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Young Pauline is left a lot of money when her wealthy uncle dies. However, her uncle's secretary has been named as her guardian until she marries, at which time she will officially take ...
199
[ "Action" ]
Young Pauline is left a lot of money when her wealthy uncle dies. However, her uncle's secretary has been named as her guardian until she marries, at which time she will officially take possession of her inheritance. Meanwhile, her "guardian" and his confederates constantly come up with schemes to get rid of Pauline so that he can get his hands on the money himself.
[ "Louis J. Gasnier", "Donald MacKenzie" ]
[ "Charles W. Goddard (screenplay)", "Basil Dickey (screenplay)", "Charles W. Goddard (novel)", "George B. Seitz", "Bertram Millhauser" ]
[ "USA" ]
https://m.media-amazon.c…00_SX677_AL_.jpg
[ "English" ]
[ "Pearl White", "Crane Wilbur", "Paul Panzer", "Edward Josè" ]
The Perils of Pauline
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{ "id": 4465, "rating": 7.6, "votes": 744 }
{ "nominations": 0, "text": "1 win.", "wins": 1 }
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-0.0019981442, 0.004042261, -0.01163094 ]
A penniless young man tries to save an heiress from kidnappers and help her secure her inheritance.
22
[ "Comedy", "Short", "Action" ]
As a penniless man worries about how he will manage to eat, he is joined by a young waif and her dog, who are in the same predicament. Meanwhile, across town a dishonest lawyer is working with a gang of criminals, trying to swindle an innocent young heiress out of her inheritance. As the heiress is on her way home from the lawyer's office, she notices the young man and the waif in the midst of their latest problem with the authorities, and she rescues them. Later on, the young man will have an unexpected opportunity to repay her for her kindness.
[ "Alfred J. Goulding", "Hal Roach" ]
[ "H.M. Walker (titles)" ]
[ "USA" ]
https://m.media-amazon.c…00_SX677_AL_.jpg
[ "English" ]
[ "Harold Lloyd", "Mildred Davis", "'Snub' Pollard", "Peggy Cartwright" ]
From Hand to Mouth
0
TV-G
{ "id": 10146, "rating": 7, "votes": 639 }
{ "nominations": 1, "text": "1 nomination.", "wins": 0 }
movie
null
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Michael "Beau" Geste leaves England in disgrace and joins the infamous French Foreign Legion. He is reunited with his two brothers in North Africa, where they face greater danger from their...
101
[ "Action", "Adventure", "Drama" ]
Michael "Beau" Geste leaves England in disgrace and joins the infamous French Foreign Legion. He is reunited with his two brothers in North Africa, where they face greater danger from their own sadistic commander than from the rebellious Arabs.
[ "Herbert Brenon" ]
[ "Herbert Brenon (adaptation)", "John Russell (adaptation)", "Paul Schofield", "Percival Christopher Wren (novel)" ]
[ "USA" ]
null
[ "English" ]
[ "Ronald Colman", "Neil Hamilton", "Ralph Forbes", "Alice Joyce" ]
Beau Geste
0
null
{ "id": 16634, "rating": 6.9, "votes": 222 }
{ "nominations": 0, "text": "1 win.", "wins": 1 }
movie
null
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-0.02588836, 0.017952265, 0.011513298 ]
Seeking revenge, an athletic young man joins the pirate band responsible for his father's death.
88
[ "Adventure", "Action" ]
A nobleman vows to avenge the death of his father at the hands of pirates. To this end he infiltrates the pirate band. Acting in character he is instrumental in the capture of a ship, but things are complicated when he finds that there is a young woman on board whom he wishes to protect from the threat of rape.
[ "Albert Parker" ]
[ "Douglas Fairbanks (story)", "Jack Cunningham (adapted by)" ]
[ "USA" ]
https://m.media-amazon.c…00_SX677_AL_.jpg
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[ "Billie Dove", "Tempe Pigott", "Donald Crisp", "Sam De Grasse" ]
The Black Pirate
1
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{ "id": 16654, "rating": 7.2, "votes": 1146 }
{ "nominations": 0, "text": "1 win.", "wins": 1 }
movie
null
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An irresponsible young millionaire changes his tune when he falls for the daughter of a downtown minister.
58
[ "Action", "Comedy", "Romance" ]
The Uptown Boy, J. Harold Manners (Lloyd) is a millionaire playboy who falls for the Downtown Girl, Hope (Ralston) who works in Brother Paul's (Weigel) mission. In order to build up attendance, and win Hope's attention, Harold runs through town causing trouble, and winds up with a crowd chasing him right into the mission. He eventually wins the girl and they marry, but not without some interference from his high-brow friends.
[ "Sam Taylor" ]
[ "Ted Wilde (story)", "John Grey (story)", "Clyde Bruckman (story)", "Ralph Spence (titles)" ]
[ "USA" ]
https://m.media-amazon.c…00_SX677_AL_.jpg
[ "English" ]
[ "Harold Lloyd", "Jobyna Ralston", "Noah Young", "Jim Mason" ]
For Heaven's Sake
0
PASSED
{ "id": 16895, "rating": 7.6, "votes": 918 }
{ "nominations": 1, "text": "1 nomination.", "wins": 0 }
movie
null
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-0.030100413, -0.043389425, 0.006911353, 0.017905476, -0.011634557, -0.009399707, -0.016010858 ]
Navy divers clear the torpedo tube of a sunken submarine.
77
[ "Action", "Drama" ]
Aboard the U.S. submarine S13 in the China seas, Chief Torpedoman Burke goes about his duties. In actuality, he is Quartermaine, the infamous former commander of the British ship Royal Scot, which was sunk by Germans with a Field Marshal aboard. Quartermaine had told his sweetheart that the Field Marshal would be aboard, not knowing that she was an informant for the enemy. When the S13 sinks, Burke takes charge when the commander, Ensign Price, is unable to command. Burke must keep his mates alive long enough on the bottom of the sea for rescuers to arrive.
