t5-model

中文对话0.2B小模型（ChatLM-Chinese-0.2B），开源所有数据集来源、数据清洗、tokenizer训练、模型预训练、SFT指令微调、RLHF优化等流程的全部代码。支持下游任务sft微调，给出三元组信息抽取微调示例。

pytorch实现 Bert 做seq2seq任务，使用unilm方案,现在也可以做自动摘要，文本分类，情感分析，NER，词性标注等任务,支持t5模型，支持GPT2进行文章续写。

PromptCLUE, 全中文任务支持零样本学习模型

simpleT5 is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.

Official implementation of the paper "CoEdIT: Text Editing by Task-Specific Instruction Tuning" (EMNLP 2023)

Official implementation of the paper "CoEdIT: Text Editing by Task-Specific Instruction Tuning" (EMNLP 2023)

AraT5: Text-to-Text Transformers for Arabic Language Understanding

The "LLM Projects Archive" is a centralized GitHub repository, offering a diverse collection of Language Model Models projects. A valuable resource for researchers, developers, and enthusiasts, it showcases the latest advancements and applications in the realm of LLMs. Explore and contribute to the dynamic landscape of language model projects.

Implement Question Generator with SOTA pre-trained Language Models (RoBERTa, BERT, GPT, BART, T5, etc.)

NLP model zoo for Russian

📚 Unearth the Core of Knowledge in Minutes 📓

A extension of Transformers library to include T5ForSequenceClassification class.

Abstractive text summarization by fine-tuning seq2seq models.

Materials for "IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation" 🇮🇹

This repository contains the data and code for the paper "Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors" (SPNLP@ACL2022)

Training T5 to perform numerical reasoning.

In this implementation, using the Flan T5 large language model, we performed the Text Classification task on the IMDB dataset and obtained a very good accuracy of 93%.

End-to-End Model - Finetuned T5 for Text-to-SPARQL Task

Automated Headline generation and Aspect Based Sentiment Analysis

About Code for the paper "NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models" (EMNLP 2023 Findings)