AdaptLLM/NER

该数据集用于命名实体识别（NER）任务，特别是在金融领域。数据集包含训练和测试文件，分别位于train.csv和test.csv中。该数据集是用于支持大语言模型在特定领域（如金融、生物医学和法律）的持续预训练，以提高其在问答任务中的表现。数据集的使用背景是基于ICLR 2024论文《Adapting Large Language Models via Reading Comprehension》中的研究，该研究提出了一种通过阅读理解文本转换大规模预训练语料库的方法，以提升模型在特定领域的表现。

This dataset is used for Named Entity Recognition (NER) tasks, particularly in the finance domain. The dataset includes training and test files located in train.csv and test.csv, respectively. It is designed to support the continual pre-training of large language models in specific domains such as finance, biomedicine, and law, to improve their performance in question-answering tasks. The dataset is part of the research presented in the ICLR 2024 paper Adapting Large Language Models via Reading Comprehension, which proposes a method to transform large-scale pre-training corpora into reading comprehension texts to enhance model performance in domain-specific tasks.

原始信息汇总

数据集概述

数据集名称

• NER

数据文件

• 训练集: train.csv
• 测试集: test.csv

任务类别

• 文本分类
• 问答
• 零样本分类

语言

• 英语 (en)

标签

• 金融