[ "John Ford" ]
[ "John Ford (story)", "James Kevin McGuinness (story)", "Dudley Nichols (screen play and scenario)", "Otis C. Freeman (titles)" ]
[ "USA" ]
null
[ "English" ]
[ "Kenneth MacKenna", "Frank Albertson", "J. Farrell MacDonald", "Warren Hymer" ]
Men Without Women
1
PASSED
{ "id": 21140, "rating": 5.8, "votes": 154 }
{ "nominations": 0, "text": "1 win.", "wins": 1 }
movie
null
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0.033587113, -0.01809855, -0.018889455, 0.016042197, -0.014144024, -0.003506347, 0.0016378331, 0.012713803 ]
Famous motor-racing champion Joe Greer returns to his hometown to compete in a local race. He discovers his younger brother has aspirations to become a racing champion and during the race ...
85
[ "Drama", "Action" ]
Famous motor-racing champion Joe Greer returns to his hometown to compete in a local race. He discovers his younger brother has aspirations to become a racing champion and during the race Joe loses his nerve when another driver his killed, leaving his brother to win. Joe's luck takes a plunge while his brother rises to height of fame.
[ "Howard Hawks" ]
[ "John Bright", "Niven Busch", "Kubec Glasmon", "Howard Hawks (story)", "Seton I. Miller" ]
[ "USA" ]
https://m.media-amazon.c…00_SX677_AL_.jpg
[ "English" ]
[ "James Cagney", "Joan Blondell", "Ann Dvorak", "Eric Linden" ]
The Crowd Roars
1
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{ "id": 22792, "rating": 6.4, "votes": 663 }
{ "nominations": 1, "text": "1 nomination.", "wins": 0 }
movie
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An ambitious and near insanely violent gangster climbs the ladder of success in the mob, but his weaknesses prove to be his downfall.
93
[ "Action", "Crime", "Drama" ]
Johnny Lovo rises to the head of the bootlegging crime syndicate on the south side of Chicago following the murder of former head, Big Louis Costillo. Johnny contracted Big Louis' bodyguard, Tony Camonte, to make the hit on his boss. Tony becomes Johnny's second in command. Johnny is not averse to killing anyone who gets in his and Johnny's way. As Tony is thinking bigger than Johnny and is not afraid of anyone or anything, Tony increasingly makes decisions on his own instead of following Johnny's orders, especially in not treading on the north side run by an Irish gang led by a man named O'Hara, of whom Johnny is afraid. Tony's murder spree increases, he taking out anyone who stands in his and Johnny's way of absolute control on the south side, and in Tony's view absolute control of the entire city. Tony's actions place an unspoken strain between Tony and Johnny to the point of the two knowing that they can't exist in their idealized world with the other. Tony's ultimate downfall may be one of two women in his life: Poppy, Johnny's girlfriend to who Tony is attracted; and Tony's eighteen year old sister, Cesca, who is self-professed to be older mentally than her years much to Tony's chagrin, he who will do anything to protect her innocence. Cesca ultimately comes to the realization that she is a lot more similar to her brother than she first imagined.
[ "Howard Hawks", "Richard Rosson" ]
[ "Armitage Trail (novel)", "Ben Hecht (screen story)", "Seton I. Miller (continuity)", "John Lee Mahin (continuity)", "W.R. Burnett (continuity)", "Seton I. Miller (dialogue)", "John Lee Mahin (dialogue)", "W.R. Burnett (dialogue)" ]
[ "USA" ]
https://m.media-amazon.c…00_SX677_AL_.jpg
[ "English" ]
[ "Paul Muni", "Ann Dvorak", "Karen Morley", "Osgood Perkins" ]
Scarface
1
PASSED
{ "id": 23427, "rating": 7.8, "votes": 18334 }
{ "nominations": 0, "text": "2 wins.", "wins": 2 }
movie
null
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A trader and his daughter set off in search of the fabled graveyard of the elephants in deepest Africa, only to encounter a wild man raised by apes.
100
[ "Action", "Adventure", "Romance" ]
James Parker and Harry Holt are on an expedition in Africa in search of the elephant burial grounds that will provide enough ivory to make them rich. Parker's beautiful young daughter Jane arrives unexpectedly to join them. Harry is obviously attracted to Jane and he does his best to help protect her from all the dangers that they experience in the jungle. Jane is terrified when Tarzan and his ape friends first abduct her, but when she returns to her father's expedition she has second thoughts about leaving Tarzan. After the expedition is captured by a tribe of violent dwarfs, Jane sends Cheetah to bring Tarzan to rescue them...
[ "W.S. Van Dyke" ]
[ "Edgar Rice Burroughs (based upon the characters created by)", "Cyril Hume (adaptation)", "Ivor Novello (dialogue)" ]
[ "USA" ]
https://m.media-amazon.c…00_SX677_AL_.jpg
[ "English" ]
[ "Johnny Weissmuller", "Neil Hamilton", "C. Aubrey Smith", "Maureen O'Sullivan" ]
Tarzan the Ape Man
0
PASSED
{ "id": 23551, "rating": 7.2, "votes": 5182 }
{ "nominations": 0, "text": "1 win.", "wins": 1 }
movie
null
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"When earthy Dolly Portland is rejected by Captain Gaskell in favor of a socialite, she aids Jamesy (...TRUNCATED)
87
[ "Action", "Drama", "Adventure" ]
"Dynamic Alan Gaskell captains a ship bound from Hong Kong to Singapore. Gaskell tries to turn over (...TRUNCATED)
[ "Tay Garnett" ]
["Jules Furthman (screen play)","James Kevin McGuinness (screen play)","Crosbie Garstin (from the bo(...TRUNCATED)
[ "USA" ]
https://m.media-amazon.c…00_SX677_AL_.jpg
[ "English" ]
[ "Clark Gable", "Jean Harlow", "Wallace Beery", "Lewis Stone" ]
China Seas
2
PASSED
{ "id": 26205, "rating": 7, "votes": 1518 }
{ "nominations": 1, "text": "1 nomination.", "wins": 0 }
movie
null
[-0.027133638,-0.01719291,0.012725786,-0.021825481,-0.04056258,0.021825481,0.0008858416,0.0110299345(...TRUNCATED)

