gokasiko

gokasiko's Stars

• sublimelsp/LSP

  Client implementation of the Language Server Protocol for Sublime Text

  Language:Python1.6k179

• huaxiuyao/HSML

  HSML for ICML 2019

  Language:Python5112

• jquery/esprima

  ECMAScript parsing infrastructure for multipurpose analysis

  Language:TypeScript7k787

• jiangnanboy/albert_lstm_crf_ner

  albert + lstm + crf实体识别，pytorch实现。识别的主要实体是人名、地名、机构名和时间。albert + lstm + crf (named entity recognition)

  Language:Python13532

• buppt/ChineseNER

  中文命名实体识别，实体抽取，tensorflow，pytorch，BiLSTM+CRF

  Language:Python1.4k394

• tristandeleu/pytorch-maml-rl

  Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

  Language:Python825158

• abisee/pointer-generator

  Code for the ACL 2017 paper "Get To The Point: Summarization with Pointer-Generator Networks"

  Language:Python2.2k812

• pcyin/tranX

  A general-purpose neural semantic parser for mapping natural language queries into machine executable code

  Language:Python460111

• thu-coai/CommonsenseStoryGen

  Implementation for paper "A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation"

  Language:Python10314

• macanv/BERT-BiLSTM-CRF-NER

  Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

  Language:Python4.7k1.3k

• shmsw25/qa-hard-em

  An original implementation of EMNLP 2019, "A Discrete Hard EM Approach for Weakly Supervised Question Answering"

  Language:Python13718

• cmasch/word-embeddings-from-scratch

  Creating word embeddings from scratch and visualize them on TensorBoard. Using trained embeddings in Keras.

  Language:Jupyter Notebook2717

• jacobeisenstein/gt-nlp-class

  Course materials for Georgia Tech CS 4650 and 7650, "Natural Language"

  Language:TeX4.9k1.1k

• liuyubobobo/Play-Leetcode

  My Solutions to Leetcode problems. All solutions support C++ language, some support Java and Python. Multiple solutions will be given by most problems. Enjoy:) 我的Leetcode解答。所有的问题都支持C++语言，一部分问题支持Java语言。近乎所有问题都会提供多个算法解决。大家加油！：）

  Language:C++2.7k718

• facebookresearch/higher

  higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

  Language:Python1.6k123

• bentrevett/pytorch-seq2seq

  Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

  Language:Jupyter Notebook5.4k1.3k

• HoyTta0/KnowledgeDistillation

  Knowledge distillation in text classification with pytorch. 知识蒸馏，中文文本分类，教师模型BERT、XLNET，学生模型biLSTM。

  Language:Python21354

• songyingxin/NLPer-Interview

  该仓库主要记录 NLP 算法工程师相关的面试题

  2.6k507

• deepset-ai/haystack

  :mag: AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

  Language:Python17.4k1.9k

• alexzuza/angular-plugin-architecture

  Example of building Angular plugin with AOT and SSR

  Language:TypeScript21382

• microsoft/vscode

  Visual Studio Code

  Language:TypeScript164k29.1k

• joni2back/angular-filemanager

  JavaScript File Manager Material Design Folder Explorer Navigator Browser Manager in AngularJS with CSS3 Responsive (with FTP in PHP / Java / Node)

  Language:JavaScript1.8k575

• iwnow/angular-plugin-example

  Angular plugin with AOT and separate build

  Language:TypeScript287

• wenhuchen/HybridQA

  Dataset and code for EMNLP2020 paper "HybridQA: A Dataset of Multi-Hop Question Answeringover Tabular and Textual Data"

  Language:Python22022

• huangxiangzhou/NLPCC2016KBQA

  KBQA based on the NLPCC2016 dataset, including reimplementation of NLPCC2016 best team`s QA.

  Language:Python319105

• wangbq18/Joint-BERT-KBQA

  端到端的基于知识图谱的问答系统，分为实体识别和关系分类两部，在BERT基础上做多任务联合训练。

  3016

• chizhu/KGQA_HLM

  基于知识图谱的《红楼梦》人物关系可视化及问答系统

  Language:HTML1.1k302

• MenglinLu/Chinese-clinical-NER

  CCKS2019中文命名实体识别任务。从医疗文本中识别疾病和诊断、解剖部位、影像检查、实验室检验、手术和药物6种命名实体。现已实现基于jieba和AC自动机的baseline构建、基于BiLSTM和CRF的序列标住模型构建。bert的部分代码主要源于https://github.com/charles9n/bert-sklearn.git 感谢作者。 模型最终测试集得分0.81，还有较大改进空间。可以当做一个baseline。

  Language:Python34576

• duterscmy/ccks2019-ckbqa-4th-codes

  中文知识库问答代码，CCKS2019 CKBQA评测第四名解决方案

  Language:Python47891

• oguzhaninan/Buka

  Buka is a modern software that helps you manage your ebook at ease.

  Language:JavaScript1.1k119