Update README.md

README.md

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 # ## Dataset Card: Terminal Log Boundary Prediction (Streaming)
 
-### 📋 Dataset Summary
-This dataset is designed to train Large Language Models to detect 
-phase transitions, or **"boundaries,"** within continuous terminal XML logs.
+### 📋 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) 
 
-The dataset uses a **sliding-window approach**. Instead of reading a 
-massive log file at once, the model analyzes a short history of events 
-to determine if the **Target Line** (the final entry) marks a new 
-logical event or the continuation of an ongoing process.
+#### 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`.
 
-### 🎯 The Model's Goal
-The primary objective of the model is **binary classification of sequential data**. 
-By looking at the historical context (e.g., "The terminal has been downloading 
-packages for the last 14 timesteps"), the model must predict if the timestamp in 
-the Target Line breaks that pattern and establishes a new boundary (e.g., 
-"The download finished and the shell prompt returned").
-
 ### ✂️ 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 
@@ -58,7 +58,6 @@ is compressed:
 * 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