mirror of https://github.com/wassname/Castor.git synced 2026-09-25 13:10:11 +08:00

T

Matt Crane a601722768 Option to disable the CuDNN backend (#52 )

Some of the kernels in this backend are known to be non-deterministic. It's also not clear to me whether it's enabled in a default install or not, enabled is set to True by default, but I think ultimately it's a runtime choice.

2017-09-22 11:19:30 +09:00

idf_baseline

Corpus idf (#23 )

2017-05-07 22:28:35 -04:00

kim_cnn

Add seed and thread arguments (#43 )

2017-09-01 09:30:01 -04:00

mp_cnn

MP-CNN Bugfixes and Improvements (#50 )

2017-09-21 15:53:54 -04:00

sm_cnn

Option to disable the CuDNN backend (#52 )

2017-09-22 11:19:30 +09:00

.gitignore

SM Model Jupyter Notebook Tutorial (#32 )

2017-07-04 18:28:17 -04:00

baseline_results.tsv

Ext feats bug fix (#19 )

2017-04-18 12:32:43 -04:00

README.md

SM Model Jupyter Notebook Tutorial (#32 )

2017-07-04 18:28:17 -04:00

setup.py

renaming sm_model; faster bridge (#18 ) (#22 )

2017-05-03 17:24:47 -04:00

README.md

Castor

PyTorch deep learning models.

SM model: Similarity between question and candidate answers.

Setting up PyTorch

You need Python 3.6 to use the models in this repository.

As per pytorch.org,

"Anaconda is our recommended package manager"

conda install pytorch torchvision -c soumith

Other pytorch installation modalities (e.g. via pip) can be seen at pytorch.org.

We also recommend gensim. We use some gensim modules to cache word embeddings.

conda install gensim

PyTorch has good support for GPU computations. CUDA installation guide for linux can be found here

NOTE: Install CUDA libraries before installing conda and pytorch.

data for models

Sourcing and pre-processing of input data for each model is described in respective model/README.md's

Baselines

IDF Baseline: IDF overlap between question and candidate answers.

Tutorials

SM Model tutorial: sm_cnn/tutorial.ipynb - notebook that walks through SM CNN model, good for beginnners.