https://github.com/Linear95/BinarySentEmb

Code for ACL 2019 oral paper - Learning Compressed Sentence Representations for On-Device Text Processing.
https://github.com/Linear95/BinarySentEmb

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Code for ACL 2019 oral paper - Learning Compressed Sentence Representations for On-Device Text Processing.

• Host: GitHub
• URL: https://github.com/Linear95/BinarySentEmb
• Owner: Linear95
• Created: 2019-06-18T17:31:29.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-09-01T21:11:07.000Z (about 4 years ago)
• Last Synced: 2024-08-02T13:24:22.952Z (3 months ago)
• Language: Python
• Homepage:
• Size: 14.6 KB
• Stars: 44
• Watchers: 6
• Forks: 7
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# BinarySentEmb

Code for the ACL 2019 paper: Learning Compressed Sentence Representations for On-Device Text Processing.

This repository contains source code necessary to reproduce the results presented in the following paper:

* [*Learning Compressed Sentence Representations for On-Device Text Processing*](https://arxiv.org/pdf/1906.08340.pdf) (ACL 2019)

This project is maintained by [Pengyu Cheng](https://linear95.github.io/). Feel free to contact [email protected] for any relevant issues.

## Dependencies

This code is written in python. The dependencies are:

* Python 3.6

* Pytorch>=0.4 (0.4.1 is recommended)

* NLTK>=3

## Download pretrained models

First, download [GloVe](https://nlp.stanford.edu/projects/glove/) pretrained word embeddings:

```bash

mkdir dataset/GloVe

curl -Lo dataset/GloVe/glove.840B.300d.zip http://nlp.stanford.edu/data/glove.840B.300d.zip

unzip dataset/GloVe/glove.840B.300d.zip -d dataset/GloVe/

```

Then, follow the instruction of [InferSent](https://github.com/facebookresearch/InferSent) to download pretrain universal sentence encoder:

```bash

mkdir encoder

curl -Lo encoder/infersent1.pkl https://dl.fbaipublicfiles.com/infersent/infersent1.pkl

```

Futhermore, download our pretrained binary sentence encoder from [here](https://drive.google.com/open?id=12lzqtxQwktywXRc1HsQ36ptHGfGOTcIJ). Make sure the binary encoder is also in the `./encoder/` folder.

## Train a binary encoder

To train a binary sentence encoder, first download `data.py`, `mutils.py`, and `models.py` from [InferSent](https://github.com/facebookresearch/InferSent). 

Then, run the command:

```bash

python train.py

```

## Evaluate the binary encoder on transfer tasks

Following the instruction of [SentEval](https://github.com/facebookresearch/SentEval) to download the sentence embeddings evaluation toolkit and datasets.

Download the original InferSent encoder model from [here](https://github.com/facebookresearch/InferSent). 

To reproduce results of our pretrained binary sentence encoder, run the command:

```bash

python evaluate.py

```

## Citation 

Please cite our ACL 2019 paper if you found the code useful.

```latex

@article{shen2019learning,

  title={Learning Compressed Sentence Representations for On-Device Text Processing},

  author={Shen, Dinghan and Cheng, Pengyu and Sundararaman, Dhanasekar and Zhang, Xinyuan and Yang, Qian and Tang, Meng and Celikyilmaz, Asli and Carin, Lawrence},

  journal={arXiv preprint arXiv:1906.08340},

  year={2019}

}

```