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README.md
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# Flock Dataset for Subnet 96 (M&A Consulting)
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This dataset is designed for use on **Bittensor Subnet 96 (Flock)** to train and evaluate models that generate high-quality consulting-style responses in the **Mergers & Acquisitions (M&A)** domain.
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It follows the JSONL structure required by Subnet 96 validators.
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---
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## 📂 Dataset Structure
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Each entry in the dataset is a JSON object stored in a `.jsonl` file.
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Format:
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```json
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{
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"system": "You are an expert M&A strategy consultant. Provide concise, bullet-point style answers.",
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"conversations": [
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{"role": "user", "content": "User input text"},
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{"role": "assistant", "content": "Assistant response text"}
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]
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}
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system → Defines the expert persona (M&A consultant).
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user → The client’s question or request.
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assistant → The consultant’s structured response.
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🏗️ Example Entry
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json
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Kopiëren
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Bewerken
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{
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"system": "You are an expert M&A strategy consultant. Provide concise, bullet-point style answers.",
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"conversations": [
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{
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"role": "user",
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"content": "What are key sales synergy levers for an integration/merger in the consumer goods industry?"
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},
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{
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"role": "assistant",
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"content": "- Improving margins by taking the most generous contractual terms\n- Consolidate sale volumes to bring more scale to the (yearly) negotiations\n- Apply cross selling by offering additional products to existing/new clients"
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}
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]
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}
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✅ Requirements Compliance
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Follows Subnet 96 JSONL format (system + conversations array).
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Answers are structured in bullet points for validator readability.
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Focused on M&A consulting (pre-deal and post-deal).
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📊 Current Dataset Size
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Entries: ~50 Q&A pairs (v1.0)
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Format: JSONL (dataset_sn96.jsonl)
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🚀 Usage
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Loading with datasets library
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python
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Kopiëren
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Bewerken
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from datasets import load_dataset
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dataset = load_dataset("neihtmahp/flock_dataset")
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print(dataset["train"][0])
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Example Output
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python
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Kopiëren
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Bewerken
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{
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'system': 'You are an expert M&A strategy consultant. Provide concise, bullet-point style answers.',
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'conversations': [
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{'role': 'user', 'content': 'What are integration risks that are often underestimated?'},
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{'role': 'assistant', 'content': '- Missing cross-functional alignment\n- Not sufficient time to apply user acceptance testing\n- Late sign-off from stakeholders'}
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]
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}
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📌 Version History
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v1.0 → Initial release with 50 curated Q&A entries.
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Future versions will expand coverage of:
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Commercial due diligence
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IT due diligence
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Post-merger integration
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✨ Acknowledgements
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This dataset was created for experimentation with Flock Subnet 96 mining and validation.
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Contributions welcome!
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---
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license: mit
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---
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