Datasets:
Tasks:
Visual Question Answering
Modalities:
Text
Formats:
text
Sub-tasks:
visual-question-answering
Size:
10K - 100K
License:
language: | |
- vi | |
- en | |
pretty_name: OK-VQA in multilang | |
source-datasets: | |
- OK-VQA | |
- COCO | |
tags: | |
- OK-VQA | |
- OK-VQA-vi | |
license: unknown | |
task_categories: | |
- visual-question-answering | |
task_ids: | |
- visual-question-answering | |
# OK-VQA in multilang | |
This is Google-translated versions of [OK-VQA](https://okvqa.allenai.org/index.html) in many languages. Each language version stays in each folder. | |
The process of building Vietnamese version as follows: | |
- In `en/` folder, | |
- From [OK-VQA](https://okvqa.allenai.org/index.html), obtain all json files: `mscoco_train2014_annotations.json`, `mscoco_val2014_annotations.json`, `OpenEnded_mscoco_train2014_questions.json`, `OpenEnded_mscoco_val2014_questions.json`. | |
- By using [set data structure](https://docs.python.org/3/tutorial/datastructures.html#sets), generate txt files of unique text: `train_answer_list.txt`, `train_question_list.txt`, `val_answer_list.txt`, `val_question_list.txt`. | |
- In `vi/` folder, | |
- By translating 4 `en/.txt` files, generate `train_answer_list.jsonl`, `train_question_list.jsonl`, `val_answer_list.jsonl`, `val_question_list.jsonl`. In each of entry of each file, the key is the original english text, the value is the translated text in vietnamese. | |
To load Vietnamese version in your code, you need original English version. Then just use English text as key to retrieve Vietnamese value from jsonl files. I provide both English and Vietnamese version. | |
Other languages (if added) shall follow the same process. | |
Current languages: | |
- en | |
- vi | |
There will be more if I have time. | |
Please refer to [this code](https://github.com/dinhanhx/velvet/blob/main/scripts/apply_translate_okvqa.py) to apply translation. |