---
dataset_info:
  features:
  - name: runtime
    dtype: float64
  - name: languages
    sequence: string
  - name: metacritic
    dtype: float64
  - name: type
    dtype: string
  - name: imdb
    struct:
    - name: id
      dtype: int64
    - name: rating
      dtype: float64
    - name: votes
      dtype: int64
  - name: genres
    sequence: string
  - name: awards
    struct:
    - name: nominations
      dtype: int64
    - name: text
      dtype: string
    - name: wins
      dtype: int64
  - name: rated
    dtype: string
  - name: cast
    sequence: string
  - name: directors
    sequence: string
  - name: fullplot
    dtype: string
  - name: title
    dtype: string
  - name: num_mflix_comments
    dtype: int64
  - name: countries
    sequence: string
  - name: plot
    dtype: string
  - name: poster
    dtype: string
  - name: writers
    sequence: string
  - name: plot_embedding
    sequence: float64
  splits:
  - name: train
    num_bytes: 13657438
    num_examples: 1452
  download_size: 10625884
  dataset_size: 13657438
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

This dataset originates from MongoDB's [embedded_movies dataset](https://huggingface.co/datasets/MongoDB/embedded_movies) and contains details on movies 
from different genres. Each row represents a single movie with detailed information. 

As opposed to the original dataset, this one includes embeddings of the fullplot column using the open source [General Text Embeddings model](https://huggingface.co/thenlper/gte-large) 
instead of OpenAI's text-embedding-ada-002 embedding model used in MongoDB Atlas. 

Those open source embeddings are also used in [Hermes](https://github.com/chrisonntag/hermes).