spacy-universal-sentence-encoder 0.3.4

SpaCy models for using Universal Sentence Encoder from TensorFlow Hub

Spacy - Universal Sentence Encoder

Make use of Google's Universal Sentence Encoder directly within SpaCy. This library lets you embed Docs, Spans and Tokens from the Universal Sentence Encoder family available on TensorFlow Hub.

For using sentence-BERT in spaCy, see https://github.com/MartinoMensio/spacy-sentence-bert

Motivation

There are many different reasons to not always use BERT. For example to have embeddings that are tuned specifically for another task (e.g. sentence similarity). See this very useful blog article: https://blog.floydhub.com/when-the-best-nlp-model-is-not-the-best-choice/

The Universal Sentence Encoder is trained on different tasks which are more suited to identifying sentence similarity. Google AI blog paper

This library uses the user_hooks of spaCy to use an external model for the vectors, in this case a simple wrapper to the models available on TensorFlow Hub.

Install

You can install this library from:

• github: pip install git+https://github.com/MartinoMensio/spacy-universal-sentence-encoder.git
• pyPI: pip install spacy-universal-sentence-encoder

To use the multilingual version of the models, you need to install the extra named multi with the command: pip install spacy-universal-sentence-encoder[multi]. This installs the dependency tensorflow-text that is required to run the multilingual models. Note that this library is still not available for Windows operating systems (https://github.com/tensorflow/text/issues/291).

In alternative, you can install the following standalone pre-packaged models with pip. The same limitation for multilingual models applies (when trying to install a multilingual model on Windows, pip will say that no tensorflow-text is available). Each model can be installed independently:

model name	source	pip package
en_use_md	https://tfhub.dev/google/universal-sentence-encoder	pip install https://github.com/MartinoMensio/spacy-universal-sentence-encoder/releases/download/v0.3.4/en_use_md-0.3.4.tar.gz#en_use_md-0.3.4
en_use_lg	https://tfhub.dev/google/universal-sentence-encoder-large	pip install https://github.com/MartinoMensio/spacy-universal-sentence-encoder/releases/download/v0.3.4/en_use_lg-0.3.4.tar.gz#en_use_lg-0.3.4
xx_use_md	https://tfhub.dev/google/universal-sentence-encoder-multilingual	pip install https://github.com/MartinoMensio/spacy-universal-sentence-encoder/releases/download/v0.3.4/xx_use_md-0.3.4.tar.gz#xx_use_md-0.3.4
xx_use_lg	https://tfhub.dev/google/universal-sentence-encoder-multilingual-large	pip install https://github.com/MartinoMensio/spacy-universal-sentence-encoder/releases/download/v0.3.4/xx_use_lg-0.3.4.tar.gz#xx_use_lg-0.3.4

Usage

First you have to import your model.

If you installed the model standalone packages (see table above) you can use the usual spacy API to load this model:

import spacy
nlp = spacy.load('en_use_md')

Otherwise you need to load the model in the following way (the first time that it is run, it downloads the model):

import spacy_universal_sentence_encoder
nlp = spacy_universal_sentence_encoder.load_model('xx_use_lg')

Then you can use the models:

# get two documents
doc_1 = nlp('Hi there, how are you?')
doc_2 = nlp('Hello there, how are you doing today?')
# get the vector of the Doc, Span or Token
print(doc_1.vector.shape)
print(doc_1[3].vector.shape)
print(doc_1[2:4].vector.shape)
# or use the similarity method that is based on the vectors, on Doc, Span or Token
print(doc_1.similarity(doc_2[0:7]))

You can use the model on an already available language pipeline (e.g. to integrate with your custom components or to have better parsing than the base spaCy model used here):

import spacy
import spacy_universal_sentence_encoder
# this is your nlp object that can be any spaCy model
nlp = spacy.load('en_core_web_sm')


# get the pipe component
use_overwrite_vectors = nlp.create_pipe('use_overwrite_vectors')
# add to your nlp the pipeline stage
nlp.add_pipe(use_overwrite_vectors)
# use the vector with the default `en_use_md` model
doc = nlp('Hi')

# extend the nlp pipeline with the `en_use_lg` model
spacy_universal_sentence_encoder.create_from(nlp, 'en_use_lg')

Common issues

Here you can find the most common issues with possible solutions.

Using a pre-downloaded model

If you want to use a model that you have already downloaded from TensorFlow Hub, belonging to the Universal Sentence Encoder family, you can use it by doing the following:

• locate the full path of the folder where you have downloaded and extracted the model. Let's suppose the location is /Users/foo/Downloads
• rename the folder of the extracted model (the one directly containing the folders variables and the file saved_model.pb) to the sha1 hash of the TFHub model source. The mapping URL / sha1 values is the following:
  • en_use_md: 063d866c06683311b44b4992fd46003be952409c
  • en_use_lg: c9fe785512ca4a1b179831acb18a0c6bfba603dd
  • xx_use_md: 26c892ffbc8d7b032f5a95f316e2841ed4f1608c
  • xx_use_lg: 97e68b633b7cf018904eb965602b92c9f3ad14c9
• set the environment variable TFHUB_CACHE_DIR to the location containing the renamed folder, in our case /Users/foo/Downloads (set it before trying to download the model)
• Now load your model and it should see that it was already downloaded

Serialisation

To serialise and deserialise nlp objects, SpaCy does not restore user_hooks after deserialisation, so a call to from_bytes will result in not using the TensorFlow vectors, so the similarities won't be good. For this reason the suggested solution is:

• serialise with bytes = doc.to_bytes() normally
• deserialise with spacy_universal_sentence_encoder.doc_from_bytes(nlp, bytes) which will also restore the user hooks

Multiprocessing

This library, relying on TensorFlow, is not fork-safe. This means that if you are using this library inside multiple processes (e.g. with a multiprocessing.pool.Pool), your processes will deadlock. The solutions are:

• use a thread-based environment (e.g. multiprocessing.pool.ThreadPool)
• only use this library inside the created processes (first create the processes and then import and use the library)

Utils

To build and upload

# change version
VERSION=0.3.4
# change version references everywhere
# update locally installed package
pip install -r requirements.txt
# build the standalone models (17)
./build_models.sh
# build the archive at dist/spacy_universal_sentence_encoder-${VERSION}.tar.gz
python setup.py sdist
# upload to pypi
twine upload dist/spacy_universal_sentence_encoder-${VERSION}.tar.gz
# upload language packages to github