README.md

metadata

language:
  - ar
  - en
  - es
  - fr
  - ru
license: mit
size_categories:
  - 1K<n<10K
task_categories:
  - visual-question-answering
pretty_name: XVNLI
dataset_info:
  features:
    - name: label
      dtype: string
    - name: caption
      dtype: string
    - name: hypothesis
      dtype: string
    - name: caption_id
      dtype: string
    - name: pair_id
      dtype: string
    - name: flikr30k_id
      dtype: string
    - name: image
      struct:
        - name: bytes
          dtype: binary
        - name: path
          dtype: 'null'
  splits:
    - name: ar
      num_bytes: 45192381
      num_examples: 1164
    - name: en
      num_bytes: 45141859
      num_examples: 1164
    - name: es
      num_bytes: 45162738
      num_examples: 1164
    - name: fr
      num_bytes: 45161740
      num_examples: 1164
    - name: ru
      num_bytes: 45256629
      num_examples: 1164
  download_size: 70974300
  dataset_size: 225915347
configs:
  - config_name: default
    data_files:
      - split: ar
        path: data/ar-*
      - split: en
        path: data/en-*
      - split: es
        path: data/es-*
      - split: fr
        path: data/fr-*
      - split: ru
        path: data/ru-*

XVNLI

This is a copy from the original repo: https://github.com/e-bug/iglue

If you use this dataset, please cite the original authors:

@inproceedings{bugliarello-etal-2022-iglue,
  title = 	 {{IGLUE}: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages},
  author =       {Bugliarello, Emanuele and Liu, Fangyu and Pfeiffer, Jonas and Reddy, Siva and Elliott, Desmond and Ponti, Edoardo Maria and Vuli{\'c}, Ivan},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {2370--2392},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/bugliarello22a/bugliarello22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/bugliarello22a.html},
}

How to read the image

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/xvnli", split="en")
ds.map(
    lambda sample: {
        "image_t": [Image().decode_example(img) for img in sample["image"]],
    },
    remove_columns=["image"],
).rename_columns({"image_t": "image"})