mirror of
https://github.com/wassname/Castor.git
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Victor Yang
57f53a81b5
* runnable * add util file * update readme * update final layer and add model name * update argument * update readme, delete useless args * fix comments * fix more comments
69 lines
2.7 KiB
Markdown
69 lines
2.7 KiB
Markdown
# SSE
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This is a PyTorch reimplementation of the following paper:
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```
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@InProceedings{nie-bansal:2017:RepEval,
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author = {Nie, Yixin and Bansal, Mohit},
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title = {Shortcut-Stacked Sentence Encoders for Multi-Domain Inference},
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booktitle = {Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP},
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year = {2017}
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}
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```
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Please ensure you have followed instructions in the main [README](../README.md) doc before running any further commands in this doc.
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The commands in this doc assume you are under the root directory of the Castor repo.
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## SICK Dataset
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To run SSE on the SICK dataset, use the following command. `--dropout 0` is for mimicking the original paper, although adding dropout can improve results. If you have any problems running it check the Troubleshooting section below.
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```
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python -m sse sse.sick.model.castor --dataset sick --epochs 19 --dropout 0.5 --lr 0.0002 --regularization 1e-4
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```
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| Implementation and config | Pearson's r | Spearman's p | MSE |
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| -------------------------------- |:-------------:|:-------------:|:----------:|
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| PyTorch using above config | 0.8812158 | 0.8292130938075161 | 0.22950001060962677 |
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## TrecQA Dataset
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To run SSE on the TrecQA dataset, use the following command:
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```
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python -m sse sse.trecqa.model --dataset trecqa --epochs 5 --holistic-filters 200 --lr 0.00018 --regularization 0.0006405 --dropout 0
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```
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| Implementation and config | map | mrr |
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| -------------------------------- |:------:|:------:|
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| PyTorch using above config | | |
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This are the TrecQA raw dataset results. The paper results are reported in [Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks](https://dl.acm.org/citation.cfm?id=2983872).
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## WikiQA Dataset
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You also need `trec_eval` for this dataset, similar to TrecQA.
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Then, you can run:
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```
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python -m sse sse.wikiqa.model --epochs 10 --dataset wikiqa --epochs 5 --holistic-filters 100 --lr 0.00042 --regularization 0.0001683 --dropout 0
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```
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| Implementation and config | map | mrr |
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| -------------------------------- |:------:|:------:|
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| PyTorch using above config | | |
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To see all options available, use
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```
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python -m sse --help
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```
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## Optional Dependencies
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To optionally visualize the learning curve during training, we make use of https://github.com/lanpa/tensorboard-pytorch to connect to [TensorBoard](https://github.com/tensorflow/tensorboard). These projects require TensorFlow as a dependency, so you need to install TensorFlow before running the commands below. After these are installed, just add `--tensorboard` when running the training commands and open TensorBoard in the browser.
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```sh
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pip install tensorboardX
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pip install tensorflow-tensorboard
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```
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