---
license: apache-2.0
tags:
- movies
- embeddings
- imbd
- nomic
- nomic-embed-text
- tmdb
- kaggle
- semantic-search
- project-ready
pretty_name: 'Embedding Enriched: The Ultimate 1Million Movies Dataset (TMDB + IMDb)'
size_categories:
- 1M<n<10M
---

# Dataset Card for Embedding Enriched `The Ultimate 1Million Movies Dataset (TMDB + IMDb)`

This dataset contains movie metadata from TMDB sourced from Kaggle, with an added layer of embeddings and token counts for semantic search and ML applications.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6444c7a59174daa2f6934a16/MhudcmbQmkND_Z6LE-is3.png)

## Dataset Details

### Dataset Description

A ~daily-updated~ snapshot taken in early 2025 of an existing movie metadata collection, sourced from [TMDB Movies Dataset 2025 on Kaggle](https://www.kaggle.com/datasets/alanvourch/tmdb-movies-daily-updates), enriched with 768-dimensional `nomic-embed-text` embeddings and token counts (using OpenAI tiktoken `cl100k_base` tokenizer on concatenated text fields), for context management purposes. 

No association with the original dataset creator, but I'm linking them below. I generated the embeddings for a personal project, which took a bit of time, so I'm sharing the result for anyone who finds it useful. 

- **Original Set Curated by:** Alan Vourch (Kaggle)
- **License:** Apache 2.0

### Dataset Sources

- **Repository:** https://www.kaggle.com/datasets/alanvourch/tmdb-movies-daily-updates
- **Embeddings Model:** Nomic Embed Text via Ollama [Link](https://ollama.com/library/nomic-embed-text)

## Uses

### Direct Use

- Semantic search and retrieval
- Movie recommendation systems
- ML model training for movie-related tasks

### Out-of-Scope Use

- Any use violating TMDB or Kaggle terms of service
- Use as ground truth for sensitive or demographic analysis without independent validation. 

## Dataset Structure

- **Fields:** Standard TMDB movie metadata (title, release_date, genres, etc.)
- **title_tagline_overview:** Concatenated text used for embedding and tokenization.
- **embedding:** 768-dimension vector, stored as a stringified list (e.g., `"[0.1, 0.2, ...]"`).
- **token_count:** Integer, calculated using OpenAI tiktoken `cl100k_base` tokenizer on the concatenated text fields.

## Dataset Creation

### Curation Rationale

To enable semantic search, recommendations, and ML research on a large, up-to-date movie dataset with ready-to-use embeddings and token counts.

### Source Data

#### Data Collection and Processing

- Downloaded from Kaggle (see above).
- Embeddings generated using `nomic-embed-text:latest` via Ollama on concatenated text fields.
- Token counts calculated using OpenAI tiktoken `cl100k_base` tokenizer (not the nomic-embed tokenizer, was added to follow a context management convention)

#### Who are the source data producers?

- Original movie data: The Movie Database (TMDB) and contributors.
- Embeddings and token counts: Jeremy Braun (Remsky)

## Bias, Risks, and Limitations

- The dataset inherits any biases present in TMDB and the embedding/tokenizer models.
- Embeddings are stored as strings; conversion to array type is recommended for downstream ML and will be included in an update soon
- Note: The majority are embedded with the genre appended to the end of the text description as well, though the initial 10-15% or so this wasn't included. Will aim to update for consistency, but it hasn't caused significant issues for my current use

### Recommendations

Users should be aware of potential biases in both the source data and the embedding model. For ML use, convert the embedding column to a numeric array as needed, or insert to pgvector