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leafspark
/

tracktext

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Text Classification

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train · 14.4k rows

text stringlengths 6 41k
{data}
[0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6,...
{action}
[1, 0, 0, 0]
{data}
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{action}
[1, 0, 0, 0]
{data}
[0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.5, 0.5, 0.3, 0.5, 0.6, 0.7, 0.6, 0.3, 0.2, 0.6, 0.7, 0.7, 0.7, 0.7, 0.5, 0.4, 0.4, 0.5, 0.4, 0.5, 0.4, 0.5, 0.7, 0.6, 0.5, 0.4,...
{action}
[1, 0, 0, 0]
{data}
[0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.4, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1, 0.1, 0.5, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6,...
{action}
[1, 0, 0, 0]
{data}

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tracktext

An experimental dataset that contains 128x64 greyscale images of TrackMania gameplay + keystrokes, designed for LLMs with 16k context or above.

Inspired by DOOM-Mistral-7b :)

Dataset Details

Format:

{data}
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
...
{action}
[0, 0, 0, 0]

Greyscale Precision: 1 decimal Capture rate: 6 frames per second

Uses

To train an LLM how to play TrackMania.

Direct Use

Specialized LLM

Out-of-Scope Use

Any regular LLM

Dataset Structure

Text files

Dataset Creation

Python script in repo

Curation Rationale

Self driving using an LLM? For fun

Source Data

TrackMania 2020 screengrabs

Personal and Sensitive Information

None

Bias, Risks, and Limitations

The resolution is not very high; there may be suboptimal results

Recommendations

Don't expect anything good

Downloads last month: 152

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