metadata
dataset_info:
features:
- name: system
dtype: string
- name: conversations
sequence:
- name: role
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 0
num_examples: 50
download_size: 0
dataset_size: 0
language:
- en
tags:
- bittensor
- flock
- consulting
- m&a
license: mit
Flock Dataset for Subnet 96 (M&A Consulting)
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.
It follows the JSONL structure required by Subnet 96 validators.
📂 Dataset Structure
Each entry in the dataset is a JSON object stored in a .jsonl file.
Format:
{
"system": "You are an expert M&A strategy consultant. Provide concise, bullet-point style answers.",
"conversations": [
{"role": "user", "content": "User input text"},
{"role": "assistant", "content": "Assistant response text"}
]
}
system → Defines the expert persona (M&A consultant).
user → The client’s question or request.
assistant → The consultant’s structured response.
🏗️ Example Entry
json
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{
"system": "You are an expert M&A strategy consultant. Provide concise, bullet-point style answers.",
"conversations": [
{
"role": "user",
"content": "What are key sales synergy levers for an integration/merger in the consumer goods industry?"
},
{
"role": "assistant",
"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"
}
]
}
✅ Requirements Compliance
Follows Subnet 96 JSONL format (system + conversations array).
Answers are structured in bullet points for validator readability.
Focused on M&A consulting (pre-deal and post-deal).
📊 Current Dataset Size
Entries: ~50 Q&A pairs (v1.0)
Format: JSONL (dataset_sn96.jsonl)
🚀 Usage
Loading with datasets library
python
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from datasets import load_dataset
dataset = load_dataset("neihtmahp/flock_dataset")
print(dataset["train"][0])
Example Output
python
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{
'system': 'You are an expert M&A strategy consultant. Provide concise, bullet-point style answers.',
'conversations': [
{'role': 'user', 'content': 'What are integration risks that are often underestimated?'},
{'role': 'assistant', 'content': '- Missing cross-functional alignment\n- Not sufficient time to apply user acceptance testing\n- Late sign-off from stakeholders'}
]
}
📌 Version History
v1.0 → Initial release with 50 curated Q&A entries.
Future versions will expand coverage of:
Commercial due diligence
IT due diligence
Post-merger integration
✨ Acknowledgements
This dataset was created for experimentation with Flock Subnet 96 mining and validation.
Contributions welcome!
---
license: mit
---