Datasets:

Jaiccc
/

Terminal_Recording_Group_Boundary_Timestamp

license: mit
task_categories:
  - text-generation
  - token-classification
pretty_name: Terminal Recording Group Boundary Timestamps
size_categories:
  - n < 1K
tags:
  - terminal
  - xml
  - boundary-detection

Terminal Recording Group Boundary Dataset

This dataset consists of raw terminal recording logs in XML format, paired with ground-truth timestamps for shell prompt boundaries.

Model 0: Terminal Boundary Extractor

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). 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.

Generation Logic & Sliding Windows (Important)

The label the model must predict is the boundary timestamp—the exact moment the shell prompt returns, signaling the end of an event.

To generate robust training data, we use a sliding window approach encompassing exactly three consecutive boundary timestamps (T1, T2, and T3) for each input:

T1 (The Anchor): The known starting timestamp of the current event.
T2 (The Target): The actual event boundary the model must predict.
T3 (The Context Cutoff): The end of the input window.

Why include the third timestamp (T3)?

Each training input contains all <user_input> and <system_output> XML tags from T1 all the way to T3.

If the input window stopped at T2, the task would be trivial: the model would eventually realize it just needs to extract the very last timestamp present in the provided text. By extending the context to T3, we include a "distractor" event. This forces the model to semantically understand the terminal log and identify the internal boundary dividing the two separate events, rather than just pointing to the end of the file.

Example

Assume a recording has three sequential boundaries: 0.007194, 21.987222, and 37.178971.

The Input Window: Contains all XML chunks from 0.007194 to 37.178971.
The Content: This window actually covers two distinct events:
- Event A: Context between 0.007194 and 21.987222.
- Event B: Context between 21.987222 and 37.178971.
The Target Output: 21.987222 (the dividing boundary between Event 1 and Event 2).