| --- |
| 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" |
| } |
