Large dataset in Squad2-format
It would also be good if there is any possibility for versioning, I think this way is much better than the dynamic way.
If you mean that part to put the tiles into one is the generation it would take up to 15-20 minutes on home computer hardware.
Are there any compression or optimization algorithms while generating the dataset ?
Otherwise the hardware limit is around 32 GB ram at the moment.
If everything works well we will add some more gigabytes of data in future what would make it pretty memory costly.
At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.
Caused the computing power we splitted it into multiple tiles, but they are all in the same format.
Right now the most important facts about are this:
- Contexts: 1.047.671
- questions: 1.677.732
- Answers: 6.742.406
- unanswerable: 377.398

It is already cleaned

<pre><code>
train_data = [
    {
        'context': "this is the context",
        'qas': [
            {
                'id': "00002",
                'is_impossible': False,
                'question': "whats is this",
                'answers': [
                    {
                        'text': "answer",
                        'answer_start': 0
                    }
                ]
            },
            {
                'id': "00003",
                'is_impossible': False,
                'question': "question2",
                'answers': [
                    {
                        'text': "answer2",
                        'answer_start': 1
                    }
                ]
            }
        ]
    }
]
</code></pre>

Cause it is growing every day we are thinking about an structure like this:
We host an Json file, containing all the download links and the script can load it dynamically.
At the moment it is around ~20GB

Any advice how to handle this, or an ready to use template ?
Large dataset in Squad2-format 
 At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.
Caused the computing power we splitted it into multiple tiles, but they are all in the same format.
Right now the most important facts about are this:
- Contexts: 1.047.671
- questions: 1.677.732
- Answers: 6.742.406
- unanswerable: 377.398

It is already cleaned

<pre><code>
train_data = [
    {
        'context': "this is the context",
        'qas': [
            {
                'id': "00002",
                'is_impossible': False,
                'question': "whats is this",
                'answers': [
                    {
                        'text': "answer",
                        'answer_start': 0
                    }
                ]
            },
            {
                'id': "00003",
                'is_impossible': False,
                'question': "question2",
                'answers': [
                    {
                        'text': "answer2",
                        'answer_start': 1
                    }
                ]
            }
        ]
    }
]
</code></pre>

Cause it is growing every day we are thinking about an structure like this:
We host an Json file, containing all the download links and the script can load it dynamically.
At the moment it is around ~20GB

Any advice how to handle this, or an ready to use template ? 
 It would also be good if there is any possibility for versioning, I think this way is much better than the dynamic way.
If you mean that part to put the tiles into one is the generation it would take up to 15-20 minutes on home computer hardware.
Are there any compression or optimization algorithms while generating the dataset ?
Otherwise the hardware limit is around 32 GB ram at the moment.
If everything works well we will add some more gigabytes of data in future what would make it pretty memory costly.