Thank you for your interest on our paper.
I'm receieving mail basically everyday and happy to know many of you implemented the model correctly.
I'm glad to debug your code or have discussion with you.
Please do not hesitate to mail me for help.
mail_address = "raph_ael@ua_ca.com".replace("_", "")
numpy and tensorflow (I also have the pytorch implementation, which will be uploaded)
- Download the project and prepare the data
> git clone https://github.com/zomux/neuralcompressor
> cd neuralcompressor
> bash scripts/download_glove_data.sh
- Convert the Glove embeddings to numpy format
> python scripts/convert_glove2numpy.py data/glove.6B.300d.txt
- Train the embedding quantization model
> python bin/quantize_embed.py -M 32 -K 16 --train
...
[epoch198] train_loss=12.82 train_maxp=0.98 valid_loss=12.50 valid_maxp=0.98 bps=618 *
[epoch199] train_loss=12.80 train_maxp=0.98 valid_loss=12.53 valid_maxp=0.98 bps=605
Training Done
- Evaluate the averaged euclidean distance
> python bin/quantize_embed.py -M 32 -K 16 --evaluate
Mean euclidean distance: 4.889592628145218
- Export the word codes and the codebook matrix
> python bin/quantize_embed.py -M 32 -K 16 --export
It will generate two files:
- data/mymodel.codes
- data/mymodel.codebook.npy
- Check the codes
> paste data/glove.6B.300d.word data/mymodel.codes | head -n 100
...
only 15 14 7 10 1 14 14 3 0 9 1 9 3 3 0 0 12 1 3 12 15 3 11 12 12 6 1 5 13 6 2 6
state 7 13 7 3 8 14 10 6 6 4 12 2 9 3 9 0 1 1 3 9 11 10 0 14 14 4 15 5 0 6 2 1
million 5 7 3 15 1 14 4 0 6 11 1 4 8 3 1 0 0 1 3 14 8 6 6 5 2 1 2 12 13 6 6 15
could 3 14 7 0 2 14 5 3 0 9 1 0 2 3 9 0 3 1 3 11 5 15 1 12 12 6 1 6 2 6 2 10
...
from nncompress import EmbeddingCompressor # Load my embedding matrix matrix = np.load("data/glove.6B.300d.npy") # Initialize the compressor compressor = EmbeddingCompressor(32, 16, "data/mymodel") # Train the quantization model compressor.train(matrix) # Evaluate distance = compressor.evaluate(matrix) print("Mean euclidean distance:", distance) # Export the codes and codebook compressor.export(matrix, "data/mymodel")
@inproceedings{shu2018compressing,
title={Compressing Word Embeddings via Deep Compositional Code Learning},
author={Raphael Shu and Hideki Nakayama},
booktitle={International Conference on Learning Representations (ICLR)},
year={2018},
url={https://openreview.net/forum?id=BJRZzFlRb},
}
Arxiv version: https://arxiv.org/abs/1711.01068