fgaim / hornvecs

Subword informed Word Embedding for Templatic Languages: Tigrinya, Amharic, Arabic ...

Table of contents

• Introduction
• Resources
  • Models
  • Supplementary data
  • FAQ
  • Cheatsheet
• Requirements
• Building hornVecs
  • Getting the source code
  • Building hornVecs using cmake (preferred)
  • Building hornVecs using make
  • Building hornVecs for Python
• Example use cases
  • Word representation learning
  • Obtaining word vectors for out-of-vocabulary words
  • Text classification
• Full documentation
• References
• License

Introduction

HornVecs is a library for efficient learning of word representations and sentence classification of Semitic Languages, particulary Tigrinya and Amahric. It is built on top of the fantastic fastText by Facebook.

The main contribution of HornVecs is that it considers the innate non-concatenative morphology of the Semitic languages when building the subwords for word embeddings. On the cases of Tigrinya and Amharic, we have observed fairly good performance gains when using HornVecs embeddings over fastText, word2vec, and GloVe.

Even though HornVecs can be used for all languages that fastText supports, for languages other than the Semitic family we recommend you to use fastText.

Resources

Models

• Recent state-of-the-art English word vectors.
• Word vectors for 294 languages trained on Wikipedia.
• Models for language identification and various supervised tasks.

Supplementary data

• The preprocessed YFCC100M data used in [2].

FAQ

You can find answers to frequently asked questions on our website.

Cheatsheet

We also provide a cheatsheet full of useful one-liners.

Requirements

We are continuously building and testing our library, CLI and Python bindings under various docker images using circleci.

Generally, hornVecs builds on modern Mac OS and Linux distributions. Since it uses some C++11 features, it requires a compiler with good C++11 support. These include :

• (g++-4.7.2 or newer) or (clang-3.3 or newer)

Compilation is carried out using a Makefile, so you will need to have a working make. If you want to use cmake you need at least version 2.8.9.

One of the oldest distributions we successfully built and tested the CLI under is Debian wheezy.

For the word-similarity evaluation script you will need:

• Python 2.6 or newer
• NumPy & SciPy

For the python bindings (see the subdirectory python) you will need:

• Python version 2.7 or >=3.4
• NumPy & SciPy
• pybind11

One of the oldest distributions we successfully built and tested the Python bindings under is Debian jessie.

If these requirements make it impossible for you to use hornVecs, please open an issue and we will try to accommodate you.

Building hornVecs

We discuss building the latest stable version of hornVecs.

Getting the source code

You can find our latest stable release in the usual place.

There is also the master branch that contains all of our most recent work, but comes along with all the usual caveats of an unstable branch. You might want to use this if you are a developer or power-user.

Building hornVecs using cmake

For now this is not part of a release, so you will need to clone the master branch.

$ git clone https://github.com/fgaim/HornVecs.git
$ cd hornVecs
$ mkdir build && cd build && cmake ..
$ make && make install

This will create the hornvecs binary and also all relevant libraries (shared, static, PIC).

Building hornVecs using make

$ wget https://github.com/fgaim/HornVecs/archive/v0.1.0.zip
$ unzip v0.1.0.zip
$ cd hornVecs-0.1.0
$ make

This will produce object files for all the classes as well as the main binary hornvecs. If you do not plan on using the default system-wide compiler, update the two macros defined at the beginning of the Makefile (CC and INCLUDES).

Building hornVecs for Python

For now this is not part of a release, so you will need to clone the master branch.

$ git clone https://github.com/fgaim/HornVecs.git
$ cd hornVecs
$ pip install .

For further information and introduction see python/README.md

Example use cases

This library has two main use cases: word representation learning and text classification.

Word representation learning

In order to learn word vectors, do:

$ ./hornvecs skipgram -input data.txt -output model

where data.txt is a training file containing UTF-8 encoded text. By default the word vectors will take into account character n-grams from 3 to 6 characters. At the end of optimization the program will save two files: model.bin and model.vec. model.vec is a text file containing the word vectors, one per line. model.bin is a binary file containing the parameters of the model along with the dictionary and all hyper parameters. The binary file can be used later to compute word vectors or to restart the optimization.

