NOTE: lamtram is no longer under active development, as it has been succeeded by xnmt. Hence, we might not be answering questions or responding to issues here.
In addition to the usage info below, you can also take a look at the nmt-tips tutorial on building strong neural machine translation systems.
(To run Lamtram easily using Docker, see docker/README.md. To install manually, read on.)
First, in terms of standard libraries, you must have autotools, libtool, and Boost. If you are on Ubuntu/Debian linux, you can install them below:
$ sudo apt-get install autotools-dev autoconf libtool libboost-all-dev
You must install Eigen and DyNet separately. Follow the directions on the
dynet page, which also explain about installing Eigen.
Note that in order to run programs like lamtram that are linked to DyNet, you will have
to set the LD_LIBRARY_PATH variable to include the DyNet library. For example, if
$DYNET_DIR is the top directory for DyNet, then you can run the following command,
or add it to you .bashrc or .zshrc file:
$ export LD_LIBRARY_PATH="$DYNET_DIR/build/dynet:$LD_LIBRARY_PATH"
Once these two packages are installed and DyNet has been added to the LD_LIBRARY_PATH,
run the following commands, specifying the correct paths for dynet and Eigen.
$ autoreconf -i
$ ./configure --with-dynet=/path/to/dynet --with-eigen=/path/to/eigen
$ make
If you want to use lamtram on a GPU and have CUDA installed, first compile dynet with
CUDA enabled, then add the option --with-cuda=/path/to/cuda to the ./configure
command.
Lamtram is licensed under the LGPL 2.1 and may be used freely according to this, or any later versions of the license. If you want to use lamtram in your research and we'd appreciate if you use the following reference:
@misc{neubig15lamtram,
author = {Graham Neubig},
title = {lamtram: A Toolkit for Language and Translation Modeling using Neural Networks},
howpublished = {http://www.github.com/neubig/lamtram},
year = {2015}
}
To train a language model, you should prepare a training data set train.txt and
a development data set dev.txt. Before running training, you should decide a
vocabulary that you want to use, and replace all words outside of the vocabulary
with the symbol ''. The easiest way to do this is with the script/unk-single.pl
script.
$ script/unk-single.pl < train.txt > train.unk
Then, we can perform training with lamtram-train. Here is a typical way to run it
with options:
$ src/lamtram/lamtram-train \
--dynet_mem 1024 \ # Allocate 1024 megabytes of memory for training
--model_type nlm \ # Train a neural language model
--layer_size 512 \ # Use a layer size of 512 for hidden layers, embeddings, etc.
--trainer adam \ # Use adam for training models
--learning_rate 0.001 \ # Set learning rate to 0.001
--seed 0 \ # A random seed, or 0 for a different seed every run
--train_trg train.unk \ # Specify the training file
--dev_trg dev.txt \ # Specify the development file
--model_out langmodel.out
Training will take a long time (at least until the "speed improvements" below are finished), so try it out on a small data set first. As soon as one iteration finishes, the model will be written out, so you can use the model right away.
You can measure perplexity on a separate test set test.txt
$ src/lamtram/lamtram --dynet_mem 1024 --operation ppl --models_in nlm=langmodel.out < test.txt
Once you have a model, you can try randomly generating sentences from the model:
$ src/lamtram/lamtram --dynet_mem 1024 --sents 100 --operation gen --models_in nlm=langmodel.out
(Note: this may not work right now as of 2015-4-11. It will just generate the single most likely sentence over and over again. This will be fixed soon.)
Training a translation model is similar to language models. Prepare a parallel corpus consisting of train-src.txt and train-trg.txt (in the source and target languages), and dev-src.txt and dev-trg.txt. First make '' symbols like before.
$ script/unk-single.pl < train-src.txt > train-src.unk
$ script/unk-single.pl < train-trg.txt > train-trg.unk
Then, we can perform training with lamtram-train. Here is a typical way to run it to train
an attentional model similar to Bahdanau et al. (ICLR2015). You can also train an LSTM encoder-decoder
model as in Sutskever et. al (NIPS2014) by just changing "encatt" into "encdec."
