|
|
| ### 1. Structural Comparison (Identical 🟢) |
| |
| • Format: Both are JSONL files formatted for SFT (Supervised Fine-Tuning) using the standard Aider multi-turn format. |
| • Top-Level Keys: Both files contain identical key schemas: ['label', 'messages', 'metadata', 'task_id']. |
| • Conversation Structure: |
| • Exactly 9 messages per record (turns 0–8). |
| • Turn 0 (System): Identical Aider system prompt ("Act as an expert software developer..."). |
| • Turns 1–6 (Few-shot context): Identical fixed 3-turn dummy conversation (show_greeting.py example setup) across all samples in both files. |
| • Turn 7 (User Prompt): Main C++ task instruction (# Introduction, # Instructions). |
| • Turn 8 (Assistant Response): Ground-truth implementation header file (.h). |
| |
| ────── |
| ### 2. Quality-Wise Comparison (Identical Quality Grade 🟢) |
| |
| • Both datasets adhere to strict C++ standard library code standards (using STL headers, namespaces, clean contracts, #pragma once or #ifndef guards). |
| • Prompt quality is consistent across both files: detailed specification documents explaining the domain requirements followed by exact method/class signatures |
| to implement. |
| • Code responses are well-structured, syntax-valid C++ header definitions. |
| ────── |
| ### 3. Content & Domain Comparison (Minor Variations 🟡) |
| |
| Dimension │ sft-v6-2000.jsonl │ train_first500_transformed_v6.jsonl |
| ──────────────────┼──────────────────────────────────────────────────────────────────────┼─────────────────────────────────────────────────────────────────────── |
| Total Samples │ 1,997 samples │ 500 samples |
| Domain Breakdown │ Highly diverse Synthetic C++ tasks across multiple families (avl-*, │ Focused heavily on temporal/time-domain C++ algorithms (temporal-*, |
| │ f26*, exercism, search-*, daily-*) │ f26*) |
| Response Headers │ Uses traditional #ifndef UNIQUE_GUARD_H guards and standard │ Uses // Generated contract for ... comments with #pragma once and |
| │ formatting │ #include <algorithm> headers |
| Metadata Depth │ Rich metadata dictionary (33 fields: difficulty, mutation_score, │ Streamlined metadata dictionary (4 fields: purpose, subset, task_id, |
| │ verified_reference, token_count, verification_receipt_sha256, etc.) │ prompt_format) |
| ────── |
| ### Summary Conclusion |
| |
| • Structurally: 100% Identical. |
| • Quality-wise: 100% Identical. |
| • Content-wise: Both belong to the same V6 synthetic C++ training pipeline, where train_first500_transformed_v6.jsonl is a 500-sample slice focused on specific |
| domain task families (mostly temporal-* arithmetic & dates), while sft-v6-2000.jsonl is a larger 2,000-sample batch covering a broader range of data structure |
| families (e.g. avl-tree, f26, exercism). |
| |
| ──────────────────────────────────────────────────────────── |
| > can you give a good .md content and paste it here in text |
|
|
| Here is the detailed Markdown summary comparing both datasets: |
| |
| # Comprehensive Comparison Report: Dataset Similarity Analysis |
| |
| ## Executive Summary |
| This document provides a detailed comparative analysis between two Supervised Fine-Tuning (SFT) datasets: |
| 1. `sft-v6-2000.jsonl` (1,997 records) |
| 2. `train_first500_transformed_v6.jsonl` (500 records) |
| |
| The analysis covers three primary dimensions: **Structure**, **Quality**, and **Content/Domain**. |
| |
| --- |
| |
| ## 1. Structural Comparison 🟢 (Identical) |
| |
| Both datasets share an identical data schema and multi-turn Aider conversation layout. |
| |
| | Structural Property | `sft-v6-2000.jsonl` | `train_first500_transformed_v6.jsonl` | Match Status | |
| | :--- | :--- | :--- | :--- | |
| | **File Format** | JSONL | JSONL | ✅ Identical | |
| | **Top-Level Keys** | `['label', 'messages', 'metadata', 'task_id']` | `['label', 'messages', 'metadata', 'task_id']` | ✅ Identical | |
| | **Message Count per Record** | 9 turns (Turn 0 to Turn 8) | 9 turns (Turn 0 to Turn 8) | ✅ Identical | |
| | **Turn 0 (System Prompt)** | Standard Aider Developer System Prompt | Standard Aider Developer System Prompt | ✅ Identical | |
| | **Turns 1–6 (Few-Shot Context)**| Fixed 3-turn setup (`show_greeting.py`) | Fixed 3-turn setup (`show_greeting.py`) | ✅ Identical | |
| | **Turn 7 (User Input)** | Task Specification (`# Introduction`, `# Instructions`)| Task Specification (`# Introduction`, `# Instructions`)| ✅ Identical | |
| | **Turn 8 (Assistant)** | C++ Header (`.h`) implementation | C++ Header (`.h`) implementation | ✅ Identical | |
| |
| --- |
| |
| ## 2. Quality-Wise Comparison 🟢 (Equivalent) |
| |
| - **Instruction Quality**: Both datasets feature comprehensive problem statements with background domain explanations, exact boundary constraints, and function |
| contract requirements. |
| - **Code Standards**: Both datasets output clean, idiomatic C++ header implementations using standard library features (`<cstddef>`, `<optional>`, `<vector>`, |
| `<algorithm>`, etc.). |
| - **Consistency**: Both datasets adhere strictly to valid syntax without structural truncation or missing turns. |
| |
| --- |
| |
| ## 3. Content & Metadata Comparison 🟡 (Minor Differences) |
| |
| While structurally identical, the datasets differ in scale, domain distribution, and metadata granularity. |
| |
| ```mermaid |
| graph TD |
| A[V6 Synthetic Dataset Pipeline] --> B[sft-v6-2000.jsonl] |
| A --> C[train_first500_transformed_v6.jsonl] |
| |
| B --> B1[1,997 Samples] |
| B --> B2[Broad C++ Domains: AVL Trees, Ring Buffers, Search Algorithms] |
| B --> B3[Full 33-key Metadata] |
| |
| C --> C1[500 Samples] |
| C --> C2[Focused C++ Domains: Temporal & Date Arithmetic] |
| C --> C3[Streamlined 4-key Metadata] |
| |
| ### Detailed Breakdown |
| |
| 1. Dataset Volume: |
| • sft-v6-2000.jsonl: 1,997 samples |
| • train_first500_transformed_v6.jsonl: 500 samples |
| 2. Domain Focus: |
| • sft-v6-2000.jsonl: High domain variance (avl-tree, ring-buffer, exercism, search-shape, etc.). |
| • train_first500_transformed_v6.jsonl: Highly concentrated on time and arithmetic domains (temporal-actuarial, temporal-affine-map, temporal-age-band, f26 |
| benchmarks). |
| 3. Metadata Granularity: |
| • sft-v6-2000.jsonl includes 33 metadata attributes (e.g., mutation_score, verification_receipt_sha256, verified_reference, difficulty, token_count). |
| • train_first500_transformed_v6.jsonl contains 4 core metadata attributes (purpose, subset, task_id, prompt_format). |
| |
| ────── |
| ## Conclusion |
| |
| Both files originate from the same generation pipeline architecture (v6 SFT C++ schema). train_first500_transformed_v6.jsonl serves as a smaller, domain-specific |
| subset, whereas sft-v6-2000.jsonl represents a larger, more diverse training set with full evaluation metadata. |
| |
| |
|
|
| license: mit |
|
|
