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# MP-CNN PyTorch Implementation
# MP-CNN (and "Lite" Variants)
This is a PyTorch implementation of the following paper
This is a PyTorch reimplementation of the following paper:
* Hua He, Kevin Gimpel, and Jimmy Lin. [Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks](http://aclweb.org/anthology/D/D15/D15-1181.pdf). *Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015)*, pages 1576-1586.
+ Hua He, Kevin Gimpel, and Jimmy Lin. [Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks](http://aclweb.org/anthology/D/D15/D15-1181.pdf). *Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015)*, pages 1576-1586.
On top of which we performed additional ablation experiments and explored a number of variants, as described in:
+ Zhucheng Tu. [An Experimental Analysis of Multi-Perspective Convolutional Neural Networks](https://uwspace.uwaterloo.ca/handle/10012/13297). Master's Thesis, Computer Science, University of Waterloo, 2018.
Please ensure you have followed instructions in the main [README](../README.md) doc before running any further commands in this doc.
The commands in this doc assume you are under the root directory of the Castor repo.
## Pre-Trained Models
We have pre-trained models for SICK, TrecQA, and WikiQA in the [Castor-models](https://git.uwaterloo.ca/jimmylin/Castor-models) repository. They are trained using the commands in each of the dataset sections below and the evaluation metrics match the reported values in those sections in our environment.
We provide pre-trained models for SICK, TrecQA, and WikiQA in the [Castor-models](https://git.uwaterloo.ca/jimmylin/Castor-models) repository. They are trained using the commands in each of the dataset sections below; evaluation metrics match the reported values in those sections in our environment.
| Dataset | Model file | Command to run pre-trained model |
| --------- |:--------------------------:|:----------------------------------------------------------------------------------------------------------------------:|
| SICK | mp_cnn/mpcnn.sick.model | `python -m mp_cnn ../Castor-models/mp_cnn/mpcnn.sick.model --dataset sick --skip-training` |
| TrecQA | mp_cnn/mpcnn.trecqa.model | `python -m mp_cnn ../Castor-models/mp_cnn/mpcnn.trecqa.model --dataset trecqa --holistic-filters 200 --skip-training` |
| WikiQA | mp_cnn/mpcnn.wikiqa.model | `python -m mp_cnn ../Castor-models/mp_cnn/mpcnn.wikiqa.model --dataset wikiqa --holistic-filters 100 --skip-training` |
**SICK** ([`mp_cnn/mpcnn.sick.model`](https://git.uwaterloo.ca/jimmylin/Castor-models/tree/master/mp_cnn))
If you want to train them yourself, please read on.
```
$ python -m mp_cnn ../Castor-models/mp_cnn/mpcnn.sick.model --dataset sick \
--skip-training
```
**TrecQA** ([`mp_cnn/mpcnn.trecqa.model`](https://git.uwaterloo.ca/jimmylin/Castor-models/tree/master/mp_cnn))
```
$ python -m mp_cnn ../Castor-models/mp_cnn/mpcnn.trecqa.model --dataset trecqa \
--holistic-filters 200 --skip-training
```
**WikiQA** ([`mp_cnn/mpcnn.wikiqa.model`](https://git.uwaterloo.ca/jimmylin/Castor-models/tree/master/mp_cnn))
```
$ python -m mp_cnn ../Castor-models/mp_cnn/mpcnn.wikiqa.model --dataset wikiqa \
--holistic-filters 100 --skip-training
```
The commands above assume GPU; for running on the CPU, add `--device -1`.
If you want to train the models yourself, please read on.
## SICK Dataset