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。