corpusit 0.2.0

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Corpusit

corpusit provides easy-to-use dataset iterators for natural language modeling tasks, such as SkipGram.

It is written in rust to enable fast multi-threading random sampling with deterministic results. So you dont have to worry about the speed / reproducibility.

Corpusit does not provide tokenization functionalities. So please use corpusit on tokenized corpus files (plain texts).

Environment

Python >= 3.6

Installation

$ pip install corpusit

On Windows and MacOS

Please install rust compiler before executing pip install corpusit.

Usage

SkipGram

Each line in the corpus file is a document, and the tokens should be separated by whitespace.

import corpusit

corpus_path = 'corpusit/data/corpus.txt'
vocab = corpusit.Vocab.build(corpus_path, min_count=1, unk='<unk>')

dataset = corpusit.SkipGramDataset(
    path_to_corpus=corpus_path,
    vocab=vocab,
    win_size=10,
    sep=" ",
    mode="onepass",       # onepass | repeat | shuffle
    subsample=1e-3,
    power=0.75,
    n_neg=1,
)

it = dataset.positive_sampler(batch_size=100, seed=0, num_threads=4)

for i, pair in enumerate(it):
    print(f'Iter {i:>4d}, shape={pair.shape}. First pair: '
          f'{pair[0,0]:>5} ({vocab.i2s[pair[0,0]]:>10}), '
          f'{pair[0,1]:>5} ({vocab.i2s[pair[0,1]]:>10})')

# Return:
# Iter    0, shape=(100, 2). First pair:    14 (        is),    10 ( anarchism)
# Iter    1, shape=(100, 2). First pair:     8 (        to),   540 (      and/)
# Iter    2, shape=(100, 2). First pair:   775 (constitutes),    34 (anarchists)
# Iter    3, shape=(100, 2). First pair:    72 (     other),   214 (  criteria)
# Iter    4, shape=(100, 2). First pair:   650 (  defining),   487 ( companion)
# ...

SkipGram with negative sampling

it = dataset.sampler(100, seed=0, num_threads=4)

for i, res in enumerate(it):
    pair, label = res
    print(f'Iter {i:>4d}, shape={pair.shape}. First pair: '
          f'{pair[0,0]:>5} ({vocab.i2s[pair[0,0]]:>10}), '
          f'{pair[0,1]:>5} ({vocab.i2s[pair[0,1]]:>10}), '
          f'label = {label[0]}')

# Returns:
# Iter    0, shape=(200, 2). First pair:    15 (        is),    10 ( anarchism), label = True
# Iter    1, shape=(200, 2). First pair:     9 (        to),   722 (      and/), label = True
# Iter    2, shape=(200, 2). First pair:   389 (constitutes),    34 (anarchists), label = True
# Iter    3, shape=(200, 2). First pair:    73 (     other),   212 (  criteria), label = True
# Iter    4, shape=(200, 2). First pair:   445 (  defining),   793 ( companion), label = True
# ...

Roadmap

• GloVe

License

MIT