Omkar2118/datasets huggingface dataset:
Cleaned_Lang8

Lang-8 Preprocessed Dataset (for GED):

 - Dataset: Lang-8, a publicly available dataset containing user-generated content, primarily from second-language learners, focused on writing errors.
 - Task: Grammatical Error Detection (GED).
 - Size: 200,000 sentences, with each sentence labeled as '0' for incorrect and '1' for its corrected version.
 - Preprocessing: The dataset has been cleaned and transformed to improve model performance, including removing noise, handling inconsistent annotations, and preparing it for training.
 - Usage: Ideal for training and evaluating models for GED and GEC.

Introducing paper: Enhancing Grammatical Error Detection using BERT with Cleaned Lang-8 Dataset
TOFU
Full name: Task of Fictitious Unlearning

The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set.

The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 chat, and Phi-1.5 models, but can be easily adapted to other models.

Introducing paper: TOFU: A Task of Fictitious Unlearning for LLMs