---
dataset_info:
  features:
  - name: question_text
    dtype: string
  - name: background_description
    sequence: string
  - name: answer_text
    dtype: string
  - name: options
    sequence: string
  - name: need_image
    dtype: string
  - name: language
    dtype: string
  - name: level
    dtype: string
  - name: subject
    dtype: string
  - name: subject_category
    dtype: string
  - name: year
    dtype: string
  - name: image_ids
    sequence: string
  - name: images
    list:
    - name: bytes
      dtype: binary
    - name: path
      dtype: 'null'
  splits:
  - name: italian
    num_bytes: 56350406
    num_examples: 407
  - name: javanese
    num_bytes: 181707
    num_examples: 5
  - name: afrikaans
    num_bytes: 28552878
    num_examples: 163
  - name: thai
    num_bytes: 112113903
    num_examples: 401
  - name: chinese
    num_bytes: 43661702
    num_examples: 453
  - name: swahili
    num_bytes: 96790
    num_examples: 4
  - name: portuguese
    num_bytes: 44423012
    num_examples: 452
  - name: vietnamese
    num_bytes: 7009517
    num_examples: 116
  - name: english
    num_bytes: 78893609
    num_examples: 795
  download_size: 248223963
  dataset_size: 371283524
configs:
- config_name: default
  data_files:
  - split: italian
    path: data/italian-*
  - split: javanese
    path: data/javanese-*
  - split: afrikaans
    path: data/afrikaans-*
  - split: thai
    path: data/thai-*
  - split: chinese
    path: data/chinese-*
  - split: swahili
    path: data/swahili-*
  - split: portuguese
    path: data/portuguese-*
  - split: vietnamese
    path: data/vietnamese-*
  - split: english
    path: data/english-*
task_categories:
- visual-question-answering
language:
- it
- th
- en
- jv
- sw
- vi
- zh
- pt
- af
pretty_name: Multi-Modal M3Exam
size_categories:
- 1K<n<10K
---



# Multi-Modal M3Exam

### Note that this is a copy from https://github.com/DAMO-NLP-SG/M3Exam, which includes ONLY the multi-modal questions!

It was created due to issues in the original repo and to ease access. It also includes the image features and has a uniform and joined structure.

If you use this dataset, please cite the original authors:
```bibtex
@article{zhang2023m3exam,
    title={M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models},
    author={Wenxuan Zhang and Sharifah Mahani Aljunied and Chang Gao and Yew Ken Chia and Lidong Bing},
    year={2023},
    eprint={2306.05179},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

### How to load the image features
Due to a [bug](https://github.com/huggingface/datasets/issues/4796), the images cannot be stored as `PIL.Image.Image`s directly but need to be converted to `dataset.Image`s-. Hence, to load them, this additional step is required:
```python
from datasets import Image, load_dataset

ds = load_dataset("floschne/multimodal-m3exam", split="english")
ds.map(
    lambda sample: {
        "images_t": [Image().decode_example(img) for img in sample["images"]]
    },
    remove_columns=["images"],
).rename_column("images_t", "images")
```

<details>
  <summary>Show the code used to generate this dataset.</summary>
  This assumes that the directory `m3exam/multimodal-question/` exists and is an exact copy from the original GitHub repository.
  
  ```python
  import pandas as pd
  from pathlib import Path
  from datasets import Image, DatasetDict, Dataset, Value, Sequence
  from PIL import Image as PILImage
  from tqdm.auto import tqdm
  from copy import deepcopy
  from functools import partial
  import re
  
  tqdm.pandas()

  def get_img_ids(row, img_base_p):
    p = r"\(image\)\[image-.*\..*\]"
    imgs = re.findall(p, row["question_text"])
    for option in row["options"]:
        imgs.extend(re.findall(p, option))
    for bgdesc in row["background_description"]:
        imgs.extend(re.findall(p, bgdesc))

    img_ids = [img.split("[")[1].split("]")[0] for img in imgs]
    # remove the last character if it is a period (eg. image-1.png. -> image-1.png)
    img_ids = [img_id[:-1] if img_id[-1] == "." else img_id for img_id in img_ids]
    # remove character after the last digit (eg. image-13c.png -> image-13.png)
    img_ids = [re.sub(r"\D*\.", ".", img_id) for img_id in img_ids]
    # remove character between dots (eg. image-13.c.png -> image-13.png)
    img_ids = [re.sub(r"\.\D*\.", ".", img_id) for img_id in img_ids]

    for img_id in img_ids:
        if not (img_base_p / img_id).exists():
            # print(f"MISSING IMAGE: {img_id=}, {imgs=}, {row.name=}")
            return None
    return img_ids

  def load_images(img_ids, img_base_p):
    if img_ids is None:
        return None
    img = Image()
    return [
        img.encode_example(deepcopy(PILImage.open(img_base_p / img_id).convert("RGB")))
        for img_id in img_ids
    ]

  if __name__ == "__main__":
    dsd = DatasetDict()
    img_base_p = "m3exam/multimodal-question/images-"
    for p in (
        pbar := tqdm(
            list(Path("m3exam/multimodal-question").glob("*-questions-image.json"))
        )
    ):
        lang = p.stem.split("-")[0]
        pbar.set_description(lang)
    
        df = pd.read_json(p)
        df["image_ids"] = df.apply(
            partial(get_img_ids, img_base_p=Path(img_base_p + lang)), axis=1
        )
        df["images"] = df["image_ids"].progress_apply(
            partial(load_images, img_base_p=Path(img_base_p + lang))
        )
        df = df[~df.image_ids.isna()]
        df["year"] = df["year"].astype(str).str.strip()
        df["answer_text"] = df["answer_text"].astype(str).str.strip()
        df["question_text"] = df["question_text"].astype(str).str.strip()
        ds = Dataset.from_pandas(df.reset_index(drop=True))
        # for javanese there are no background descs thus it is interpreted as dtype null. We need to change it to string
        features = ds.features.copy()
        features["background_description"] = Sequence(
            feature=Value(dtype="string", id=None), length=-1, id=None
        )
        ds = ds.cast(features)
    
        dsd[lang] = ds

        dsd.push_to_hub(
          "floschne/multimodal-m3exam", token=<OMITTED>
        )
  
  ```
</details>