From 1d99b28bbb8f8a674465bd2f05031ceada298ee8 Mon Sep 17 00:00:00 2001 From: Tim Dettmers Date: Thu, 9 Nov 2017 12:22:28 -0800 Subject: [PATCH] Updated README.md. --- README.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 6c5799e..cd5f3b5 100644 --- a/README.md +++ b/README.md @@ -97,11 +97,14 @@ lr_decay = learning_rate_decay lr = learning_rate label_smoothing = label_smoothing_epsilon ``` +The parameters with the equal sign are equivalent and short-forms of each other. + To reproduce most of the results in the ConvE paper, you can use command below: ``` -CUDA_VISIBLE_DEVICES=0 python main.py model ConvE input_drop 0.2 hidden_drop 0.3 \ - feat_drop 0.2 lr 0.003 dataset DATASET_NAME +CUDA_VISIBLE_DEVICES=0 python main.py model ConvE input_drop 0.2 hidden_drop 0.3 \ + feat_drop 0.2 lr 0.003 lr_decay 0.995 \ + dataset DATASET_NAME ``` For the reverse model, you can run the provided file with the name of the dataset name and a threshold probability: @@ -113,9 +116,9 @@ python reverse_rule.py WN18RR 0.9 To run it on a new datasets, copy your dataset folder into the data folder and make sure your dataset split files have the name `train.txt`, `valid.txt`, and `test.txt` which contain tab separated triples of a knowledge graph. Then execute `python wrangle_KG.py FOLDER_NAME`, afterwards, you can use the folder name of your dataset in the dataset parameter. -### Adding new models +### Adding your own model -A barebone model `MyModel` can be found in the `model.py` file. You can easily extend this to create your own link-prediction model. +You can easily write your own knowledge graph model by extending the barebone model `MyModel` that can be found in the `model.py` file. ### Quirks