mirror of
https://github.com/wassname/Castor.git
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Michael Tu
43dd6fdb1f
Add code to using GPU for the SM Model (#25). To use the GPU if one is available, add the --cuda optional parameter when calling main.py.
81 lines
2.1 KiB
Markdown
81 lines
2.1 KiB
Markdown
## SM model
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#### References:
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1. Aliaksei _S_everyn and Alessandro _M_oschitti. 2015. Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15). ACM, New York, NY, USA, 373-382. DOI: http://dx.doi.org/10.1145/2766462.2767738
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### Requirements
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gensim==1.0.1
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nltk==3.2.2
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numpy==1.11.3
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pandas==0.19.2
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torch==0.1.11+b13b701
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Please install the requirements. See [Castor/README.md](../README.md) for pytorch installation details.
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### Training the model
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1. Setup repository layout.
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```
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mkdir castorini
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cd castorini
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git clone https://github.com/castorini/data.git
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git clone https://github.com/castorini/models.git
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git clone https://github.com/castorini/Castor.git
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```
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This should generate:
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```
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├── Castor
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│ ├── README.md
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│ ├── idf_baseline
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│ ├── kim_cnn
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│ ├── simple_qa_rnn
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│ └── sm_cnn/
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├── data
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│ ├── README.md
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│ ├── TrecQA/
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│ └── word2vec/
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└── models
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├── README.md
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└── sm_cnn/
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```
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2. Preprocess data
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```
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cd data/TrecQA
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python3 parse.py
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python3 overlap_features.py
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python3 build_vocab.py
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```
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3. Download word embeddings from [here](https://drive.google.com/folderview?id=0B-yipfgecoSBfkZlY2FFWEpDR3M4Qkw5U055MWJrenE5MTBFVXlpRnd0QjZaMDQxejh1cWs&usp=sharing) and save the ``aquaint+wiki.txt.gz.ndim=50.bin`` into ``data/word2vec/``.
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4. Train the model
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Make trec_eval
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```
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cd Castor/sm_cnn/
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cd trec_eval-8.0
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make clean && make
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cd ..
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```
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To train the S&M model on TrecQA
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```
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python main.py ../../models/sm_model/sm_model.TrecQA.TRAIN-ALL.2017-04-02.castor --paper-ext-feats
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```
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**To use the GPU, add `--cuda`.**
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The final model will be saved to ```../../models/sm_model/sm_model.TrecQA.TRAIN-ALL.2017-04-02.castor```
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_NOTE:_ On first run, the program will create a memory-mapped cache for word e mbeddings (943MB) in ``data/word2vec``.
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The cache allows for faster loading of data in future runs.
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Run ```python main.py -h``` for more default options.
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