luca0621/amex-gelab

--- pretty_name: AMEX SFT task_categories: - image-text-to-text - text-generation tags: - gui - mobile - navigation - multimodal - ge-lab size_categories: - 10K<n<100K --- # AMEX SFT This dataset is a packaged export of the local `amex_sft` directory for uploading to the Hugging Face Hub as a dataset repository. ## Source - Source dataset roots: - `/ext_hdd2/tsyou/gelab-env/data_engine/amex_sft` (3046 trajectories) - Number of trajectory folders: `3046` - Number of tar shards: `61` - Trajectories per shard: `50` ## Layout - `shards/*.tar`: tar shards containing trajectory folders - `manifest.jsonl`: trajectory-to-shard index - `dataset_info.json`: high-level metadata Each tar shard preserves the original trajectory folder layout. For example: ```text <source_root>/<trajectory_id>/ ui_structure.json ui_structure_layer.json trajectory_assets_manifest.json action_coord/... extracted_assets/... ``` ## Why tar shards? The raw source contains a very large number of small PNG files. Packaging them into tar shards makes Hub upload and downstream download much more reliable for large-scale storage. ## Notes - This repository is intended for storage and reuse of the packaged dataset. - If you want Hub-native previews and lighter access patterns, consider a future Parquet/WebDataset conversion.pretty_name: AMEX SFT task_categories: - 图像-文本转文本 - 文本生成 tags: - 图形用户界面（GUI） - 移动（Mobile） - 导航（Navigation） - 多模态（Multimodal） - GE实验室（GE-LAB） size_categories: - 10,000 < 样本数 < 100,000 --- # AMEX SFT 本数据集为本地`amex_sft`目录的打包导出产物，用于作为数据集仓库上传至Hugging Face Hub。 ## 源数据说明 - 源数据集根目录： - `/ext_hdd2/tsyou/gelab-env/data_engine/amex_sft`（共3046条轨迹） - 轨迹文件夹数量：3046 - Tar分片数量：61 - 单分片包含轨迹数：50 ## 目录结构 - `shards/*.tar`：包含轨迹文件夹的Tar分片文件 - `manifest.jsonl`：轨迹-分片索引文件 - `dataset_info.json`：高级元数据文件 每个Tar分片均保留原始轨迹文件夹的目录结构，示例如下： text <源根目录>/<轨迹ID>/ ui_structure.json ui_structure_layer.json trajectory_assets_manifest.json action_coord/... extracted_assets/... ## 为何采用Tar分片打包？ 原始源文件包含大量小型PNG文件，将其打包为Tar分片可显著提升Hugging Face Hub上传及下游下载的可靠性，更适配大规模存储场景。 ## 注意事项 - 本仓库仅用于该打包数据集的存储与复用。 - 若需实现Hub原生预览与更轻量化的访问模式，可考虑后续将数据集转换为Parquet或WebDataset格式。