https://github.com/Ldpe2G/AC-BLSTM

MXNet implementation of AC-BLSTM
https://github.com/Ldpe2G/AC-BLSTM

Last synced: 2 months ago
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MXNet implementation of AC-BLSTM

• Host: GitHub
• URL: https://github.com/Ldpe2G/AC-BLSTM
• Owner: Ldpe2G
• License: mit
• Created: 2017-06-30T01:04:31.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-04-03T12:06:22.000Z (almost 6 years ago)
• Last Synced: 2024-11-08T11:56:48.661Z (2 months ago)
• Language: Scala
• Size: 519 KB
• Stars: 23
• Watchers: 3
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• Awesome-MXNet - AC-BLSTM

README

        
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# AC-BLSTM 

MXNet Scala module implementation of my work [AC-BLSTM[1]](https://arxiv.org/abs/1611.01884).

# Setup

## Environment

Tested on Ubuntu 14.04, using CUDA 8.0.61.

## Build Steps

### Build MXNet

make -j4 USE_MKLDNN=0 USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1

For more details how to build MXNet from source pls refer to: http://mxnet.io/get_started/ubuntu_setup.html.

#### Requirements to Build MXNet-Scala-Package

* Java 8

* [maven](https://maven.apache.org/download.cgi)

make scalapkg

For more details how to build MXNet-Scala-Package pls refer to: http://mxnet.io/get_started/ubuntu_setup.html#install-the-mxnet-package-for-scala.

### Build AC-BLSTM Project

#### Requirements

* [sbt 0.13](http://www.scala-sbt.org/)

under the AC-BLSTM folder:

```bash

mkdir lib

cp mxnet/scala-package/assembly/linux-x86_64-gpu/target/mxnet-full_2.11-linux-x86_64-gpu-0.1.2-SNAPSHOT.jar lib

```

Then run `sbt` and compile the project

## Run Experiments

### Download Word2Vec Model

You can download the pretrained Word2Vec Model in this url: https://code.google.com/archive/p/word2vec/, then put the 

 `GoogleNews-vectors-negative300.bin` file to the `datas` path.

 

### Run Experiments

#### AC-BLSTM on MR Dataset

```bash

cd run_scripts

bash train_ac_blstm.sh

```

#### G-AC-BLSTM on MR Dataset

```bash

cd run_scripts

bash train_g_ac_blstm.sh

```

Because I was doing the 10-fold cross-validation on MR dataset, so you can modify the `CROSS_VALIDATION_ID=` flag from 0 to 9 for the cross-validation expriements.

By the way, If you can successfully reproduce the result reported in the paper, congratulations :) . 

If not, God knows what happen :( . 

May the force be with you :) .....

## References

[1] Liang, Depeng, and Yongdong Zhang. "AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification." arXiv preprint arXiv:1611.01884 (2016).