sample_mflix.embedded_movies

This data set contains details on movies with genres of Western, Action, or Fantasy. Each document contains a single movie, and information such as its title, release year, and cast.

In addition, documents in this collection include a plot_embedding field that contains embeddings created using OpenAI's text-embedding-ada-002 embedding model that you can use with the Atlas Search vector search feature.

Overview

This dataset offers a comprehensive collection of data on various movies. It includes details such as plot summaries, genres, runtime, ratings, cast, and more. This dataset is ideal for movie recommendation systems, film analysis, and educational purposes in film studies.

Dataset Structure

Each record in the dataset represents a movie and includes the following fields:

  • _id: A unique identifier for the movie.
  • plot: A brief summary of the movie's plot.
  • genres: A list of genres associated with the movie.
  • runtime: The runtime of the movie in minutes.
  • rated: The MPAA rating of the movie.
  • cast: A list of main actors in the movie.
  • num_mflix_comments: The number of comments on the movie in the mflix platform.
  • poster: A URL to the movie's poster image.
  • title: The title of the movie.
  • lastupdated: The last date and time when the movie information was updated.
  • languages: The languages available in the movie.
  • directors: A list of directors of the movie.
  • writers: A list of writers of the movie.
  • awards: Information about awards won and nominations.
  • imdb: IMDb rating, votes, and ID.
  • countries: A list of countries where the movie was produced.
  • type: The type of record, in this case, movie.
  • tomatoes: Ratings and reviews from Rotten Tomatoes.
  • plot_embedding: An array of numerical values representing the plot embedding.

