coref-data/flan2021_coreference_raw

该数据集包含了Flan 2022集合中的所有共指消解示例，这些示例最初包含在Flan 2021中。数据集是从DataProvenanceInitiative/flan2021_submix_original预处理的数据集中复制的。数据集的特征包括inputs、targets、task_source、task_name和template_type，均为字符串类型。训练集包含116,664个样本，总大小为195,544,293.50492442字节。不包括与共指消解相关的任务，如quoref和qrecc任务。

原始信息汇总

Flan 2021 Coreference Tasks 数据集概述

数据集信息

特征

• inputs: 字符串类型
• targets: 字符串类型
• task_source: 字符串类型
• task_name: 字符串类型
• template_type: 字符串类型

数据分割

• train: 包含 116664 个样本，总大小为 195544293.50492442 字节

数据大小

• 下载大小: 26571254 字节
• 数据集大小: 195544293.50492442 字节

配置

• default: 包含训练数据文件，路径为 data/train-*

数据集详情

包含的任务名称

• definite_pronoun_resolution:1.1.0
• glue/wnli:2.0.0
• super_glue/wsc.fixed:1.0.2
• winogrande:1.1.0

数据来源

• 数据来源于预处理的 Flan2021 数据集，位于 DataProvenanceInitiative/flan2021_submix_original

引用

@inproceedings{flan_2022_collection, author = {Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V. and Zoph, Barret and Wei, Jason and Roberts, Adam}, title = {The flan collection: designing data and methods for effective instruction tuning}, year = {2023}, publisher = {JMLR.org}, abstract = {We study the design decisions of publicly available instruction tuning methods, by reproducing and breaking down the development of Flan 2022 (Chung et al., 2022). Through careful ablation studies on the Flan Collection of tasks and methods, we tease apart the effect of design decisions which enable Flan-T5 to outperform prior work by 3-17%+ across evaluation settings. We find task balancing and enrichment techniques are overlooked but critical to effective instruction tuning, and in particular, training with mixed prompt settings (zero-shot, few-shot, chain-of-thought) actually yields equivalent or stronger (2%+) performance in all settings. In further experiments, we show Flan-T5 requires less finetuning to converge higher and faster than T5 on single downstream tasks--motivating instruction-tuned models as more computationally-efficient starting checkpoints for new tasks. Finally, to accelerate research on instruction tuning, we make the Flan 2022 collection of datasets, templates, and methods publicly available.}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, articleno = {941}, numpages = {18}, location = {Honolulu, Hawaii, USA}, series = {ICML23} }