task_categories:question-answering task_categories:visual-question-answering size_categories:100B<n<1T language:en code region:us
---
dataset_info:
  features:
  - name: caption
    dtype: string
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: choices
    dtype: string
  - name: answer
    dtype: string
  - name: solution
    dtype: string
  - name: CTH
    dtype: bool
  splits:
  - name: train
    num_bytes: 3191572848.375
    num_examples: 3781
  - name: test
    num_bytes: 791791803
    num_examples: 946
  download_size: 3952860603
  dataset_size: 3983364651.375
task_categories:
- question-answering
- visual-question-answering
language:
- en
tags:
- code
size_categories:
- 100B<n<1T
---
# Dataset Card for "BilbaoQA2"

## Dataset Description

- **Homepage:** https://github.com/TheMrguiller/MUCSI_Modal
- **Repository:** https://github.com/TheMrguiller/MUCSI_Modal
- **Paper:** It is a follow up of the Flamingo model paper
- **Leaderboard:**
- **Point of Contact:** https://github.com/TheMrguiller/MUCSI_Modal
### Dataset Summary
This dataset was collected for a proyect for a master degree in Computation and Intelligent System from University of Deusto. It was done by students and recolected from webpages famous in the Basque Country: Deia and Getimages. The questions and answers were created using a set of models that are able to generate this information from a description of a text.
### Supported Tasks and Leaderboards
The dataset is prepared to used it for visual question-answering. 
### Languages
The dataset is in english.
## Dataset Structure
### Data Fields
- `image`: This field has the image, which is the context given to the model.
- `question`: This field incorporates the question that has to answer the model from the image context.
- `choices`: Multiple choice selection.
- `answer`: The answer from the multiple choice.
- `solution`: The chain of thought process of the solution selection.
- `CTH`: A flag that indicates whether it doesnt have chain of thought in that row.
### Data Splits
The dataset is split in 80% train and 20% test.
## Considerations for Using the Data
The dataset has some flaws regarding to the descriptions. The descriptions sometimes are to specific for a captioning task. There are also to many futbol match data, so it isnt to well balanced. There are also some description that are to generic. There are some repetition in the answers due to the bad quality of the descriptions, be aware of this.
## Additional Information
### Dataset Curators
The curators of this dataset where the students from the Masters degree in Computation and Inteligent Systems from University of Deusto.