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mind-and-hand's Projects

clue icon clue

中文语言理解测评基准 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard

ernie icon ernie

Official implementations for various pre-training models of ERNIE-family, covering topics of Language Understanding & Generation, Multimodal Understanding & Generation, and beyond.

hei_dong icon hei_dong

Toyhom的学习之路,Toyhom's way of learning

knowledgedistillation icon 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。

lightgbm icon lightgbm

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

paddlenlp icon paddlenlp

Easy-to-use and powerful NLP library with Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including Neural Search, Question Answering, Information Extraction and Sentiment Analysis end-to-end system.

pre-training-language-model icon pre-training-language-model

配套视频链接:https://www.bilibili.com/video/BV1At4y1W75x?spm_id_from=333.999.0.0;博客链接:https://www.cnblogs.com/nickchen121/p/15105048.html

publicopinion icon publicopinion

與情分析系统,包括爬虫、数据清洗、文本摘要、主题分类、情感倾向性识别以及分析结果数据可视化

repdistiller icon repdistiller

[ICLR 2020] Contrastive Representation Distillation (CRD), and benchmark of recent knowledge distillation methods

rl-book icon rl-book

Source codes for the book "Reinforcement Learning: Theory and Python Implementation"

senta icon senta

Baidu's open-source Sentiment Analysis System.

sentiment_cls icon sentiment_cls

一个基于bert系模型的情感分类器(金融类新闻)

text-classification-surveys icon text-classification-surveys

文本分类资源汇总,包括深度学习文本分类模型,如SpanBERT、ALBERT、RoBerta、Xlnet、MT-DNN、BERT、TextGCN、MGAN、TextCapsule、SGNN、SGM、LEAM、ULMFiT、DGCNN、ELMo、RAM、DeepMoji、IAN、DPCNN、TopicRNN、LSTMN 、Multi-Task、HAN、CharCNN、Tree-LSTM、DAN、TextRCNN、Paragraph-Vec、TextCNN、DCNN、RNTN、MV-RNN、RAE等,浅层学习模型,如LightGBM 、SVM、XGboost、Random Forest、C4.5、CART、KNN、NB、HMM等。介绍文本分类数据集,如MR、SST、MPQA、IMDB、Yelp、20NG、AG、R8、DBpedia、Ohsumed、SQuAD、SNLI、MNLI、MSRP、MRDA、RCV1、AAPD,评价指标,如accuracy、Precision、Recall、F1、EM、MRR、HL、Micro-F1、Macro-F1、P@K,和技术挑战,包括多标签文本分类。

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