Pinned Repositories
Agriculture_KnowledgeGraph
农业知识图谱(AgriKG)：农业领域的信息检索，命名实体识别，关系抽取，智能问答，辅助决策
Chinese
Tools and resources for Chinese texts preprocessing. Validated in two papers, one CCF C, EI indexing and one CCF B, SCI indexing.
elasticsearch-dsl-py
High level Python client for Elasticsearch
elasticsearch-py
Official Python low-level client for Elasticsearch.
hrq-vae
Hierarchical Sketch Induction for Paraphrase Generation (Hosking et al., ACL 2022)
KnowledgeGraph-QA-Service
基于知识图谱的问答
LSS
A semi-supevised model for document scaling
NER
基于tensorflow深度学习的中文的命名实体识别
nstools
Some meaningless nscripter tools.
Prediction-of-stock-market-deviation-using-ARIMA-algorithm
Stock market is an ideal way to invest hard earned money as it has the potential to provide great returns. But, even with the current technology at hand, it is a risky deed due to the inability to understand sudden market changes and interpret data appropriately. To ease the process of investment and to provide better awareness, we propose ‘Prediction of stock market deviation using ARIMA algorithm’: a real-time risk prediction software that considers market interests. It is based on a parametric time series analysis technique- ARIMA (Auto Regressive Integrated Moving Average) algorithm to interpret historic data. It also makes use of Sentiment analysis to convert market trends to valuable information. Since stock market is highly influenced by information release and public acceptance, the addition of Sentiment Analysis to ARIMA boosts system performance and provides a more accurate representation of market volatility. The software provides pictorial and graphical representations and can also be used to compare the growth of two companies for the required time period. The objective is to provide short term and long term prediction capabilities to prepare for future potential investments.
casually-PYlearner's Repositories
casually-PYlearner/ACL2022_KnowledgeNLP_Tutorial
Materials for ACL-2022 tutorial: Knowledge-Augmented Methods for Natural Language Processing
casually-PYlearner/AdaLoGN
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension (ACL 2022)
casually-PYlearner/awesome-pretrained-chinese-nlp-models
Awesome Pretrained Chinese NLP Models，高质量中文预训练模型集合
casually-PYlearner/causal-reasoning
Knowledge-Augmented Language Models for Cause-Effect Relation Classification https://arxiv.org/abs/2112.08615
casually-PYlearner/Chinese-Pretrain-MRC-Model
casually-PYlearner/ChineseMRC-Data
收集了目前为止中文领域的MRC抽取式数据集
casually-PYlearner/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。
casually-PYlearner/chineseocr
yolo3+ocr
casually-PYlearner/chineseocr_lite
超轻量级中文ocr，支持竖排文字识别, 支持ncnn、mnn、tnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M
casually-PYlearner/ContextualSP
Multiple paper open-source codes of the Microsoft Research Asia DKI group
casually-PYlearner/DeepSpeedExamples
Example models using DeepSpeed
casually-PYlearner/FiD
Fusion-in-Decoder
casually-PYlearner/GLM-130B
GLM-130B: An Open Bilingual Pre-Trained Model
casually-PYlearner/GTR
[SIGIR 2021] Retrieving Complex Tables with Multi-Granular Graph Representation Learning.
casually-PYlearner/IM-TQA
Dataset for ACL 2023 paper: "IM-TQA: A Chinese Table Question Answering Dataset with Implicit and Multi-type Table Structures". We proposed a new TQA problem which aims at real application scenarios, together with a supporting dataset and a baseline method.
casually-PYlearner/language_tool_python
a free python grammar checker 📝✅
casually-PYlearner/MRC_Competition_Dureader
机器阅读理解 冠军/亚军代码及中文预训练MRC模型
casually-PYlearner/nlp_chinese_corpus
大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP
casually-PYlearner/promptsource
Toolkit for creating, sharing and using natural language prompts.
casually-PYlearner/QA-Survey-CN
北京航空航天大学大数据高精尖中心自然语言处理研究团队开展了智能问答的研究与应用总结。包括基于知识图谱的问答（KBQA），基于文本的问答系统（TextQA），基于表格的问答系统（TableQA）、基于视觉的问答系统（VisualQA）和机器阅读理解（MRC）等，每类任务分别对学术界和工业界进行了相关总结。
casually-PYlearner/responsibleNLPresearch
templates and other documents regarding responsible NLP research
casually-PYlearner/RetroMAE
Codebase for RetroMAE and beyond.
casually-PYlearner/Semantic-Retrieval-Models
A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).
casually-PYlearner/StruBERT
StruBERT: Structure-aware BERT for Table Search and Matching
casually-PYlearner/SYLLOBASE
casually-PYlearner/TableQA
NL2SQL competition dataset
casually-PYlearner/tableQA-Chinese
Unsupervised tableQA and databaseQA on chinese finance question and tabular data
casually-PYlearner/Tabular-LLM
本项目旨在收集开源的表格智能任务数据集（比如表格问答、表格-文本生成等），将原始数据整理为指令微调格式的数据并微调LLM，进而增强LLM对于表格数据的理解，最终构建出专门面向表格智能任务的大型语言模型。
casually-PYlearner/tapas
End-to-end neural table-text understanding models.
casually-PYlearner/tevatron
Tevatron - A flexible toolkit for dense retrieval research and development.