Fast Sentence Embeddings is a Python library that serves as an addition to Gensim. This library is intended to compute sentence vectors for large collections of sentences or documents.
Find the corresponding blog post(s) here:
fse implements three algorithms for sentence embeddings. You can choose between unweighted sentence averages, smooth inverse frequency averages, and unsupervised smooth inverse frequency averages.
Key features of fse are:
[X] Up to 500.000 sentences / second (1)
[X] Supports Average, SIF, and uSIF Embeddings
[X] Full support for Gensims Word2Vec and all other compatible classes
[X] Full support for Gensims FastText with out-of-vocabulary words
[X] Induction of word frequencies for pre-trained embeddings
[X] Incredibly fast Cython core routines
[X] Dedicated input file formats for easy usage (including disk streaming)
[X] Ram-to-disk training for large corpora
[X] Disk-to-disk training for even larger corpora
[X] Many fail-safe checks for easy usage
[X] Simple interface for developing your own models
[X] Extensive documentation of all functions
[X] Optimized Input Classes
(1) May vary significantly from system to system (i.e. by using swap memory) and processing. I regularly observe 300k-500k sentences/s for preprocessed data on my Macbook (2016). Visit Tutorial.ipynb for an example.
This software depends on NumPy, Scipy, Scikit-learn, Gensim, and Wordfreq. You must have them installed prior to installing fse. Required Python version is 3.6.
As with gensim, it is also recommended you install a BLAS library before installing fse.
The simple way to install fse is:
pip install -U fse
In case you want to build from source, just run:
python setup.py install
If building the Cython extension fails (you will be notified), try:
pip install -U git+https://github.com/oborchers/Fast_Sentence_Embeddings
Within the folder nootebooks you can find the following guides:
Tutorial.ipynb offers a detailed walk-through of some of the most important functions fse has to offer.
STS-Benchmarks.ipynb contains an example of how to use the library with pre-trained models to replicate the STS Benchmark results [4] reported in the papers.
Speed Comparision.ipynb compares the speed between the numpy and the cython routines.
In order to use the fse model, you first need some pre-trained gensim word embedding model, which is then used by fse to compute the sentence embeddings.
After computing sentence embeddings, you can use them in supervised or unsupervised NLP applications, as they serve as a formidable baseline.
The models presented are based on
- Deep-averaging embeddings [1]
- Smooth inverse frequency embeddings [2]
- Unsupervised smooth inverse frequency embeddings [3]
Credits to Radim Řehůřek and all contributors for the awesome library and code that Gensim provides. A whole lot of the code found in this lib is based on Gensim.
In order to use fse you must first estimate a Gensim model which contains a gensim.models.keyedvectors.BaseKeyedVectors class, for example Word2Vec or Fasttext. Then you can proceed to compute sentence embeddings for a corpus.
from gensim.models import FastText
sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]]
ft = FastText(sentences, min_count=1, size=10)
from fse.models import Average
from fse import IndexedList
model = Average(ft)
model.train(IndexedList(sentences))
model.sv.similarity(0,1)
fse offers multi-thread support out of the box. However, for most applications a single thread will most likely be sufficient.
To install fse on Colab, check out: https://colab.research.google.com/drive/1qq9GBgEosG7YSRn7r6e02T9snJb04OEi
| Model | STS Benchmark |
|---|---|
CBOW-Paranmt |
79.85 |
uSIF-Paranmt |
79.02 |
SIF-Paranmt |
76.75 |
SIF-Paragram |
73.86 |
uSIF-Paragram |
73.64 |
SIF-FT |
73.38 |
SIF-Glove |
71.95 |
SIF-W2V |
71.12 |
uSIF-FT |
69.4 |
uSIF-Glove |
67.16 |
uSIF-W2V |
66.99 |
CBOW-W2V |
61.54 |
CBOW-Paragram |
50.38 |
CBOW-FT |
48.49 |
CBOW-Glove |
40.41 |
0.1.15 from 0.1.11:
- Fixed major FT Ngram computation bug
- Rewrote the input class. Turns out NamedTuple was pretty slow.
- Added further unittests
- Added documentation
- Major speed improvements
- Fixed division by zero for empty sentences
- Fixed overflow when infer method is used with too many sentences
- Fixed similar_by_sentence bug
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Iyyer M, Manjunatha V, Boyd-Graber J, Daumé III H (2015) Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Int. Jt. Conf. Nat. Lang. Process., 1681–1691.
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Arora S, Liang Y, Ma T (2017) A Simple but Tough-to-Beat Baseline for Sentence Embeddings. Int. Conf. Learn. Represent. (Toulon, France), 1–16.
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Ethayarajh K (2018) Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline. Proceedings of the 3rd Workshop on Representation Learning for NLP. (Toulon, France), 91–100.
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Eneko Agirre, Daniel Cer, Mona Diab, Iñigo Lopez-Gazpio, Lucia Specia. Semeval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation. Proceedings of SemEval 2017.
Author: Oliver Borchers borchers@bwl.uni-mannheim.de
Copyright (C) 2019 Oliver Borchers
If you found this software useful, please cite it in your publication.
@misc{Borchers2019,
author = {Borchers, Oliver},
title = {Fast sentence embeddings},
year = {2019},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/oborchers/Fast_Sentence_Embeddings}},
}