Pinned Repositories
adversarial-multi-criteria-learning-for-CWS
The implementation of paper https://arxiv.org/abs/1704.07556, ACL 2017
basilisk
BASILISK (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge)
BREDS
Bootstrapping Relationship Extractors with Distributional Semantics
chainer
A flexible framework of neural networks for deep learning
end-to-end-negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
fairseq
Facebook AI Research Sequence-to-Sequence Toolkit
fastText
Library for fast text representation and classification.
generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
iclr2016
Python code for training all models in the ICLR paper, "Towards Universal Paraphrastic Sentence Embeddings". These models achieve strong performance on semantic similarity tasks without any training or tuning on the training data for those tasks. They also can produce features that are at least as discriminative as skip-thought vectors for semantic similarity tasks at a minimum. Moreover, this code can achieve state-of-the-art results on entailment and sentiment tasks.
InferSent
Sentence embeddings (InferSent) and training code for NLI.
koshinryuu's Repositories
koshinryuu/adversarial-multi-criteria-learning-for-CWS
The implementation of paper https://arxiv.org/abs/1704.07556, ACL 2017
koshinryuu/basilisk
BASILISK (Bootstrapping Approach to SemantIc Lexicon Induction using Semantic Knowledge)
koshinryuu/BREDS
Bootstrapping Relationship Extractors with Distributional Semantics
koshinryuu/chainer
A flexible framework of neural networks for deep learning
koshinryuu/end-to-end-negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
koshinryuu/fairseq
Facebook AI Research Sequence-to-Sequence Toolkit
koshinryuu/fastText
Library for fast text representation and classification.
koshinryuu/generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
koshinryuu/iclr2016
Python code for training all models in the ICLR paper, "Towards Universal Paraphrastic Sentence Embeddings". These models achieve strong performance on semantic similarity tasks without any training or tuning on the training data for those tasks. They also can produce features that are at least as discriminative as skip-thought vectors for semantic similarity tasks at a minimum. Moreover, this code can achieve state-of-the-art results on entailment and sentiment tasks.
koshinryuu/InferSent
Sentence embeddings (InferSent) and training code for NLI.
koshinryuu/keras-gcn
Keras implementation of Graph Convolutional Networks
koshinryuu/nanoGPT
The simplest, fastest repository for training/finetuning medium-sized GPTs.
koshinryuu/neural-dep-srl
A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling
koshinryuu/pydata-book
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
koshinryuu/pytorch-cn
Document for Pytorch project
koshinryuu/reinforcement-learning-an-introduction
Python code for Reinforcement Learning: An Introduction
koshinryuu/rfa-doc-mt
koshinryuu/sentencepiece
koshinryuu/Seq2seq-Chatbot-for-Keras
This repository contains a new generative model of chatbot based on seq2seq modeling.
koshinryuu/Snowball
Snowball: Extracting Relations from Large Plain-Text Collections
koshinryuu/vecmap
A framework to learn bilingual word embedding mappings
koshinryuu/ViLT
Code for the ICML 2021 (long talk) paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"