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diff --git a/README.md b/README.md
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+---
+license: mit
+language:
+- en
+tags:
+- conversations
+- tagging
+- embeddings
+- bittensor
+pretty_name: Bittensor Conversational Tagging and Embedding
+size_categories:
+- 10M<n<100M
+---
+[![Hugging Face Dataset](https://img.shields.io/badge/Dataset-on%20Hugging%20Face-blue)](https://huggingface.co/datasets/your-username/readyai-bittensor-conversational-tags)
+
+## 🗂️ ReadyAI - Bittensor Conversational Tagging and Embedding Dataset
+
+ReadyAI is an open-source initiative focused on low-cost, resource-minimal pipelines for structuring raw data for AI applications.  
+This dataset is part of the ReadyAI Conversational Genome Project, leveraging the Bittensor decentralized network.
+
+AI runs on structured data — and this dataset bridges the gap between raw conversation transcripts and structured, vectorized semantic tags.
+
+You can find more about our subnet on GitHub [here](https://github.com/afterpartyai/bittensor-conversation-genome-project).
+
+---
+
+## 📋 Dataset Overview
+
+This dataset contains **annotated conversation transcripts** with:
+- Human-readable semantic tags
+- **Embedding vectors** contextualized to each conversation
+- Participant metadata
+
+It is ideal for:
+- Conversational AI training
+- Dialogue understanding research
+- Retrieval-augmented generation (RAG)
+- Semantic search
+- Fine-tuning large language models (LLMs)
+
+---
+
+## 📂 Dataset Structure
+
+The dataset consists of four main components:
+
+### 1. **data/bittensor-conversational-tags-and-embeddings-part-*.parquet** — Tag Embeddings and Metadata
+
+Each Parquet file contains rows with:
+
+
+| Column | Type | Description |
+|:-------|:-----|:------------|
+| c_guid | int64 | Unique conversation group ID |
+| tag_id | int64 | Unique identifier for the tag |
+| tag | string | Semantic tag (e.g., "climate change") |
+| vector | list of float32 | Embedding vector representing the tag's meaning **in the conversation's context** |
+
+
+✅ Files split into ~1GB chunks for efficient loading and streaming.
+
+---
+
+### 2. **tag_to_id.parquet** — Tag Mapping
+
+Mapping between tag IDs and human-readable tags.
+
+| Column | Type | Description |
+|:-------|:-----|:------------|
+| tag_id | int64 | Unique tag ID |
+| tag | string | Semantic tag text |
+
+✅ Useful for reverse-mapping tags from models or outputs.
+
+---
+
+### 3. **conversations_to_tags.parquet** — Conversation-to-Tag Mappings
+
+Links conversations to their associated semantic tags.
+
+| Column | Type | Description |
+|:-------|:-----|:------------|
+| c_guid | int64 | Conversation group ID |
+| tag_ids | list of int64 | List of tag IDs relevant to the conversation |
+
+✅ For supervised training, retrieval tasks, or semantic labeling.
+
+---
+
+### 4. **conversations.parquet** — Full Conversation Text and Participants
+
+Contains the raw multi-turn dialogue and metadata.
+
+| Column | Type | Description |
+|:-------|:-----|:------------|
+| c_guid | int64 | Conversation group ID |
+| transcript | string | Full conversation text |
+| participants | list of strings | List of speaker identifiers |
+
+✅ Useful for dialogue modeling, multi-speaker AI, or fine-tuning.
+
+---
+
+## 🚀 How to Use
+
+**Install dependencies**
+
+```
+pip install pandas pyarrow
+```
+
+**Load a single Parquet split**
+
+``` 
+import pandas as pd
+
+df = pd.read_parquet("data/bittensor-conversational-tags-and-embeddings-part-0000.parquet")
+print(df.head())
+```
+
+**Load all tag splits**
+
+``` 
+import pandas as pd
+import glob
+
+files = sorted(glob.glob("data/bittensor-conversational-tags-and-embeddings-part-*.parquet"))
+df_tags = pd.concat((pd.read_parquet(f) for f in files), ignore_index=True)
+
+print(f"Loaded {len(df_tags)} tag records.")
+```
+
+**Load tag dictionary**
+
+```
+tag_dict = pd.read_parquet("tag_to_id.parquet")
+print(tag_dict.head())
+```
+
+**Load conversation to tags mapping**
+
+``` 
+df_mapping = pd.read_parquet("conversations_to_tags.parquet")
+print(df_mapping.head())
+```
+
+**Load full conversations dialog and metadata**
+
+``` 
+df_conversations = pd.read_parquet("conversations.parquet")
+print(df_conversations.head())
+```
+
+---
+
+## 🔥 Example: Reconstruct Tags for a Conversation
+
+``` 
+# Build tag lookup
+tag_lookup = dict(zip(tag_dict['tag_id'], tag_dict['tag']))
+
+# Pick a conversation
+sample = df_mapping.iloc[0]
+c_guid = sample['c_guid']
+tag_ids = sample['tag_ids']
+
+# Translate tag IDs to human-readable tags
+tags = [tag_lookup.get(tid, "Unknown") for tid in tag_ids]
+
+print(f"Conversation {c_guid} has tags: {tags}")
+```
+
+---
+
+## 📦 Handling Split Files
+
+| Situation | Strategy |
+|:----------|:---------|
+| Enough RAM | Use `pd.concat()` to merge splits |
+| Low memory | Process each split one-by-one |
+| Hugging Face datasets | Use streaming mode |
+
+**Example (streaming with Hugging Face `datasets`)**
+
+```
+from datasets import load_dataset
+
+# Stream the dataset directly
+dataset = load_dataset(
+    "ReadyAi/bittensor-conversational-tags-and-embeddings",
+    split="train",
+    streaming=True
+)
+
+for example in dataset:
+    print(example)
+    break
+```
+
+---
+
+## 📜 License
+
+MIT License —  
+✅ Free to use and modify,  
+❌ Commercial redistribution without permission is prohibited.
+
+---
+
+## ✨ Credits
+
+Built using contributions from Bittensor conversational miners and the ReadyAI open-source community.
+
+---
+
+## 🎯 Summary
+
+| Component | Description |
+|:----------|:------------|
+| parquets/part_*.parquet | Semantic tags and their contextual embeddings |
+| tag_to_id.parquet | Dictionary mapping of tag IDs to text |
+| conversations_to_tags.parquet | Links conversations to tags |
+| conversations.parquet | Full multi-turn dialogue with participant metadata |
\ No newline at end of file
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