https://github.com/vinhkhuc/MemN2N-babi-python
End-To-End Memory Networks for bAbI question-answering tasks
https://github.com/vinhkhuc/MemN2N-babi-python
machine-learning python question-answering
Last synced: 3 months ago
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End-To-End Memory Networks for bAbI question-answering tasks
- Host: GitHub
- URL: https://github.com/vinhkhuc/MemN2N-babi-python
- Owner: vinhkhuc
- License: other
- Created: 2016-01-10T06:56:50.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2019-04-13T01:54:18.000Z (over 6 years ago)
- Last Synced: 2024-08-08T23:24:56.656Z (about 1 year ago)
- Topics: machine-learning, python, question-answering
- Language: Python
- Homepage:
- Size: 15.6 MB
- Stars: 577
- Watchers: 43
- Forks: 146
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## End-To-End Memory Networks for Question Answering
This is an implementation of MemN2N model in Python for the [bAbI question-answering tasks](http://fb.ai/babi)
as shown in the Section 4 of the paper "[End-To-End Memory Networks](http://arxiv.org/abs/1503.08895)". It is based on
Facebook's [Matlab code](https://github.com/facebook/MemNN/tree/master/MemN2N-babi-matlab).
## Requirements
* Python 2.7
* Numpy, Flask (only for web-based demo) can be installed via pip:
```
$ sudo pip install -r requirements.txt
```
* [bAbI dataset](http://fb.ai/babi) should be downloaded to `data/tasks_1-20_v1-2`:
```
$ wget -qO- http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz | tar xvz -C data
```
## Usage
* To run on a single task, use `babi_runner.py` with `-t` followed by task's id. For example,
```
python babi_runner.py -t 1
```
The output will look like:
```
Using data from data/tasks_1-20_v1-2/en
Train and test for task 1 ...
1 | train error: 0.876116 | val error: 0.75
|=================================== | 71% 0.5s
```
* To run on 20 tasks:
```
python babi_runner.py -a
```
* To train using all training data from 20 tasks, use the joint mode:
```
python babi_runner.py -j
```
## Question Answering Demo
* In order to run the Web-based demo using the pretrained model `memn2n_model.pklz` in `trained_model/`, run:
```
python -m demo.qa
```
* Alternatively, you can try the console-based demo:
```
python -m demo.qa -console
```
* The pretrained model `memn2n_model.pklz` can be created by running:
```
python -m demo.qa -train
```
* To show all options, run `python -m demo.qa -h`
## Benchmarks
See the results [here](https://github.com/vinhkhuc/MemN2N-babi-python/tree/master/bechmarks).
### Author
Vinh Khuc
### Future Plans
* Port to TensorFlow/Keras
* Support Python 3
### References
* Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus,
"[End-To-End Memory Networks](http://arxiv.org/abs/1503.08895)",
*arXiv:1503.08895 [cs.NE]*.