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---
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).
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