likevivi Goto Github PK

awesome-medical-healthcare-dataset-for-llm

A curated list of popular Datasets, Models and Papers for LLMs in Medical/Healthcare

bert-bilstm-crf-ner

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

bert-ner-tf

Named Entity Recognition with BERT using TensorFlow 2.0

casrel

A Novel Cascade Binary Tagging Framework for Relational Triple Extraction. Accepted by ACL 2020.

deeplearning.ai

该存储库包含由deeplearning.ai提供的相关课程的个人的笔记和实现代码。

deeplearning_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

dlaicourse

Notebooks for learning deep learning

fe4ml-zh

:book: [译] 面向机器学习的特征工程

feature-engineering-handbook

A practical feature engineering handbook

gplinker

基于GlobalPointer的实体/关系/事件抽取

kashgari

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

kbqa-for-diagnosis

Knowledge Graph,Question Answering System，基于知识图谱和向量检索的医疗诊断问答系统

knowledgegraphcourse

东南大学《知识图谱》研究生课程

langchain-chatglm

langchain-ChatGLM, local knowledge based ChatGLM with langchain ｜ 基于本地知识库的 ChatGLM 问答

leetcode

LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解，记录自己的leetcode解题之路。)

lihang-code

《统计学习方法》的代码实现

machine-learning

ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

nlp-interview-notes

本项目是作者们根据个人面试和经验总结出的自然语言处理(NLP)面试准备的学习笔记与资料，该资料目前包含 自然语言处理各领域的 面试题积累。

openjere

EMNLP2020 findings paper: Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction

opennre

An Open-Source Package for Neural Relation Extraction (NRE)

pytorch_geometric

Graph Neural Network Library for PyTorch

seqeval

A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)

tensorflow-course

Simple and ready-to-use tutorials for TensorFlow

two-are-better-than-one

Code associated with the paper **Two are Better Than One: Joint Entity and Relation Extraction with Table-Sequence Encoders**, at EMNLP 2020

uie

Unified Structure Generation for Universal Information Extraction