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skip-thoughts

Simple implementation of skip-thought vectors (Kiros et al., 2015). This repository can be used to generate general-purpose sentence embeddings for numerous NLP tasks, and gives the means to obtain the data, train the model, and port it to any python script.

Usage

This code is written for python 3.6. To download, clone this repository:

git clone https://github.com/danielwatson6/skip-thoughts.git

Obtaining the training data

To obtain the training data, navigate to [https://www.smashwords.com/] and navigate the website to restrict the books to obtain to the desired categories (e.g. only free books of >=20,000 word length, of a certain genre, etc.). The resulting URL in the browser with the paginated list of books can be passed to this script to download all books in English that are available in text file format:

python smashwords.py [URL] [SAVE_DIRECTORY (defaults to data/books)]
# Example: python smashwords.py https://www.smashwords.com/books/category/1/newest/0/free/medium

We use Google's pre-trained 300-dimensional word vectors, which can be downloaded here (this link just mirrors the download link of the official website). The general model is of course independent of what word vectors are fed.

Preprocessing

To clean the training data, there is a script provided that will textually normalize (sentences are extracted and only alphanumerics and apostrophes are kept) all the files in the input directory, convert the words to unique integer IDs according to the provided word vector model, and save them in the TensorFlow binary format. See the help page for further details:

python clean.py --help

Training the model

To train the model, change hyperparameters, or get sentence embeddings, see the help page of the training script:

python train.py --help

Running a trained model

To use the model in any python script, follow this basic pattern:

import tensorflow as tf
from gensim.models import KeyedVectors

from skip_thoughts import SkipThoughts


# Initialize the word2vec and skip-thoughts models only once:
word2vec_model = KeyedVectors.load('path/to/word2vec_file', mmap='r')
graph = tf.Graph()
with graph.as_default():
  # Refer to the constructor docstring for more information on the arguments.
  model = SkipThoughts(word2vec_model, **kwargs)

with tf.Session(graph=graph):
  # Restore the model only once.
  # Here, `save_dir` is the directory where the .ckpt files live. Typically
  # this would be "output/mymodel" where --model_name=mymodel in train.py.
  model.restore(save_dir)
  
  # Run the model like this as many times as desired.
  print(model.encode(sentence_strings))

Evaluating a trained model

We provide an evaluation script to test the quality of the sentence vectors produced by the trained model.

Dependencies

All the dependencies are listed in the requirements.txt file. They can be installed with pip as follows:

pip install -r requirements.txt