README.md

---
license: mit
language:
- en
tags:
- conversations
- tagging
- embeddings
- bittensor
pretty_name: 5,000 Podcast Conversations with Metadata and Embedding Dataset
size_categories:
- 1M<n<10M
---
## 🗂️ ReadyAI - 5,000 Podcast Conversations with Metadata and Embedding Dataset

ReadyAI, operating subnet 33 on the [Bittensor Network](https://bittensor.com/) 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).

---

## Full Vectors Access

➡️ **Download the full 45 GB conversation tags embeddings** from [here](https://huggingface.co/datasets/ReadyAi/5000-podcast-conversations-with-metadata-and-embedding-dataset/tree/main/data)

For large-scale processing and fine-tuning.

---

## 📋 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:
- Semantic search over conversations
- AI assistant training (OpenAI models, fine-tuning)
- Vector search implementations using **pg_vector** and **Pinecone**
- Metadata analysis and tag retrieval for LLMs

The embeddings were generated with the [text-embedding-ada-002](https://huggingface.co/Xenova/text-embedding-ada-002) model and have 1536 dimensions per tag.
---

## 📂 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_train.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_train.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

dataset = load_dataset(
    "ReadyAi/5000-podcast-conversations-with-metadata-and-embedding-dataset",
    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_train.parquet | Full multi-turn dialogue with participant metadata |