floschne/multimodal-m3exam

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:

@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, the images cannot be stored as PIL.Image.Images directly but need to be converted to dataset.Images-. Hence, to load them, this additional step is required:

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")

Show the code used to generate this dataset.

This assumes that the directory `m3exam/multimodal-question/` exists and is an exact copy from the original GitHub repository.

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>
      )