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Yanan Lu's Projects

ranknet icon ranknet

My (slightly modified) Keras implementation of RankNet and PyTorch implementation of LambdaRank.

rappor icon rappor

RAPPOR: Privacy-Preserving Reporting Algorithms

recadam icon recadam

Code for the RecAdam paper: Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting.

reltextrank icon reltextrank

RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representation

replicating-deepmind icon replicating-deepmind

Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind

rgcn-with-bert icon rgcn-with-bert

Graph Convolutional Networks (GCN) with BERT for Coreference Resolution Task [Pytorch][DGL]

rnn-theano icon rnn-theano

使用Theano实现的一些RNN代码,包括最基本的RNN,LSTM,以及部分最新论文MLSTM等

rnn-tutorial-rnnlm icon rnn-tutorial-rnnlm

Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano

rnnlg icon rnnlg

RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.

roberta_zh icon roberta_zh

RoBERTa中文预训练模型: RoBERTa for Chinese

s-lstm icon s-lstm

Code for the paper: Sentence-State LSTM for Text Representation

scrnns icon scrnns

This is a self contained software accompanying the paper titled: Learning Longer Memory in Recurrent Neural Networks: http://arxiv.org/abs/1412.7753.

sentence-similarity icon sentence-similarity

问题句子相似度计算,即给定客服里用户描述的两句话,用算法来判断是否表示了相同的语义。

sentence2vec icon sentence2vec

Tools for mapping a sentence with arbitrary length to vector space

seq2seq icon seq2seq

Sequence to Sequence Learning with Keras

seq2seq-attn icon seq2seq-attn

Sequence-to-sequence model with LSTM encoder/decoders and attention

seqgan icon seqgan

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

skip-thoughts icon skip-thoughts

Sent2Vec encoder and training code from the paper "Skip-Thought Vectors"

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