fengbinzhu

fengbinzhu's Stars

• georgezouq/awesome-ai-in-finance

  🔬 A curated list of awesome LLMs & deep learning strategies & tools in financial market.

  3.4k413

• isXinLiu/Awesome-MLLM-Safety

  Accepted by IJCAI-24 Survey Track

  Language:Python1342

• BradyFU/Awesome-Multimodal-Large-Language-Models

  :sparkles::sparkles:Latest Advances on Multimodal Large Language Models

  12k769

• fengbinzhu/TAT-LLM

  TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data

  Language:Python81

• teacherpeterpan/self-correction-llm-papers

  This is a collection of research papers for Self-Correcting Large Language Models with Automated Feedback.

  39923

• facebookresearch/atlas

  Code repository for supporting the paper "Atlas Few-shot Learning with Retrieval Augmented Language Models",(https//arxiv.org/abs/2208.03299)

  Language:Python50868

• AI4Finance-Foundation/FinGPT

  FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.

  Language:Jupyter Notebook13.7k1.9k

• meta-llama/llama

  Inference code for Llama models

  Language:Python55.8k9.5k

• f/awesome-chatgpt-prompts

  This repo includes ChatGPT prompt curation to use ChatGPT better.

  Language:HTML111k15.2k

• openai/human-eval

  Code for the paper "Evaluating Large Language Models Trained on Code"

  Language:Python2.3k334

• NExTplusplus/TAT-DQA

  TAT-DQA: Towards Complex Document Understanding By Discrete Reasoning

  181

• lupantech/dl4math

  Resources of deep learning for mathematical reasoning (DL4MATH).

  33526

• microsoft/unilm

  Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

  Language:Python19.6k2.5k

• NExTplusplus/L2I

  The baseline method for CCIR 22 https://www.datafountain.cn/competitions/573

  Language:Python131

• 6estates/idp-tutorial

  A Tutorial on how to apply 6Estates Intelligent Document Processing(IDP) Platform

  Language:Vue1

• 6estates/idp-nodejs

  A node.js SDK for communicating with the 6Estates Intelligent Document Processing(IDP) Platform.

  Language:JavaScript1

• 6estates/idp-golang

  A Golang SDK for communicating with the 6Estates Intelligent Document Processing(IDP) Platform

  Language:Go11

• 6estates/idp-java

  A Java SDK for communicating with the 6Estates Intelligent Document Processing(IDP) Platform.

  Language:Java3

• 6estates/idp-ruby

  A Ruby SDK for communicating with the 6Estates Intelligent Document Processing(IDP) Platform.

  Language:Ruby11

• 6estates/idp-python

  A Python SDK for communicating with the 6Estates Intelligent Document Processing(IDP) Platform.

  Language:Python31

• kubeflow/arena

  A CLI for Kubeflow.

  Language:Go733177

• LYH-YF/MWPToolkit

  MWPToolkit is an open-source framework for math word problem(MWP) solvers.

  Language:Python16237

• befelix/safe_learning

  Safe reinforcement learning with stability guarantees

  Language:Python22263

• NExTplusplus/TAT-QA

  TAT-QA (Tabular And Textual dataset for Question Answering) contains 16,552 questions associated with 2,757 hybrid contexts from real-world financial reports.

  Language:Python9022

• tstanislawek/awesome-document-understanding

  A curated list of resources for Document Understanding (DU) topic

  1.3k145

• andrewdcampbell/OpenCV-Document-Scanner

  An interactive document scanner built in Python using OpenCV featuring automatic corner detection, image sharpening, and color thresholding.

  Language:Python478144

• zzzDavid/ICDAR-2019-SROIE

  ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction

  Language:Python383132

• google-research-datasets/natural-questions

  Natural Questions (NQ) contains real user questions issued to Google search, and answers found from Wikipedia by annotators. NQ is designed for the training and evaluation of automatic question answering systems.

  Language:Python921152

• teacherpeterpan/Question-Generation-Paper-List

  A summary of must-read papers for Neural Question Generation (NQG)

  58279

• crazydonkey200/neural-symbolic-machines

  Neural Symbolic Machines is a framework to integrate neural networks and symbolic representations using reinforcement learning, with applications in program synthesis and semantic parsing.

  Language:Python37569