
   [ 5,  1,  6, 18, 19, 19]])
>>> t2i.unindex(t)
['the new words <unk> ideas <eos>', 'the green horse <eos> <pad> <pad>']

PyTorch

>>> import torch
>>> t = torch.LongTensor(t2i.index(["the new words are ideas <eos>", "the green horse <eos> <pad> <pad>"]))
>>> t
tensor([[ 5, 15, 16, 17,  2, 18],
    [ 5,  1,  6, 18, 19, 19]])
>>> t2i.unindex(t)
['the new words <unk> ideas <eos>', 'the green horse <eos> <pad> <pad>']

Tensorflow

>>> import tensorflow as tf
>>> t = tf.convert_to_tensor(t2i.index(["the new words are ideas <eos>", "the green horse <eos> <pad> <pad>"]), dtype=tf.int32)
>>> t
tensor([[ 5, 15, 16, 17,  2, 18],
    [ 5,  1,  6, 18, 19, 19]])
>>> t2i.unindex(t)
['the new words <unk> ideas <eos>', 'the green horse <eos> <pad> <pad>']

:inbox_tray: Installation

Installation can simply be done using pip:

pip3 install token2index

:mortar_board: Citing

If you use token2index for research purposes, please cite the library using the following citation info:

@misc{ulmer2020token2index,
    title={token2index: A lightweight but powerful library for token indexing},
    author={Ulmer, Dennis},
    journal={https://github.com/Kaleidophon/token2index},
    year={2020}
}