From 0afccb5b012041eba8cf415108f3c4d85b089c4a Mon Sep 17 00:00:00 2001 From: leo <1252298449@qq.com> Date: Wed, 2 Nov 2022 18:16:50 +0800 Subject: [PATCH] Add description of THU-OpenSK --- README.md | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 4cc5c72..aad440b 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,15 @@ # TensorFlow-TransX +This repository is a subproject of THU-OpenSK, and all subprojects of THU-OpenSK are as follows. + +- [OpenNE](https://www.github.com/thunlp/OpenNE) +- [OpenKE](https://www.github.com/thunlp/OpenKE) + - [KB2E](https://www.github.com/thunlp/KB2E) + - [TensorFlow-Transx](https://www.github.com/thunlp/TensorFlow-Transx) + - [Fast-TransX](https://www.github.com/thunlp/Fast-TransX) +- [OpenNRE](https://www.github.com/thunlp/OpenNRE) + - [JointNRE](https://www.github.com/thunlp/JointNRE) + The implementation of TransE [1], TransH [2], TransR [3], TransD [4] for knowledge representation learning (KRL). The overall framework is based on TensorFlow. We use C++ to implement some underlying operations such as data preprocessing and negative sampling. For each specific model, it is implemented by TensorFlow with Python interfaces so that there is a convenient platform to run models on GPUs. These codes will be gradually integrated into the new framework [[OpenKE]](https://github.com/thunlp/openke). @@ -35,7 +45,7 @@ To train models based on random initialization: 1. Change class Config in transX.py class Config(object): - + def __init__(self): ... lib.setInPath("your training data path...") @@ -50,7 +60,7 @@ To train models based on pretrained results: 1. Change class Config in transX.py class Config(object): - + def __init__(self): ... lib.setInPath("your training data path...") @@ -67,7 +77,7 @@ To test your models: 1. Change class Config in transX.py class Config(object): - + def __init__(self): ... test_lib.setInPath("your testing data path...")