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--- |
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language: |
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- id |
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- sw |
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- ta |
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- tr |
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- zh |
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- en |
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license: cc-by-4.0 |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- visual-question-answering |
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pretty_name: MaRVL |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: hypothesis |
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dtype: string |
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- name: hypo_en |
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dtype: string |
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- name: language |
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dtype: string |
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- name: label |
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dtype: bool |
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- name: chapter |
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dtype: string |
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- name: concept |
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dtype: string |
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- name: annotator_info |
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struct: |
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- name: age |
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dtype: int64 |
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- name: annotator_id |
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dtype: string |
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- name: country_of_birth |
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dtype: string |
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- name: country_of_residence |
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dtype: string |
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- name: gender |
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dtype: string |
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- name: left_img_id |
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dtype: string |
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- name: right_img_id |
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dtype: string |
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- name: left_img |
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struct: |
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- name: bytes |
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dtype: binary |
|
- name: path |
|
dtype: 'null' |
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- name: right_img |
|
struct: |
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- name: bytes |
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dtype: binary |
|
- name: path |
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dtype: 'null' |
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- name: resized_left_img |
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struct: |
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- name: bytes |
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dtype: binary |
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- name: path |
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dtype: 'null' |
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- name: resized_right_img |
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struct: |
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- name: bytes |
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dtype: binary |
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- name: path |
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dtype: 'null' |
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- name: vertically_stacked_img |
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struct: |
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- name: bytes |
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dtype: binary |
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- name: path |
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dtype: 'null' |
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- name: horizontally_stacked_img |
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struct: |
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- name: bytes |
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dtype: binary |
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- name: path |
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dtype: 'null' |
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splits: |
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- name: id |
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num_bytes: 2079196646 |
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num_examples: 1128 |
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- name: sw |
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num_bytes: 899838181 |
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num_examples: 1108 |
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- name: ta |
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num_bytes: 801784098 |
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num_examples: 1242 |
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- name: tr |
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num_bytes: 1373652829 |
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num_examples: 1180 |
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- name: zh |
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num_bytes: 1193602152 |
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num_examples: 1012 |
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download_size: 6234764237 |
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dataset_size: 6348073906 |
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configs: |
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- config_name: default |
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data_files: |
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- split: id |
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path: data/id-* |
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- split: sw |
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path: data/sw-* |
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- split: ta |
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path: data/ta-* |
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- split: tr |
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path: data/tr-* |
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- split: zh |
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path: data/zh-* |
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--- |
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|
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# MaRVL |
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### This is a copy from the original repo: https://github.com/marvl-challenge/marvl-code |
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|
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If you use this dataset, please cite the original authors: |
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```bibtex |
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@inproceedings{liu-etal-2021-visually, |
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title = "Visually Grounded Reasoning across Languages and Cultures", |
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author = "Liu, Fangyu and |
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Bugliarello, Emanuele and |
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Ponti, Edoardo Maria and |
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Reddy, Siva and |
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Collier, Nigel and |
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Elliott, Desmond", |
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booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", |
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month = nov, |
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year = "2021", |
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address = "Online and Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.emnlp-main.818", |
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pages = "10467--10485", |
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} |
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``` |
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### Additional data |
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In addition to the data available in the original repo, this dataset contains the following columns |
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* `en_translation` --> English translation of the `hypothesis` created using Bing Translate |
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* `left_img` --> PIL Image |
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* `right_img`--> PIL Image |
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* `resized_left_img` --> PIL Image resized |
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* `resized_right_img` --> PIL Image resized |
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* `vertically_stacked_img` --> PIL image that contains the left and right resized images stacked vertically with a black gutter of `10px` |
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* `horizontally_stacked_img` --> PIL image that contains the left and right resized images stacked horizontally with a black gutter of `10px` |
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|
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The images were resized using [`img2dataset`](https://github.com/rom1504/img2dataset/blob/main/img2dataset/resizer.py): |
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<details> |
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<summary>Show code snippet</summary> |
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|
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```python |
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Resizer( |
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image_size=640, |
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resize_mode=ResizeMode.keep_ratio, |
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resize_only_if_bigger=True, |
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) |
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``` |
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|
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</details> |
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|
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### How to read the images |
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Due to a [bug](https://github.com/huggingface/datasets/issues/4796), 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: |
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|
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```python |
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from datasets import Image, load_dataset |
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|
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ds = load_dataset("floschne/marvl", split="sw") |
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ds.map( |
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lambda sample: { |
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"left_img_t": [Image().decode_example(img) for img in sample["left_img"]], |
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"right_img_t": [Image().decode_example(img) for img in sample["right_img"]], |
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"resized_left_img_t": [ |
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Image().decode_example(img) for img in sample["resized_left_img"] |
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], |
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"resized_right_img_t": [ |
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Image().decode_example(img) for img in sample["resized_right_img"] |
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], |
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"vertically_stacked_img_t": [ |
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Image().decode_example(img) for img in sample["vertically_stacked_img"] |
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], |
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"horizontally_stacked_img_t": [ |
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Image().decode_example(img) for img in sample["horizontally_stacked_img"] |
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], |
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}, |
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remove_columns=[ |
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"left_img", |
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"right_img", |
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"resized_left_img", |
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"resized_right_img", |
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"vertically_stacked_img", |
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"horizontally_stacked_img", |
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], |
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).rename_columns( |
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{ |
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"left_img_t": "left_img", |
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"right_img_t": "right_img", |
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"resized_left_img_t": "resized_left_img", |
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"resized_right_img_t": "resized_right_img", |
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"vertically_stacked_img_t": "vertically_stacked_img", |
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"horizontally_stacked_img_t": "horizontally_stacked_img", |
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} |
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) |
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|
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``` |