$ src/lamtram/lamtram-train \
--dynet_mem 1024 \ # Allocate 1024 megabytes of memory for training
--model_type encatt \ # Create an atttentional model
--layer_size 512 \ # Use a layer size of 512 for hidden layers, embeddings, etc.
--trainer adam \ # Use adam for training models
--learning_rate 0.001 \ # Set learning rate to 0.001
--seed 0 \ # A random seed, or 0 for a different seed every run
--train_src train-src.unk \ # Specify the training source file
--train_trg train-trg.unk \ # Specify the training target file
--dev_src dev-src.txt \ # Specify the development source file
--dev_trg dev-trg.txt \ # Specify the development target file
--model_out transmodel.out
Again, as soon as one iteration finishes, the model will be written out.
You can measure perplexity on a separate test set test-src.txt and test-trg.txt
$ src/lamtram/lamtram \
--dynet_mem 1024 \
--operation ppl \
--src_in test-src.txt \
--models_in encdec=transmodel.out \
< test-trg.txt
If you used the same data is before, the perplexity of the translation model should be significantly lower than that of the language model, as the translation model has the extra information from the input sentence.
You can also generate the most likely translation for the input sentence. Here is a typical use case.
$ src/lamtram/lamtram \
--dynet_mem 1024 \
--operation ppl \
--operation gen \
--src_in test-src.txt \
--models_in encdec=transmodel.out \
--beam 5 \ # Perform beam search with a beam of 5
--word_pen 0.0 \ # The word penalty can be used to increase or decrease
# the average length of the sentence (positive for more words)
> results.txt
You can also use model ensembles. Model ensembles allow you to combine two different models with different initializations or structures. Using model ensembles is as simple as listing multiple models separated by a pipe.
--models_in "encdec=transmodel.out|nlm=langmodel.out"
Note however that the models must have the same vocabulary (i.e. be trained on the same data). Ensembles will work for both generation and perplexity measurement.
Training a text classifier is very similar to translation models. The only difference is that you want to create a file of the text you want to classify train-src.txt, and a correct label file with one label per line, train-lbl.txt. This is not absolutely necessary, but you can convert rare words in the source into unknown symbols like before:
$ script/unk-single.pl < train-src.txt > train-src.unk
Then, we can perform training with lamtram-train. Here is a typical way to run it to train
an LSTM model.
$ src/lamtram/lamtram-train \
--dynet_mem 1024 \ # Allocate 1024 megabytes of memory for training
--model_type enccls \ # Create an encoder-classifier model
--layer_size 512 \ # Use a layer size of 512 for hidden layers, embeddings, etc.
--trainer adam \ # Use adam for training models
--learning_rate 0.001 \ # Set learning rate to 0.001
--seed 0 \ # A random seed, or 0 for a different seed every run
--train_src train-src.unk \ # Specify the training source file
--train_trg train-lbl.txt \ # Specify the training label file
--dev_src dev-src.txt \ # Specify the development source file
--dev_trg dev-lbl.txt \ # Specify the development label file
--model_out clsmodel.out
Again, as soon as one iteration finishes, the model will be written out.
You can measure perplexity and classification accuracy on a separate test set test-src.txt
and test-lbl.txt
$ src/lamtram/lamtram \
--dynet_mem 1024 \
--operation clseval \
--src_in test-src.txt \
--models_in enccls=clsmodel.out \
< test-lbl.txt
You can also generate the most likely label for the input sentence. Here is a typical use case.
$ src/lamtram/lamtram \
--dynet_mem 1024 \
--operation cls \
--models_in enccls=clsmodel.out \
> results.txt
You can also use model ensembles as with the translation models.
--models_in "enccls=clsmodel1.out|enccls=clsmodel2.out"
Note however that the models must have the same vocabulary (i.e. be trained on the same data). Ensembles will work for both perplexity measurement and classification.
- Noise-contrastive estimation
- More activation functions (softplus, maxout)
- Add other encoders