butsugiri Goto Github PK

100_knock

言語処理100本ノック

analyze-my-tweet

Twitterから自分のポストを取得して，統計を出す奴

bokete

Generate "boke" from image with the power of neural network

butsugiri.github.io

My page

captioning_chainer

A fast implementation of Neural Image Caption by Chainer

chainer

A flexible framework of neural networks for deep learning

chainer-abcnn

Re-implementation of Attention-Based Convolutional Neural Network (ABCNN) by Chainer

chainer-dashboard

visualize the result of the (chainer's) experiment

chainer-esim

Re-implementation of ESIM by Chainer

chainer-qrnn

Re-implementation of Quasi-Recurrent Neural Networks by Chainer

chainer-rnn-ner

Named Entity Recognition with RNN, implemented by Chainer

cnn_sentence

Re-implementation of Kim Yoon's CNN model by Chainer

deeplearningexamples

Deep Learning Examples

dotfiles

download_arxiv

CLI app for downloading PDF from arXiv and ACL anthology

efficient_softmax

BlackOut and Adaptive Softmax for language models by Chainer

eos-decision

espnet

End-to-End Speech Processing Toolkit

fairseq-1

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

gec-pseudodata

Repository of "An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction" (EMNLP-IJCNLP 2019)

goreinu-slackbot

ゴレイヌとチャット

gpustat

📊 A simple command-line script (wrapper for nvidia-smi) for querying and monitoring GPU status

hojichar

A robust tool enabling customizable, efficient, and metric-logged text preprocessing.

homemade_bookcorpus

Make BookCorpus by myself: crawl, scrape and preprocess

lipy_notify

send message to your Line.app from Python

mlpnlp-nmt

This is a sample code of "LSTM encoder-decoder with attention mechanism" mainly for understanding a recently developed machine translation framework based on deep neural networks.

models

namas

Neural Attention Model for Abstractive Summarization

neuralcompressor

Embedding Quantization by Chainer

nlp-progress

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.