kostyaev / sentence2vec

Deep sentence embedding using Sequence to Sequence learning

Deep sentence embedding using Sequence to Sequence learning

Installing

1. Install Torch.

2. Install the following additional Lua libs:

   luarocks install nn
   luarocks install rnn
   luarocks install penlight

   To train with CUDA install the latest CUDA drivers, toolkit and run:

   luarocks install cutorch
   luarocks install cunn

   To train with opencl install the lastest Opencl torch lib:

   luarocks install cltorch
   luarocks install clnn

3. Download the Cornell Movie-Dialogs Corpus and extract all the files into data/cornell_movie_dialogs.

Training

th train.lua [-h / options]

Use the --dataset NUMBER option to control the size of the dataset. Training on the full dataset takes about 5h for a single epoch.

The model will be saved to data/model.t7 after each epoch if it has improved (error decreased).

Getting a pretrained model

Download:

1. The pretraned model.t7
2. Vocabulary vocab.t7

Put them into the data directory.

Extracting embeddings from sentences

Run the following command

th -i extract_embeddings.lua --model_file data/model.t7 --input_file data/test_sentences.txt --output_file data/embeddings.t7 --cuda

To visualize 2D projections of the embeddings refer to: example.ipynb

Acknowledgments

This implementation utilizes code from Marc-André Cournoyer's repo

License

MIT License