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Piji Li's Projects

li_emnlp_2017 icon li_emnlp_2017

Deep Recurrent Generative Decoder for Abstractive Text Summarization in DyNet

lihang icon lihang

Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]

llama icon llama

Inference code for LLaMA models

llama_index icon llama_index

LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data.

ltp icon ltp

Language Technology Platform

mathq icon mathq

Data and code for the paper "A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level" by Drori et al., 2022.

mctx icon mctx

Monte Carlo tree search in JAX

megatron-lm icon megatron-lm

Ongoing research training transformer language models at scale, including: BERT & GPT-2

methane-gapfill-ml icon methane-gapfill-ml

Python codebase for gap-filling eddy covariance methane fluxes at FLUXNET-CH4 wetlands with machine learning.

minedojo icon minedojo

Building Open-Ended Embodied Agents with Internet-Scale Knowledge

minigpt-4 icon minigpt-4

MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models

moo_elm icon moo_elm

Multi-Objective Optimization of ELM for RUL Prediction

moss icon moss

An open-source tool-augmented conversational language model from Fudan University

mucgec icon mucgec

MuCGEC中文纠错数据集及文本纠错SOTA模型开源;Code & Data for our NAACL 2022 Paper "MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction"

n-cmapss_dl icon n-cmapss_dl

N-CMAPSS data preparation for Machine Learning and Deep Learning models. (Python source code for new CMAPSS dataset)

nas_transformer icon nas_transformer

Evolutionary Neural Architecture Search on Transformers for RUL Prediction

neural-brain-decoding icon neural-brain-decoding

The purpose of this repository is to collect and investigate language oriented neural decoding work

neural-summ-cnndm-pytorch icon neural-summ-cnndm-pytorch

Neural abstractive summarization (seq2seq + copy (or pointer network) + coverage) in pytorch on CNN/Daily Mail

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