Jeff654's repositories
Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
PaperForONLG
Paper list for open-ended language generation
text_classification
all kinds of text classification models and more with deep learning
LLMSurvey
The official GitHub page for the survey paper "A Survey of Large Language Models".
awesome-knowledge-graph
整理知识图谱相关学习资料
bert
TensorFlow code and pre-trained models for BERT
bert4torch
An elegent pytorch implement of transformers
ChitChatAssistant
Rasa中文聊天机器人
conceptnet5
Code for building ConceptNet from raw data.
DeepSpeedExamples
Example models using DeepSpeed
dl4s
source code accompanying "Deep Learning for Search" book
dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
EasyTransfer
EasyTransfer is designed to make the development of transfer learning in NLP applications easier.
few_shot_learning
classical model code implementation of few-shot/one-shot lenaring, including siamese network, prototypical network, relation network, induction network
kaggle_public
阿水的开源分支
linformer-pytorch
My take on a practical implementation of Linformer for Pytorch. https://arxiv.org/pdf/2006.04768.pdf
LLMsPracticalGuide
A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)
mtgbmcode
mtgbmcode
neuralcoref
✨Fast Coreference Resolution in spaCy with Neural Networks
NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
NLP-with-Python
Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more
notebooks
Jupyter notebooks for the Natural Language Processing with Transformers book
OpenPipe
Turn expensive prompts into cheap fine-tuned models
PLMs-in-Practical-KBQA
The code of An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering
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。
search_with_lepton
Building a quick conversation-based search demo with Lepton AI.
sentence-transformers
Sentence Embeddings with BERT & XLNet
ToolBench
An open platform for training, serving, and evaluating large language model for tool learning.