Update README.md

README.md

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----
-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.
-
-## Dataset Structure
-
-Each entry in the `.jsonl` file contains:
-* **instruction**: A specialized prompt identifying the `start_timestamp` and the extraction rules.
-* **input**: Raw XML chunks including `<user_input>` and `<system_output>` tags, preserving all ANSI escape codes.
-* **output**: The target timestamp of the **first-most** shell prompt found after the starting point.
-
-## Purpose
-
-This data is designed to train **Model 0** to navigate messy terminal logs and precisely identify the point where a command execution ends and the shell prompt returns.
-
 ## Generation Logic & Sliding Windows (Important)
 
 The ultimate goal of **Model 0** is to accurately chunk continuous terminal recordings into distinct "events." The label the model must predict is the **boundary timestamp**—the exact moment one event ends and a new one begins.
 
-To generate robust training data, we use a sliding window approach encompassing exactly **three consecutive boundary timestamps** ($T_1$, $T_2$, and $T_3$) for each input:
-
-* **$T_1$ (The Anchor):** The known starting timestamp of the current event.
-* **$T_2$ (The Target):** The actual event boundary the model must predict.
-* **$T_3$ (The Context Cutoff):** The end of the input window. 
+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 ($T_3$)?
+### Why include the third timestamp (T3)?
 
-Each input is fed to the model containing all `<user_input>` and `<system_output>` XML tags from $T_1$ all the way to $T_3$. If the input simply stopped at $T_2$, the task would be trivial: the model would just learn a shortcut to extract the very last timestamp present in the text. By including the data up to $T_3$, the model is forced to semantically identify the actual boundary dividing the two separate events within the continuous log.
+Each input is fed to the model containing all `<user_input>` and `<system_output>` XML tags from **T1** all the way to **T3**. If the input simply stopped at **T2**, the task would be trivial: the model would just learn a shortcut to extract the very last timestamp present in the text. By including the data up to **T3**, the model is forced to semantically identify the actual boundary dividing the two separate events within the continuous log.
 
 ### Example