Field Details

Awards Object

  • wins: The number of awards won.
  • nominations: The number of awards the movie was nominated for.
  • text: A text summary of the awards and nominations.

IMDb Object

  • rating: The IMDb rating.
  • votes: The number of votes on IMDb.
  • id: The IMDb ID of the movie.

Tomatoes Object

  • Contains viewer and critic ratings, reviews count, DVD release date, and production details.

Plot Embedding

  • An array representing a numerical embedding of the movie's plot. Useful for machine learning applications, like content-based filtering in recommendation systems.

Usage

The dataset is suited for a range of applications, including:

  • Analyzing trends in film genres and ratings over time.
  • Building movie recommendation engines using plot embeddings and genres.
  • Studying the correlation between cast/directors and movie success.
  • Educational purposes in film studies and data analysis courses.

Notes

  • The data is provided as-is and intended for informational and educational purposes.
  • Users should verify the accuracy of the information for any critical use-cases.

Sample Document

{
  "_id": {
    "$oid": "573a1396f29313caabce582d"
  },
  "plot": "A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.",
  "genres": ["Action", "Adventure", "Comedy"],
  "runtime": {
    "$numberInt": "106"
  },
  "rated": "PG",
  "cast": ["Oliver Reed", "Raquel Welch", "Richard Chamberlain", "Michael York"],
  "num_mflix_comments": {
    "$numberInt": "0"
  },
  "poster": "https://m.media-amazon.com/images/M/MV5BODQwNmI0MDctYzA5Yy00NmJkLWIxNGMtYzgyMDBjMTU0N2IyXkEyXkFqcGdeQXVyMjI4MjA5MzA@._V1_SY1000_SX677_AL_.jpg",
  "title": "The Three Musketeers",
  "lastupdated": "2015-09-16 06:21:07.210000000",
  "languages": ["English"],
  "directors": ["Richard Lester"],
  "writers": ["George MacDonald Fraser (screenplay)", "Alexandre Dumas père (novel)"],
  "awards": {
    "wins": {
      "$numberInt": "4"
    },
    "nominations": {
      "$numberInt": "7"
    },
    "text": "Won 1 Golden Globe. Another 3 wins & 7 nominations."
  },
  "imdb": {
    "rating": {
      "$numberDouble": "7.3"
    },
    "votes": {
      "$numberInt": "11502"
    },
    "id": {
      "$numberInt": "72281"
    }
  },
  "countries": ["Spain", "USA", "Panama", "UK"],
  "type": "movie",
  "tomatoes": {
    "viewer": {
      "rating": {
        "$numberDouble": "3.5"
      },
      "numReviews": {
        "$numberInt": "9600"
      },
      "meter": {
        "$numberInt": "78"
      }
    },
    "dvd": {
      "$date": {
        "$numberLong": "982022400000"
      }
    },
    "critic": {
      "rating": {
        "$numberDouble": "7.1"
      },
      "numReviews": {
        "$numberInt": "11"
      },
      "meter": {
        "$numberInt": "82"
      }
    },
    "lastUpdated": {
      "$date": {
        "$numberLong": "1441307415000"
      }
    },
    "rotten": {
      "$numberInt": "2"
    },
    "production": "Live Home Video",
    "fresh": {
      "$numberInt": "9"
    }
  },
  "plot_embedding": [
    -0.004237316,
    -0.022958077,
    -0.005921211,
    -0.020323543,
    0.010051459
  ]
}

Ingest Data

The small script ingest.py can be used to load the data into your MongoDB Atlas cluster.

pip install pymongo
pip install datasets
## export MONGODB_ATLAS_URI=<your atlas uri>

The ingest.py:

import os
from pymongo import MongoClient
import datasets
from datasets import load_dataset
from bson import json_util


uri = os.environ.get('MONGODB_ATLAS_URI')
client = MongoClient(uri)
db_name = 'sample_mflix'
collection_name = 'embedded_movies'

embedded_movies_collection = client[db_name][collection_name]

dataset = load_dataset("MongoDB/embedded_movies")

insert_data = []

for movie in dataset['train']:
    doc_movie = json_util.loads(json_util.dumps(movie))
    insert_data.append(doc_movie)

    if len(insert_data) == 1000:
        embedded_movies_collection.insert_many(insert_data)
        print("1000 records ingested")
        insert_data = []

if len(insert_data) > 0:
    embedded_movies_collection.insert_many(insert_data)
    insert_data = []

print("Data Ingested")
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