ZhanxiangHua/WeatherQA_SFT

WeatherQA_SFT 是一个基于 WeatherQA 数据集的监督微调（SFT）数据集，采用 sharegpt 格式，专为视觉/多模态语言模型设计。该数据集聚焦于美国本土（CONUS）的严重天气地理位置定位和潜在灾害分析。数据集包含超过7,000个图像-文本对，包括2014年至2019年的训练集和2020年的测试集。图像涵盖了复杂的天气模式，如环境不稳定性参数、地表观测和雷达反射率合成。每个图像都与格式化的文本配对，用于监督微调，基于两步骤的多项选择题问答任务：受影响区域预测和严重对流体分类。该数据集旨在促进开发能够推理动态和复杂气象现象的多模态模型。

WeatherQA_SFT is a supervised fine-tuning (SFT) dataset in sharegpt format derived from the WeatherQA dataset for vision/multimodal language models. It focuses on severe weather geo-localization and potential hazard analysis over the Contiguous United States (CONUS). The dataset contains over 7,000 image-text pairs, including a train set from the year 2014-2019 and a test set for the year 2020. The images encapsulate complex weather patterns, such as environmental instability parameters, surface observations, and radar reflectivity composites. Each image is paired with text formatted for supervised fine-tuning, based on a two-step multiple-choice question-answering task: Affected Area Prediction and Severe Convection Classification. This dataset aims to facilitate the development and evaluation of multimodal models capable of reasoning about dynamic and complex meteorological phenomena.