πŸ¦† Use NLP to go beyond vanilla word2vec
Switch branches/tags
Nothing to show
Clone or download

README.rst

sense2vec: Use NLP to go beyond vanilla word2vec

sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting, detailed and context-sensitive word vectors. For an interactive example of the technology, see our sense2vec demo that lets you explore semantic similarities across all Reddit comments of 2015.

This library is a simple Python/Cython implementation for loading and querying sense2vec models. While it's best used in combination with spaCy, the sense2vec library itself is very lightweight and can also be used as a standalone module. See below for usage details.

πŸ¦† Version 1.0 alpha out now! Read the release notes here.

Build Status Current Release Version pypi Version

Usage Examples

Usage with spaCy

import spacy
from sense2vec import Sense2VecComponent

nlp = spacy.load('en')
s2v = Sense2VecComponent('/path/to/reddit_vectors-1.1.0')
nlp.add_pipe(s2v)

doc = nlp(u"A sentence about natural language processing.")
assert doc[3].text == u'natural language processing'
freq = doc[3]._.s2v_freq
vector = doc[3]._.s2v_vec
most_similar = doc[3]._.s2v_most_similar(3)
# [(('natural language processing', 'NOUN'), 1.0),
#  (('machine learning', 'NOUN'), 0.8986966609954834),
#  (('computer vision', 'NOUN'), 0.8636297583580017)]

Standalone usage without spaCy

import sense2vec

s2v = sense2vec.load('/path/to/reddit_vectors-1.1.0')
query = u'natural_language_processing|NOUN'
assert query in s2v
freq, vector = s2v[query]
words, scores = s2v.most_similar(vector, 3)
most_similar = list(zip(words, scores))
# [('natural_language_processing|NOUN', 1.0),
#  ('machine_learning|NOUN', 0.8986966609954834),
#  ('computer_vision|NOUN', 0.8636297583580017)]

Installation & Setup

Operating system macOS / OS X, Linux, Windows (Cygwin, MinGW, Visual Studio)
Python version CPython 2.7, 3.4+. Only 64 bit.
Package managers pip (source packages only)

sense2vec releases are available as source packages on pip:

pip install sense2vec==1.0.0a0

The Reddit vectors model is attached to the latest release. To load it in, download the .tar.gz archive, unpack it and point sense2vec.load to the extracted data directory:

import sense2vec
s2v = sense2vec.load('/path/to/reddit_vectors-1.1.0')

Usage

Usage with spaCy v2.x

The easiest way to use the library and vectors is to plug it into your spaCy pipeline. Note that sense2vec doesn't depend on spaCy, so you'll have to install it separately and download the English model.

pip install -U spacy
python -m spacy download en

The sense2vec package exposes a Sense2VecComponent, which can be initialised with the data path and added to your spaCy pipeline as a custom pipeline component. By default, components are added to the end of the pipeline, which is the recommended position for this component, since it needs access to the dependency parse and, if available, named entities.

import spacy
from sense2vec import Sense2VecComponent

nlp = spacy.load('en')
s2v = Sense2VecComponent('/path/to/reddit_vectors-1.1.0')
nlp.add_pipe(s2v)

The pipeline component will merge noun phrases and entities according to the same schema used when training the sense2vec models (e.g. noun chunks without determiners like "the"). This ensures that you'll be able to retrieve meaningful vectors for phrases in your text. The component will also add serveral extension attributes and methods to spaCy's Token and Span objects that let you retrieve vectors and frequencies, as well as most similar terms.

doc = nlp(u"A sentence about natural language processing.")
assert doc[3].text == u'natural language processing'
assert doc[3]._.in_s2v
freq = doc[3]._.s2v_freq
vector = doc[3]._.s2v_vec
most_similar = doc[3]._.s2v_most_similar(10)

For entities, the entity labels are used as the "sense" (instead of the token's part-of-speech tag):

doc = nlp(u"A sentence about Facebook and Google.")
for ent in doc.ents:
    assert ent._.in_s2v
    most_similar = ent._.s2v_most_similar(3)

Available attributes

The following attributes are available via the ._ property – for example token._.in_s2v:

Name Attribute Type Type Description
in_s2v property bool Whether a key exists in the vector map.
s2v_freq property int The frequency of the given key.
s2v_vec property ndarray[float32] The vector of the given key.
s2v_most_similar method list Get the n most similar terms. Returns a list of ((word, sense), score) tuples.

A note on span attributes: Under the hood, entities in doc.ents are Span objects. This is why the pipeline component also adds attributes and methods to spans and not just tokens. However, it's not recommended to use the sense2vec attributes on arbitrary slices of the document, since the model likely won't have a key for the respective text. Span objects also don't have a part-of-speech tag, so if no entity label is present, the "sense" defaults to the root's part-of-speech tag.

Standalone usage

To use only the sense2vec library, you can import the package and then call its load() method to load in the vectors.

import sense2vec
s2v = sense2vec.load('/path/to/reddit_vectors-1.1.0')

sense2vec.load returns an instance of the VectorMap class, which you can interact with via the following methods.

