David-Lee-1990's repositories

albert-chinese-ner

使用预训练语言模型ALBERT做中文NER

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AliceMind

ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab

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Chinese-Text-Classification-Pytorch

中文文本分类,TextCNN,TextRNN,FastText,TextRCNN,BiLSTM_Attention,DPCNN,Transformer,基于pytorch,开箱即用。

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ChineseNLPCorpus

中文自然语言处理数据集,平时做做实验的材料。欢迎补充提交合并。

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CogKR

Source code and dataset for paper "Cognitive Knowledge Graph Reasoning for One-shot Relational Learning"

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daniel_fintoc2019

Our participation to the "Financial Document Structure Extraction" task 2019

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data-centric-ai

Resources for Data Centric AI

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DRL-code-pytorch

Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

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dynamic-KG

Dynamic (Temporal) Knowledge Graph Completion (Reasoning)

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google-research

Google Research

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hgcn

Hyperbolic Graph Convolutional Networks in PyTorch.

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kglib

Grakn Knowledge Graph Library (ML R&D)

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KID

Knowledge Infused Decoding

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MetaICL

An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

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OpenKP

Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.

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References--Reinforcement-Learning

Papers being part of the state of the art on reinforcement learning

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sentence-transformers

Sentence Embeddings with BERT & XLNet

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sequence_tagging

Named Entity Recognition (LSTM + CRF) - Tensorflow

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stopwords

中文常用停用词表(哈工大停用词表、百度停用词表等)

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TabQGen

This repository hosts the code for the paper "Answer-Aware Question Generation from Tabular and Textual Data using T5"

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text_classification

all kinds of text classification models and more with deep learning

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Text_Classification-1

Text Classification Algorithms: A Survey

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