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  This dataset consists of raw terminal recording logs in XML format, paired with ground-truth timestamps for shell prompt boundaries.
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- ## Training Approach: Event Chunking
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  Terminal recordings are essentially long, continuous streams of text data. To make this data useful for downstream tasks, we must first break this stream into logical "events" (e.g., a single command execution and its resulting output).
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  **Model 0** is specifically designed for this segmentation task. Instead of just looking for text patterns, the model is trained to identify the precise temporal boundary where one event ends and the next begins.
 
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  This dataset consists of raw terminal recording logs in XML format, paired with ground-truth timestamps for shell prompt boundaries.
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+ # Model 0: Terminal Boundary Extractor
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  Terminal recordings are essentially long, continuous streams of text data. To make this data useful for downstream tasks, we must first break this stream into logical "events" (e.g., a single command execution and its resulting output).
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  **Model 0** is specifically designed for this segmentation task. Instead of just looking for text patterns, the model is trained to identify the precise temporal boundary where one event ends and the next begins.