---
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`.

1. **The Input Window:** Contains all XML chunks from `0.007194` to `37.178971`.
2. **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`.
3. **The Target Output:** `21.987222` (the dividing boundary between Event 1 and Event 2).