axiong/pmc_oa

# PMC-OA Dataset **News: We have released the PMC-OA dataset. You can choose the subset specifically.** **P.S.** There's something wrong with the huggingface dataset viewer when the dataset scale gets large. So we sample a subset of it to visualize it directly on web. Click [PMC-OA-Demo](https://huggingface.co/datasets/axiong/pmc_oa_demo) to view it. [中文文档](./README.zh.md) - [PMC-OA Dataset](#pmc-oa-dataset) - [Model Zoo](#model-zoo) - [Daraset Structure](#daraset-structure) - [Sample](#sample) ## Model Zoo Check it out if you want to load model pretrained on PMC-OA directly. We plan to release more models pretrained on PMC-OA. Feel free to reach us if the model you want is not included in model zoo for now. Also, we express our thanks to the help from the community. | Model | Link | Provider | | --- | --- | --- | | ViT-L-14 | https://huggingface.co/ryanyip7777/pmc_vit_l_14 | @ryanyip7777 | ## Daraset Structure **PMC-OA** (seperated images, separated caption). - `images.zip`: images folder - `pmc_oa.jsonl`: dataset file of pmc-oa - `pmc_oa_beta.jsonl`: dataset file of pmc-oa-beta ~~- `train.jsonl`: metafile of train set~~ ~~- `valid.jsonl`: metafile of valid set~~ ~~- `test.jsonl`: metafile of test set~~ The difference between PMC-OA & PMC-OA-Beta lies in the methods of processing captions. In PMC-OA, we utilize ChatGPT to help us divide compound captions into seperate ones. While PMC-OA-Beta keeps all the compound ones without division. ## Sample A row in `pmc_oa.jsonl` is shown bellow, ```python { "image": "PMC212319_Fig3_4.jpg", "caption": "A. Real time image of the translocation of ARF1-GFP to the plasma membrane ...", } ``` Explanation to each key - image: path to the image - caption: corresponding to the image# PMC-OA 数据集 **新闻：我们已发布PMC-OA数据集。您可选择特定子集进行使用。** **附言**：当数据集规模较大时，Hugging Face数据集查看器会出现异常。因此我们采样生成一个子集以支持在网页端直接可视化。点击[PMC-OA演示版](https://huggingface.co/datasets/axiong/pmc_oa_demo)即可查看。 [中文文档](./README.zh.md) - [PMC-OA 数据集](#pmc-oa-dataset) - [模型库](#model-zoo) - [数据集结构](#dataset-structure) - [数据样例](#sample) ## 模型库 若您希望直接加载基于PMC-OA预训练的模型，可查阅本模型库。我们计划后续发布更多基于PMC-OA预训练的模型，若当前模型库中未包含您所需的模型，欢迎随时与我们联系。同时，我们对社区提供的帮助致以诚挚谢意。 | 模型 | 链接 | 提供者 | | --- | --- | --- | | ViT-L-14 | https://huggingface.co/ryanyip7777/pmc_vit_l_14 | @ryanyip7777 | ## 数据集结构 **PMC-OA**（分离图像与分离描述文本）。 - `images.zip`：图像文件夹压缩包 - `pmc_oa.jsonl`：PMC-OA 数据集文件 - `pmc_oa_beta.jsonl`：PMC-OA-Beta 数据集文件 ~~- `train.jsonl`：训练集元文件~~ ~~- `valid.jsonl`：验证集元文件~~ ~~- `test.jsonl`：测试集元文件~~ PMC-OA 与 PMC-OA-Beta 的区别在于描述文本的处理方式：在 PMC-OA 中，我们借助ChatGPT将复合描述文本拆分为独立的单条描述；而 PMC-OA-Beta 则保留所有复合描述文本，未进行拆分处理。 ## 数据样例 `pmc_oa.jsonl` 中的单条数据样例如下： python { "image": "PMC212319_Fig3_4.jpg", "caption": "A. Real time image of the translocation of ARF1-GFP to the plasma membrane ...", } 各字段说明如下： - image：图像文件路径 - caption：对应图像的描述文本

原始信息汇总

PMC-OA Dataset 概述

数据集结构

• PMC-OA (分离的图像，分离的标题)
  • images.zip: 图像文件夹
  • pmc_oa.jsonl: PMC-OA 数据集文件
  • pmc_oa_beta.jsonl: PMC-OA-Beta 数据集文件

数据集样本

• 示例行来自 pmc_oa.jsonl： python { "image": "PMC212319_Fig3_4.jpg", "caption": "A. Real time image of the translocation of ARF1-GFP to the plasma membrane ...", }

• 键解释：

  • image: 图像路径
  • caption: 与图像对应的描述

搜集汇总

背景与挑战

背景概述

PMC-OA是一个包含生物医学图像及其标题的大型数据集，总大小为28.8GB，提供两种不同标题处理方式的版本。数据集主要用于文本到图像的任务，并已用于预训练视觉语言模型。