sent2vec 0.2.0

How to encode sentences in a high-dimensional vector space, a.k.a., sentence embedding.

A Generic Sentence Embedding Library

In the past, we mostly encode text data using, for example, one-hot, term frequency, or TF-IDF (normalized term frequency). There are many challenges to these techniques. In recent years, the latest advancements give us the opportunity to encode sentences or words in more meaningful formats. The word2vec technique and BERT language model are two important ones.

The sentence embedding is an important step of various NLP tasks such as sentiment analysis and summarization. A flexible sentence embedding library is needed to prototype fast and contextualized. The open-source sent2vec Python package gives you the opportunity to do so. You currently have access to the standard encoders. More advanced techniques will be added in the later releases. Hope you can use this library in your exciting NLP projects.

Install

The sent2vec is developed to help you prototype faster. That is why it has many dependencies on other libraries. The module requires the following libraries:

• gensim
• numpy
• spacy
• transformers
• torch

Then, it can be installed using pip:

pip3 install sent2vec

Usage

If you want to use the BERT language model (more specifically, distilbert-base-uncased) to encode sentences for downstream applications, you must use the code below.

from sent2vec.vectorizer import Vectorizer

sentences = [
    "This is an awesome book to learn NLP.",
    "DistilBERT is an amazing NLP model.",
    "We can interchangeably use embedding, encoding, or vectorizing.",
]
vectorizer = Vectorizer()
vectorizer.bert(sentences)
vectors = vectorizer.vectors

Now, you can compute distance among sentences by using their vectors. In the example, as expected, the distance between vectors[0] and vectors[1] is less than the distance between vectors[0] and vectors[2].

from scipy import spatial

dist_1 = spatial.distance.cosine(vectors[0], vectors[1])
dist_2 = spatial.distance.cosine(vectors[0], vectors[2])
print('dist_1: {0}, dist_2: {1}'.format(dist_1, dist_2))
assert dist_1 < dist_2
# dist_1: 0.043, dist_2: 0.192

If you want to use a word2vec approach instead, you must first split sentences into lists of words using the sent2words method from the Splitter class. In this stage, you can customize the list of stop-words by adding or removing to/from the default list. When you extract the most important words in sentences, you can compute the sentence embeddings using the word2vec method from the Vectorizer class. This method computes the average of vectors corresponding to the remaining words using the code below.

from sent2vec.vectorizer import Vectorizer
from sent2vec.splitter import Splitter

sentences = [
    "Alice is in the Wonderland.",
    "Alice is not in the Wonderland.",
]

splitter = Splitter()
splitter.sent2words(sentences=sentences, remove_stop_words=['not'], add_stop_words=[])
# print(splitter.words)
# [['alice', 'wonderland'], ['alice', 'not', 'wonderland']]
vectorizer = Vectorizer()
vectorizer.word2vec(splitter.words, pretrained_vectors_path= MODEL_PATH)
vectors = vectorizer.vectors

As seen above, you can use different word2vec models by sending its path to the word2vec method. You can use a pre-trained model or a customized one.

And, that's pretty much it!