The repository for the survey paper <<Survey on Large Language Models Factuality: Knowledge, Retrieval and Domain-Specificity>>

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

Fine-tuning ChatGLM-6B with PEFT | 基于 PEFT 的高效 ChatGLM 微调

Fast and memory-efficient exact attention

chatglm 6b finetuning and alpaca finetuning

Ongoing research training transformer models at scale

RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference,…

PandaOCR - 多功能OCR图文识别+翻译+朗读+弹窗+公式+表格+图床+搜图+二维码

Vector Quantization, in Pytorch

Computational Fact-Checking Algorithm on Knowledge Graphs

Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (ACL 2021)

Code and datasets for the Tsetlin Machine

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2020

A very simple framework for state-of-the-art Natural Language Processing (NLP)

Implementation of unified embedding model from Embedding-based Retrieval in Facebook Search.

Transformers for Natural Language Processing, published by Packt

Annotations of the interesting ML papers I read

A curated list of resources dedicated to Natural Language Generation (NLG)

《机器翻译：基础与模型》肖桐 朱靖波 著 - Machine Translation: Foundations and Models

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

Tensors and Dynamic neural networks in Python with strong GPU acceleration

🌍「算法面试+算法知识」针对小白的算法训练 | 还包括：1、阿里、字节、滴滴 百篇大厂面经汇总 2、千本开源电子书 3、百张思维导图 （右侧来个 star 吧 🌹，English version supported）

Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

该系列包括数组，链表，树，图，递归，DP，有序表等相关数据结构与算法的讲解及代码实现。

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。

端到端的长本文摘要模型（法研杯2020司法摘要赛道）

Code - Unsupervised Question Answering for Fact-Checking

Learning to Rank in TensorFlow

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06