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Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

Sandeep Subramanian, Adam Trischler, Yoshua Bengio & Christopher Pal

ICLR 2018


GenSen is a technique to learn general purpose, fixed-length representations of sentences via multi-task training. These representations are useful for transfer and low-resource learning. For details please refer to our ICLR paper.


We provide a PyTorch implementation of our paper along with pre-trained models as well as code to evaluate these models on a variety of transfer learning benchmarks.


  • Python 2.7 (Python 3 compatibility coming soon)
  • PyTorch 0.2 or 0.3
  • nltk
  • h5py
  • numpy
  • scikit-learn


Setting up Models & pre-trained word vecotrs

You download our pre-trained models and set up pre-trained word vectors for vocabulary expansion by

cd data/models
cd ../embedding
Using a pre-trained model to extract sentence representations.

You can use our pre-trained models to extract the last hidden state or all hidden states of our multi-task GRU. Additionally, you can concatenate the output of multiple models to replicate the numbers in our paper.

from gensen import GenSen, GenSenSingle

gensen_1 = GenSenSingle(
reps_h, reps_h_t = gensen_1.get_representation(
    sentences, pool='last', return_numpy=True, tokenize=True
print reps_h.shape, reps_h_t.shape
  • The input to get_representation is sentences, which should be a list of strings. If your strings are not pre-tokenized, then set tokenize=True to use the NLTK tokenizer before computing representations.
  • reps_h (batch_size x seq_len x 2048) contains the hidden states for all words in all sentences (padded to the max length of sentences)
  • reps_h_t (batch_size x 2048) contains only the last hidden state for all sentences in the minibatch

GenSenSingle will return the output of a single model nli_large_bothskip (+STN +Fr +De +NLI +L +STP). You can concatenate the output of multiple models by creating a GenSen instance with multiple GenSenSingle instances, as follows:

gensen_2 = GenSenSingle(
gensen = GenSen(gensen_1, gensen_2)
reps_h, reps_h_t = gensen.get_representation(
    sentences, pool='last', return_numpy=True, tokenize=True
  1. reps_h (batch_size x seq_len x 4096) contains the hidden states for all words in all sentences (padded to the max length of sentences)
  2. reps_h_t (batch_size x 4096) contains only the last hidden state for all sentences in the minibatch

The model will produce a fixed-length vector for each sentence as well as the hidden states corresponding to each word in every sentence (padded to max sentence length). You can also return a numpy array instead of a torch.FloatTensor by setting return_numpy=True.

Vocabulary Expansion

If you have a specific domain for which you want to compute representations, you can call vocab_expansion on instances of the GenSenSingle or GenSen class simply by gensen.vocab_expansion(vocab) where vocab is a list of unique words in the new domain. This will learn a linear mapping from the provided pretrained embeddings (which have a significantly larger vocabulary) provided to the space of gensen's word vectors. For an example of how this is used in an actual setting, please refer to

Training a model from scratch

To train a model from scratch, simply run with an appropriate JSON config file. An example config is provided in example_config.json. To continue training, just relaunch the same scripy with load_dir=auto in the config file.

To download some of the data required to train a GenSen model, run:


Note that this script can take a while to complete since it downloads, tokenizes and lowercases a fairly large En-Fr corpus. If you already have these parallel corpora processed, you can replace the paths to these files in the provided example_config.json

Some of the data used in our work is no longer publicly available (BookCorpus - see or has an LDC license associated (Penn Treebank). As a result, the example_config.json script will only train on Multilingual NMT and NLI, since they are publicly available. To use models trained on all tasks, please use our available pre-trained models.

Additional Sequence-to-Sequence transduction tasks can be added trivally to the multi-task framework by editing the json config file with more tasks.

python --config example_config.json

To use the default settings in example_config.json you will need a GPU with atleast 16GB of memory (such as a P100), to train on smaller GPUs, you may need to reduce the batch size.

Note that if "load_dir" is set to auto, the script will resume from the last saved model in "save_dir".

Creating a GenSen model from a trained multi-task model

Once you have a trained model, we can throw away all of the decoders and just retain the encoder used to compute sentence representations.

You can do this by running

python -t <path_to_trained_model> -s <path_to_save_encoder> -n <name_of_encoder>

Once you have done this, you can load this model just like any of the pre-trained models by specifying the model_folder as path_to_save_encoder and filename_prefix as name_of_encoder in the above command.

your_gensen = GenSenSingle(

Transfer Learning Evaluations

We used the SentEval toolkit to run most of our transfer learning experiments. To replicate these numbers, clone their repository and follow setup instructions. Once complete, copy and into their examples folder and run the following commands to reproduce different rows in Table 2 of our paper. Note: Please set the path to the pretrained glove embeddings (glove.840B.300d.h5) and model folder as appropriate.

(+STN +Fr +De +NLI +L +STP)      python --prefix_1 nli_large --prefix_2 nli_large_bothskip
(+STN +Fr +De +NLI +2L +STP)     python --prefix_1 nli_large_bothskip --prefix_2 nli_large_bothskip_2layer
(+STN +Fr +De +NLI +L +STP +Par) python --prefix_1 nli_large_bothskip_parse --prefix_2 nli_large_bothskip


title={Learning general purpose distributed sentence representations via large scale multi-task learning},
author={Subramanian, Sandeep and Trischler, Adam and Bengio, Yoshua and Pal, Christopher J},
journal={arXiv preprint arXiv:1804.00079},


Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning







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