For non-R users we provide CLI (command line interface) to GloVe algorithm. See separate repository - text2vec-cli.

Here is copy of README from text2vec-cli:

text2vec-cli made for those people who don’t know R, but want to try alternative implementation of the GloVe algorithm. Compared to original implemetation text2vec usually ~2 times faster. It is also can fit word embeddings model with L1 regularization, which can be very useful for small datasets - algorithm can generalize much better than vanilla GloVe.

One possible limitation of text2vec is that it calculates co-occurence statistics in RAM. This can be a problem for very large corpuses with very large vocabularies. For example you can process english wikipedia dump with vocabulary consisting of 400000 unique terms and window=10 on machine with 32gb of RAM.



You need R 3.2+ be installed - check CRAN for instructions (should be very straightforward).

For main linux distribultions it should be even simpler:


# change following line accordingly to your system:
# here is string for ubuntu 14.04
echo 'deb trusty/' | sudo tee --append /etc/apt/sources.list
sudo apt-key adv --keyserver --recv-keys E084DAB9
sudo apt-key update
# install dependencies
sudo apt-get install -y libssl-dev libcurl4-openssl-dev git
# isntall R
sudo apt-get install -y r-base r-base-dev


Need something similar to instructions above. See how to install fresh R (3.2+) here.

sudo yum install openssl-devel libcurl-openssl-devel R


After R is installed clone this repo and make main scripts executable:

git clone
cd text2vec-cli
# make scripts executable
chmod +x install.R vocabulary.R cooccurence.R glove.R analogy.R

And install text2vec with dependencies:



Shut up and show me the code

./vocabulary.R vocab_file=vocab.rds
./cooccurence.R vocab_file=vocab.rds vocab_min_count=5 window_size=5 cooccurences_file=tcm.rds
./glove.R cooccurences_file=tcm.rds word_vectors_size=50 iter=10 x_max=10 convergence_tol=0.01


text2vec-cli is made for non-R users. We also assume that they are fluent in some another programming language and can preprocess input data using their favourite tools.

Also in contrast to text2vec R packge which can use all cores for vocabulary creation and calculation of the co-occurence statistics, text2vec-cli is single threaded (but GloVe training uses all available threads).

Input data

text2vec process data file by file. It read each file into RAM, process it and goes to next file. So if you text collection is in one large file (several gigs) we recommend to split it to chunks using standart unix split tool.

Example: your data is in single BIG_FILE.gz. In the following line we are:

  • unzipping it to stream and pass to pipe
  • split stream by lines with a constraint that each chunk should not be more than 100mb
  • pass each chunk to pipe, compress it back and save to disk
gunzip -c BIG_FILE.gz | split --line-bytes=100m --filter='gzip --fast > ./chunk_$FILE.gz'

For OS X install coreutils: brew install coreutils and use gsplit instead of split.

We assume:

  1. documents already preprocessed (lowercase, stemming, collocations, etc. - whatever user wan’t).
  2. each line in input files = sentence/document
  3. words/tokens are space separated
  4. Files ending in .gz, .bz2, .xz, or .zip will be automatically uncompressed. Files starting with http://, https://, ftp://, or ftps:// will be automatically downloaded. Remote gz files can also be automatically downloaded & decompressed.


To fit GloVe model user need to go through following steps.

create vocabulary

./vocabulary.R vocab_file=vocab.rds


  1. files - filenames of input files.multiple input files can be provided to files argument - use comma , to concatenate names: files=file1,file2.
  2. dir. Also can pass dir argument- all files from dir will be used.
  3. vocab_file - name of the output file

create co-occurence statistics

./cooccurence.R vocab_file=vocab.rds vocab_min_count=5 window_size=5 cooccurences_file=tcm.rds


  1. files - filenames of input files.multiple input files can be provided to files argument - use comma , to concatenate names: files=file1,file2.
  2. dir. Also can pass dir argument- all files from dir will be used.
  3. vocab_file - name of the vocabulary file
  4. vocab_min_count - prune vocanulary and use words thar appeared at least vocab_min_count times.
  5. window_size - how many neighbor words use for calculation of the co-occurence statistics
  6. cooccurences_file - name of the output file

train GloVe model

./glove.R cooccurences_file=tcm.rds word_vectors_size=50 iter=10 x_max=10 convergence_tol=0.01


  1. cooccurences_file - name of the file with co-occurence statistics
  2. word_vectors_size - dimension of word embeddings
  3. iter - maximum number of iterations of optimization algorithm
  4. x_max - maximum value of co-occurence value. Corresponds to X_MAX in original implementation.
  5. convergence_tol - 0.01 by default. Stop training if improvement between epochs less than convergence_tol.
  6. lambda - L1 regularization coefficient. Ususally values from 1e-4 to 1e-5 are useful.
  7. learning_rate - 0.2 by default. Initial rate for AdaGrad. Not recommended to change.
  8. clip_gradients - 10 by default. Clip gradients with this value for numerical stability. Not recommended to change.
  9. alpha - 0.75 by default.

check accuracy on word-analogy task

text2vec is created by Dmitry Selivanov and contributors. © 2016.
If you have found any BUGS please report them here.