license: mit
language:
- en
tags:
- conversations
- tagging
- embeddings
- bittensor
- dialog
- social media
- podcast
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 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.
Full Vectors Access
➡️ Download the full 45 GB conversation tags embeddings from here
For large-scale processing and fine-tuning.
📦 Dataset Versions
In addition to the full dataset, two smaller versions are available:
Small version
- Located in the
small_datasetfolder. - Contains 1,000 conversations with the same file structure as the full dataset.
- All filenames are prefixed with
small_.
- Located in the
Medium version
- Located in the
medium_datasetfolder. - Contains 2,500 conversations
- Also using the same structure and
medium_prefix for all files.
- Located in the
These subsets are ideal for lightweight experimentation, prototyping, or benchmarking.
📋 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 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 datasets
Download the dataset
import datasets
path = "ReadyAi/5000-podcast-conversations-with-metadata-and-embedding-dataset"
dataset = datasets.load_dataset(path)
print(dataset['train'].column_names)
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
✨ 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 |