DecstionBack's repositories
AAAI_2020_CommonsenseQA
Code for "Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering. (published at AAAI 2020)"
Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
attention-is-all-you-need-pytorch
A PyTorch implementation of the Transformer model in "Attention is All You Need".
coding-interview-university
A complete computer science study plan to become a software engineer.
commonsense-rc
Code for Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
CommonsenseERL_EMNLP_2019
Code for the EMNLP 2019 paper: Event Representation Learning Enhanced with External Commonsense Knowledge.
decstionback.github.io
BY Blog ->
DisExtract
The library that uses dependency parsing to preprocess text to train DisSent model
L2EWeb
A Python web server for L2E
LeetCodeAnimation
Demonstrate all the questions on LeetCode in the form of animation.(用动画的形式呈现解LeetCode题目的思路)
multi_relational_script_learning
This repository contains code, data, and pre-trained models for the paper "Multi-Relation Script Learning for Discourse Relations"
pytorch-openai-transformer-lm
A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI
pytorch-pretrained-BERT
A PyTorch implementation of Google AI's BERT model provided with Google's pre-trained models, examples and utilities.
SMRCToolkit
This toolkit was designed for the fast and efficient development of modern machine comprehension models, including both published models and original prototypes.
SPM_toolkit
Neural network toolkit for sentence pair modeling.
stanfordnlp
Official Stanford NLP Python Library for Many Human Languages
tensor2tensor
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
TransBERT_ijcai2019
Code used in our ijcai 2019 paper "Story Ending Prediction by Transferable BERT"