Update README.md

README.md

@@ -21,43 +21,33 @@ The dataset is provided in `JSONL` format, mapped specifically for conversationa
   "input": "### CONTEXT (Previous Events):\n<system_output timestamp=\"10.01\">demo@server:~$ apt update</system_output>\n<system_output timestamp=\"10.05\">Reading package lists...</system_output>\n\n### TARGET LINE:\n<user_input timestamp=\"12.40\">s</user_input>",
   "output": "12.40, old 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 steps"), 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 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.
+### 🎯 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 steps"), 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").
 
-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" (continuous output or typing), which would lead the model to simply guess the majority class. To force actual learning, this dataset uses Negative Downsampling:
+### ✂️ 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:
 
-New Events (Positives): 100% of detected boundaries are kept.
+#### 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.
 
-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.
+#### 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.
 
-💻 How to Load the Dataset
-You can load this dataset directly in Python using the Hugging Face datasets library:
+---
 
-Python
-from datasets import load_dataset
+### ⚖️ 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**:
 
-# Load the dataset
-dataset = load_dataset("Jaiccc/model0_boundary_predict_streaming", split="train")
+* **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).
 
-# Optional: Split into training and validation
-split = dataset.train_test_split(test_size=0.1, seed=42)
-train_dataset = split["train"]
-eval_dataset = split["test"]
+#### 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.