Datasets:

Modalities:
Image
Text
Formats:
parquet
ArXiv:
Libraries:
Datasets
pandas
MMBench_CN / README.md
PY007's picture
Update README.md
78601bc verified
---
dataset_info:
- config_name: chinese_culture
features:
- name: index
dtype: int32
- name: question
dtype: string
- name: image
dtype: image
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer
dtype: string
- name: category
dtype: string
- name: source
dtype: string
splits:
- name: test
num_bytes: 55546140.0
num_examples: 2176
download_size: 54795762
dataset_size: 55546140.0
- config_name: default
features:
- name: index
dtype: int32
- name: question
dtype: string
- name: image
dtype: image
- name: hint
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer
dtype: string
- name: category
dtype: string
- name: source
dtype: string
- name: L2-category
dtype: string
- name: comment
dtype: string
- name: split
dtype: string
splits:
- name: dev
num_bytes: 102763038.0
num_examples: 4329
- name: test
num_bytes: 148195795.0
num_examples: 6666
download_size: 238168349
dataset_size: 250958833.0
configs:
- config_name: chinese_culture
data_files:
- split: test
path: chinese_culture/test-*
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
๐Ÿ  [Homepage](https://lmms-lab.github.io/) | ๐Ÿ“š [Documentation](docs/README.md) | ๐Ÿค— [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of the Chinese subset of [MMBench](https://arxiv.org/abs/2307.06281). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{MMBench,
author = {Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, Dahua Lin},
journal = {arXiv:2307.06281},
title = {MMBench: Is Your Multi-modal Model an All-around Player?},
year = {2023},
}
```