small_sparse_structured_table
收藏魔搭社区2026-01-06 更新2025-06-14 收录
下载链接：
https://modelscope.cn/datasets/nanonets/small_sparse_structured_table
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资源简介：
This dataset is generated syhthetically to create tables with following characteristics:
1. Empty cell percentage in following range [40,70] (Sparse)
2. There is clear seperator between rows and columns (Structured).
3. 4 <= num rows <= 10, 2 <= num columns <= 6 (Small)
### Load the dataset
```python
import io
import pandas as pd
from PIL import Image
def bytes_to_image(self, image_bytes: bytes):
return Image.open(io.BytesIO(image_bytes))
def parse_annotations(self, annotations: str) -> pd.DataFrame:
return pd.read_json(StringIO(annotations), orient="records")
test_data = load_dataset('nanonets/small_sparse_structured_table', split='test')
data_point = test_data[0]
image, gt_table = (
bytes_to_image(data_point["images"]),
parse_annotations(data_point["annotation"]),
)
```
本数据集为合成生成，用于构建具备如下特征的表格：
1. 空单元格占比处于区间[40,70]（稀疏型(Sparse)表格）
2. 行列间带有清晰分隔符（结构化(Structured)表格）
3. 行数满足4 ≤ 行数 ≤ 10，列数满足2 ≤ 列数 ≤ 6（小型(Small)表格）
### 加载数据集
python
import io
import pandas as pd
from PIL import Image
def bytes_to_image(self, image_bytes: bytes):
return Image.open(io.BytesIO(image_bytes))
def parse_annotations(self, annotations: str) -> pd.DataFrame:
return pd.read_json(StringIO(annotations), orient="records")
test_data = load_dataset('nanonets/small_sparse_structured_table', split='test')
data_point = test_data[0]
image, gt_table = (
bytes_to_image(data_point["images"]),
parse_annotations(data_point["annotation"]),
)
提供机构：
maas
创建时间：
2025-06-13