⚠️ Important note: When interacting with the VectorMap directly, the keys need to follow the scheme of phrase_text|SENSE (note the _ instead of spaces and the | before the tag or label) – for example, machine_learning|NOUN. Also note that the underlying vector table is case-sensitive.

VectorMap.__len__

The total number of entries in the map.

Argument Type Description
RETURNS int The number of entries in the map.
s2v = sense2vec.load('/path/to/reddit_vectors-1.1.0')
assert len(s2v) == 1195261

VectorMap.__contains__

Check whether the VectorMap has a given key. Keys consist of the word string, a pipe and the "sense", i.e. the part-of-speech tag or entity label. For example: 'duck|NOUN' or 'duck|VERB'. See the section on "Senses" below for more details. Also note that the underlying vector table is case-sensitive.

Argument Type Description
string unicode The key to check.
RETURNS bool Whether the key is part of the map.
assert u'duck|NOUN' in s2v
assert u'duck|VERB' in s2v
assert u'dkdksl|VERB' not in s2v

VectorMap.__getitem__

Retrieve a (frequency, vector) tuple from the vector map. The frequency is an integer, the vector a numpy.ndarray(dtype='float32'). If the key is not found, a KeyError is raised.

Argument Type Description
string unicode The key to retrieve the frequency and vector for.
RETURNS tuple The (frequency, vector) tuple.
freq, vector = s2v[u'duck|NOUN']

VectorMap.__setitem__

Assign a (frequency, vector) tuple to the vector map. The frequency should be an integer, the vector a numpy.ndarray(dtype='float32').

Argument Type Description
key unicode The key to assign the frequency and vector to.
value tuple The (frequency, vector) tuple to assign.
freq, vector = s2v[u'avocado|NOUN']
s2v[u'πŸ₯‘|NOUN'] = (freq, vector)

VectorMap.__iter__, VectorMap.keys

Iterate over the keys in the map, in order of insertion.

Argument Type Description
YIELDS unicode The keys in the map.

VectorMap.values

Iterate over the values in the map, in order of insertion and yield (frequency, vector) tuples from the vector map. The frequency is an integer, the vector a numpy.ndarray(dtype='float32')

Argument Type Description
YIELDS tuple The values in the map.

VectorMap.items

Iterate over the items in the map, in order of insertion and yield (key, (frequency, vector)) tuples from the vector map. The frequency is an integer, the vector a numpy.ndarray(dtype='float32')

Argument Type Description
YIELDS tuple The items in the map.

VectorMap.most_similar

Find the keys of the n most similar entries, given a vector. Note that the most similar entry with a score of 1.0 will be the key of the query vector itself.

Argument Type Description
vector numpy.ndarray(dtype='float32') The vector to compare to.
n int The number of entries to return. Defaults to 10.
RETURNS tuple A (words, scores) tuple.
freq, vector = s2v[u'avocado|NOUN']
words, scores = s2v.most_similar(vector, n=3)
for word, score in zip(words, scores):
    print(word, score)
# avocado|NOUN 1.0
# avacado|NOUN 0.970944344997406
# spinach|NOUN 0.962776780128479

VectorMap.save

Serialize the model to a directory. This will export three files to the output directory: a strings.json containing the keys in insertion order, a freqs.json containing the frequencies and a vectors.bin containing the vectors.

Argument Type Description
data_dir unicode The path to the output directory.

VectorMap.load

Load a model from a directory. Expects three files in the directory (see VectorMap.save for details).

Argument Type Description
data_dir unicode The path to load the model from.

Senses

The pre-trained Reddit vectors support the following "senses", either part-of-speech tags or entity labels. For more details, see spaCy's annotation scheme overview.

Tag Description Examples
ADJ adjective big, old, green
ADP adposition in, to, during
ADV adverb very, tomorrow, down, where
AUX auxiliary is, has (done), will (do)
CONJ conjunction and, or, but
DET determiner a, an, the
INTJ interjection psst, ouch, bravo, hello
NOUN noun girl, cat, tree, air, beauty
NUM numeral 1, 2017, one, seventy-seven, MMXIV
PART particle 's, not
PRON pronoun I, you, he, she, myself, somebody
PROPN proper noun Mary, John, London, NATO, HBO
PUNCT punctuation , ? ( )
SCONJ subordinating conjunction if, while, that
SYM symbol $, %, =, :), 😝
VERB verb run, runs, running, eat, ate, eating
Entity Label Description
PERSON People, including fictional.
NORP Nationalities or religious or political groups.
FACILITY Buildings, airports, highways, bridges, etc.
ORG Companies, agencies, institutions, etc.
GPE Countries, cities, states.
LOC Non-GPE locations, mountain ranges, bodies of water.
PRODUCT Objects, vehicles, foods, etc. (Not services.)
EVENT Named hurricanes, battles, wars, sports events, etc.
WORK_OF_ART Titles of books, songs, etc.
LANGUAGE Any named language.

Training a sense2vec model

🚧 TODO: Update training scripts for spaCy v2.x.