Obtaining word vectors for out-of-vocabulary words

The previously trained model can be used to compute word vectors for out-of-vocabulary words. Provided you have a text file queries.txt containing words for which you want to compute vectors, use the following command:

$ ./hornvecs print-word-vectors model.bin < queries.txt

This will output word vectors to the standard output, one vector per line. This can also be used with pipes:

$ cat queries.txt | ./hornvecs print-word-vectors model.bin

See the provided scripts for an example. For instance, running:

$ ./word-vector-example.sh

will compile the code, download data, compute word vectors and evaluate them on the rare words similarity dataset RW [Thang et al. 2013].

Text classification

This library can also be used to train supervised text classifiers, for instance for sentiment analysis. In order to train a text classifier using the method described in 2, use:

$ ./hornvecs supervised -input train.txt -output model

where train.txt is a text file containing a training sentence per line along with the labels. By default, we assume that labels are words that are prefixed by the string __label__. This will output two files: model.bin and model.vec. Once the model was trained, you can evaluate it by computing the precision and recall at k (P@k and R@k) on a test set using:

$ ./hornvecs test model.bin test.txt k

The argument k is optional, and is equal to 1 by default.

In order to obtain the k most likely labels for a piece of text, use:

$ ./hornvecs predict model.bin test.txt k

or use predict-prob to also get the probability for each label

$ ./hornvecs predict-prob model.bin test.txt k

where test.txt contains a piece of text to classify per line. Doing so will print to the standard output the k most likely labels for each line. The argument k is optional, and equal to 1 by default. See classification-example.sh for an example use case. In order to reproduce results from the paper 2, run classification-results.sh, this will download all the datasets and reproduce the results from Table 1.

If you want to compute vector representations of sentences or paragraphs, please use:

$ ./hornvecs print-sentence-vectors model.bin < text.txt

This assumes that the text.txt file contains the paragraphs that you want to get vectors for. The program will output one vector representation per line in the file.

You can also quantize a supervised model to reduce its memory usage with the following command:

$ ./hornvecs quantize -output model

This will create a .ftz file with a smaller memory footprint. All the standard functionality, like test or predict work the same way on the quantized models:

$ ./hornvecs test model.ftz test.txt

The quantization procedure follows the steps described in 3. You can run the script quantization-example.sh for an example.

Full documentation

Invoke a command without arguments to list available arguments and their default values:

$ ./hornvecs supervised
Empty input or output path.

The following arguments are mandatory:
  -input              training file path
  -output             output file path

The following arguments are optional:
  -verbose            verbosity level [2]

The following arguments for the dictionary are optional:
  -minCount           minimal number of word occurences [1]
  -minCountLabel      minimal number of label occurences [0]
  -wordNgrams         max length of word ngram [1]
  -bucket             number of buckets [2000000]
  -minn               min length of char ngram [0]
  -maxn               max length of char ngram [0]
  -t                  sampling threshold [0.0001]
  -label              labels prefix [__label__]

The following arguments for training are optional:
  -lr                 learning rate [0.1]
  -lrUpdateRate       change the rate of updates for the learning rate [100]
  -dim                size of word vectors [100]
  -ws                 size of the context window [5]
  -epoch              number of epochs [5]
  -neg                number of negatives sampled [5]
  -loss               loss function {ns, hs, softmax} [softmax]
  -thread             number of threads [12]
  -pretrainedVectors  pretrained word vectors for supervised learning []
  -saveOutput         whether output params should be saved [0]

The following arguments for quantization are optional:
  -cutoff             number of words and ngrams to retain [0]
  -retrain            finetune embeddings if a cutoff is applied [0]
  -qnorm              quantizing the norm separately [0]
  -qout               quantizing the classifier [0]
  -dsub               size of each sub-vector [2]

Defaults may vary by mode. (Word-representation modes skipgram and cbow use a default -minCount of 5.)

References

If you use HornVecs in your work, please cite the fastText papers listed in the original repository and in addition cite this repository.

License

hornVecs adhers to the original fastText BSD-license. Please refer to the fastText repo.