# LingoQA Datasets README

## Overview

The LingoQA datasets comprise a collection of complementary datasets designed for training and evaluating machine learning models on video understanding and question-answering tasks. These datasets are categorized into three main types: action, scenery, and evaluation, each containing video segments, questions, and answers, along with associated images to aid in visual understanding tasks.

## Directory Structure
The datasets are organized as follows:

```
LingoQA/
├── action/
│   ├── train.parquet          # Training data for action-related questions
│   └── images.zip             # Zipped directory of images related to action segments
├── scenery/
│   ├── train.parquet          # Training data for scenery-related questions
│   └── images.zip             # Zipped directory of images related to scenery segments
└── evaluation/
    ├── val.parquet            # Validation data for evaluating models
    └── images.zip             # Zipped directory of images related to evaluation segments
```

## Data Format
Each .parquet file within the datasets contains the following columns:

- `segment_id`: An md5 hash that uniquely identifies each video segment.
- `question_id`: A unique identifier for each question associated with a video segment.
- `images`: An array of relative paths pointing to images that correspond to the video segment. Example format: ['images/train/hash_segment_1/0.jpg', ...].
- `question`: The question text related to the video segment.
- `answer`: The answer text related to the question posed.


## How to Use

### Preparing the Data

1. **Extract Images**: First, unzip the images.zip files in their respective directories to access the images associated with each dataset.

```bash
unzip action/images.zip -d action/
unzip scenery/images.zip -d scenery/
unzip evaluation/images.zip -d evaluation/
```

2. **Load Datasets**: You can load the `.parquet` files using Python with libraries such as pandas or pyarrow. Here's an example of how to load a dataset:

```python
import pandas as pd

# Load an example dataset
action_train = pd.read_parquet('action/train.parquet')
```

### Using the Data

- Model Training: Use the train.parquet files from the action and scenery datasets to train your machine learning models. These datasets provide a rich set of questions and answers along with images to facilitate training models capable of understanding and responding to queries about video content.

- Model Evaluation: The evaluation/val.parquet file is intended for validating the performance of your models. It offers a separate set of questions and answers to test your model's ability to generalize to new data.

## License
Please ensure to review the license agreement associated with the LingoQA datasets before using them for your projects.