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ai_project2

KTH AI-multi course project 2

awesome-courses

:books: List of awesome university courses for learning Computer Science!

bert_lstm_crf

code_snippets

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++

faster-rcnn.pytorch

A faster pytorch implementation of faster r-cnn

keyphrase-extraction

Attribute extraction from text

models

Models and examples built with TensorFlow

object-detection

Using deformable convolution

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.

pacmac_ucb

pke

Python Keyphrase Extraction module

recommendationdeepmatch

Some recommendation models and retrieval models

scene-text-recognition

Scene Text Recognition using Convolutional Recurrent Network

textclassification

Text classification using kernel method

xxx

yopo_reproduce

Reproduce works from "You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle"

yowo