Omkar2118/datasets huggingface dataset:
ChineseSquad

**ChineseSquad** (中文机器阅读理解数据集) is a dataset specifically designed for **Chinese machine reading comprehension**. It is created by translating and manually correcting the original **SQuAD (Stanford Question Answering Dataset)** into Chinese. The dataset includes both **V1.1** and **V2.0** versions of SQuAD. However, due to some translation challenges (especially with short answers and document translations), the Chinese version has slightly fewer examples compared to the original English SQuAD¹.

Here are some key details about the ChineseSquad dataset:

- **Data Sources**:
    - ChineseSquad is derived from the original SQuAD dataset through machine translation and subsequent manual corrections.
    - It includes both the **V1.1** and **V2.0** versions.
- **Data Size**:
    - The dataset contains both questions with answers and questions without answers.
    - Here's a breakdown of the data:
        - **squad-zen 1.0 train**: 68,213 examples with answers, 43,498 examples without answers (total: 110k)
        - **squad-zen 1.0 dev**: 8,326 examples with answers, 5,954 examples without answers (total: 14k)
        - **squad 2.0 train**: 46,530 examples with answers, 43,498 examples without answers (total: 90k)
        - **squad 2.0 dev**: 3,391 examples with answers, 5,945 examples without answers (total: 9k)
        - **squad 1.1 dev**: 7,679 examples (no answers provided)
        - **squad 1.1 train**: 55,526 examples (no answers provided)
AesBench

**AesBench** is an expert benchmark designed to comprehensively evaluate the aesthetic perception capacities of **Multimodal Large Language Models (MLLMs)** when it comes to **image aesthetics perception**. Let me break it down for you:

1. **Purpose and Challenge**:
   - MLLMs, which combine language and vision, are rapidly advancing.
   - However, their performance in **aesthetic perception** (assessing the beauty or visual appeal of images) remains uncertain.
   - The lack of a specific benchmark for evaluating MLLMs in this domain hinders their further development.

2. **What Is AesBench?**:
   - AesBench addresses this challenge by providing a comprehensive benchmark.
   - It evaluates MLLMs' aesthetic perception abilities through **dual facets**:
     - **Expert-labeled Aesthetics Perception Database (EAPD)**: This database contains diverse image contents with high-quality annotations from professional aesthetic experts.
     - **Integrative Criteria**: AesBench proposes criteria to measure MLLMs' aesthetic perception abilities from four perspectives:
       - **Perception (AesP)**: How well MLLMs perceive aesthetics.
       - **Empathy (AesE)**: Their ability to empathize with aesthetic preferences.
       - **Assessment (AesA)**: How accurately they assess aesthetics.
       - **Interpretation (AesI)**: Their understanding of aesthetic features.

3. **Findings**:
   - Extensive experiments reveal that current MLLMs possess only **rudimentary aesthetic perception ability**.
   - There remains a significant gap between MLLMs and human aesthetic perception.

In summary, AesBench provides a valuable tool for assessing how well MLLMs understand and appreciate the beauty of images. 📸🌟

(1) [2401.08276] AesBench: An Expert Benchmark for Multimodal Large .... https://arxiv.org/abs/2401.08276.
(2) GitHub - yipoh/AesBench: An expert benchmark aiming to comprehensively .... https://github.com/yipoh/AesBench.
(3) AesBench/README.md at main · yipoh/AesBench · GitHub. https://github.com/yipoh/AesBench/blob/main/README.md.
(4) undefined. https://doi.org/10.48550/arXiv.2401.08276.

Introducing paper: AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception