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@@ -18,8 +18,8 @@ This dataset consists of raw terminal recording logs in XML format, paired with
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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.
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  ## Generation Logic & Sliding Windows (Important)
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@@ -45,4 +45,4 @@ Assume a recording has three sequential boundaries: `0.007194`, `21.987222`, and
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  2. **The Content:** This window actually covers two distinct events:
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  * **Event A:** Context between `0.007194` and `21.987222`.
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  * **Event B:** Context between `21.987222` and `37.178971`.
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- 3. **The Target Output:** `21.987222` (the dividing boundary).
 
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  # Model 0: Terminal Boundary Extractor
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+ Terminal recordings are 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. The model is trained to identify the precise timestamp boundary where one event ends and the next begins.
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  ## Generation Logic & Sliding Windows (Important)
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  2. **The Content:** This window actually covers two distinct events:
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  * **Event A:** Context between `0.007194` and `21.987222`.
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  * **Event B:** Context between `21.987222` and `37.178971`.
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+ 3. **The Target Output:** `21.987222` (the dividing boundary between Event 1 and Event 2).