text-classification

This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).

Collection of papers and resources for data augmentation for NLP.

Active Learning for Text Classification in Python

BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)

A flexible, adaptive classification system for dynamic text classification

Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.

中文文本分类、序列标注工具包（pytorch），支持中文长文本、短文本的多类、多标签分类任务，支持中文命名实体识别、词性标注、分词、抽取式文本摘要等序列标注任务。 Chinese text classification and sequence labeling toolkit, supports multi class and multi label classification, text similsrity, text summary and NER.

基于Transformers的文本分类

Cybertron: the home planet of the Transformers in Go

Based on the Pytorch-Transformers library by HuggingFace. To be used as a starting point for employing Transformer models in text classification tasks. Contains code to easily train BERT, XLNet, RoBERTa, and XLM models for text classification.

Open source no-code system for text annotation and building of text classifiers

Summarization, translation, sentiment-analysis, text-generation and more at blazing speed using a T5 version implemented in ONNX.

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

BERT-based AI-generated academic text detection model

OpenTextClassification is all you need for text classification! Open text classification for everyone, enjoy your NLP journey! 这可能是目前为止最全面的开源文本分类项目，支持中英双语、多种模型、多种任务。

[NAACL 2021] This is the code for our paper `Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach'.

Datasets collection and preprocessings framework for NLP extreme multitask learning

Interpret text data with LLMs (sklearn compatible).

A comparison and discussion of different NLP methods for 5-class sentiment classification on the SST-5 dataset.

Code for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation"