---
license: mit
task_categories:
- text-classification
tags:
- code
pretty_name: model 0 training dataset
size_categories:
- 1K<n<10K
---
# ## Dataset Card: Terminal Log Boundary Prediction (Streaming)

### 📋 Dataset Purpose & Model 0 Overview
This dataset is designed to train **"Model 0"** for the Winter 2026 iteration of the **AutoDocs** project. 
You can access the official repository here:  
[AutoDocs (Winter 2026) Repository](https://github.com/CSC392-CSC492-Building-AI-ML-systems/AutoDocs-Winter2026/tree/main) 

#### Objective
The primary objective of the model is the **binary classification of sequential data**. 
It is engineered to process continuous, timestamped terminal logs formatted in XML to determine 
if a specific line represents a **"Boundary"** between logical events.

#### Methodology: Sliding-Window Approach
Instead of ingesting a massive log file in its entirety, the dataset employs a **sliding-window approach**. 
The model analyzes a short historical context to evaluate the **Target Line** (the most recent entry):
* **Pattern Recognition**: The model looks at the previous 14 timesteps (e.g., "The terminal has been downloading packages").
* **Boundary Prediction**: It predicts if the Target Line breaks that pattern (e.g., "The download finished and the shell prompt returned")
* or represents the continuation of the ongoing process.
  
### 🗂️ Dataset Structure
The dataset is in `JSONL` format, each row contains three primary fields:
* **`instruction`**: The system prompt defining "new" vs. "old" events.
* **`input`**: The sliding-window data, split into:
    * `### CONTEXT`: Up to 14 historical XML chunks. (Or 14 timestamps)
    * `### TARGET LINE`: The 15th chunk to be classified. (Or the 15-th timestamp)
* **`label / output`**: Formatted as `{timestamp}, {class} event`.

### ✂️ Rules of Truncation
Raw terminal logs (like `apt-get` installations) can easily overflow an LLM's 
context window. To prevent this, the data engineering pipeline applies a 
strict **Two-Phase Truncation** rule:

#### Phase 1: Intra-Chunk Truncation (Line Limit)
If a single `<system_output>` block contains more than 15 lines of text, it 
is sliced. The first 5 lines and the last 5 lines are preserved, and the 
middle is replaced with a marker: `... [TRUNCATED X LINES] ...`. Note 
that `<user_input>` tags are **never** truncated to preserve 
human-interaction signals.

#### Phase 2: Window-Level Compression (Context Limit)
If the entire 14-chunk context window exceeds 25 total lines, the window 
is compressed:
* The **5 oldest chunks** and the **5 most recent chunks** are kept 
  fully intact.
* For the chunks in the **middle**, the text is completely stripped out, 
  leaving only the XML tags (e.g., `<system_output timestamp="X">... 
  [TRUNCATED TO SAVE SPACE] ...</system_output>`).
* This preserves the chronological timeline and sequence of events 
  without bloating the token count.

### ⚖️ Data Sampling & Balancing
In a typical terminal log, over 95% of the lines are "Old Events," which 
would lead the model to simply guess the majority class. To force actual 
learning, this dataset uses **Negative Downsampling**:

* **New Events (Positives):** 100% of detected boundaries are kept.
* **Old Events (Negatives):** Downsampled so that there is exactly a 
  **2:1 ratio** (Two old events for every one new event).

#### Hard Negative Mining
When selecting which "Old Events" to keep for the 2:1 ratio, the algorithm 
prioritizes **Hard Negatives**. Specifically, it targets `<user_input>` 
tags that contain a newline character (`\n`). This teaches the model the 
difficult lesson that a user pressing "Enter" is often just a completion 
of an input phase, not necessarily a new logical event.

#### Example Data Row
```json
{
  "instruction": "Your task is to analyze terminal XML logs...",
  "input": "### CONTEXT (Previous Events):\n<system_output timestamp=\"10.01\">demo@server:~$ apt update</system_output>\n<system_output timestamp=\"10.05\">Reading lists...</system_output>\n\n### TARGET LINE:\n<user_input timestamp=\"12.40\">s</user_input>",
  "output": "12.40, old event"
}