metadata

language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: MIT Movie

Dataset Card for "tner/mit_movie_trivia"

Dataset Description

Repository: T-NER
Dataset: MIT Movie
Domain: Movie
Number of Entity: 12

Dataset Summary

MIT Movie NER dataset formatted in a part of TNER project.

Entity Types: Actor, Plot, Opinion, Award, Year, Genre, Origin, Director, Soundtrack, Relationship, Character_Name, Quote

Dataset Structure

Data Instances

An example of train looks as follows.

{
    'tags': [0, 13, 14, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4],
    'tokens': ['a', 'steven', 'spielberg', 'film', 'featuring', 'a', 'bluff', 'called', 'devil', 's', 'tower', 'and', 'a', 'spectacular', 'mothership']
}

Label ID

The label2id dictionary can be found at here.

{
    "O": 0,
    "B-Actor": 1,
    "I-Actor": 2,
    "B-Plot": 3,
    "I-Plot": 4,
    "B-Opinion": 5,
    "I-Opinion": 6,
    "B-Award": 7,
    "I-Award": 8,
    "B-Year": 9,
    "B-Genre": 10,
    "B-Origin": 11,
    "I-Origin": 12,
    "B-Director": 13,
    "I-Director": 14,
    "I-Genre": 15,
    "I-Year": 16,
    "B-Soundtrack": 17,
    "I-Soundtrack": 18,
    "B-Relationship": 19,
    "I-Relationship": 20,
    "B-Character_Name": 21,
    "I-Character_Name": 22,
    "B-Quote": 23,
    "I-Quote": 24
}

Data Splits

name	train	validation	test
mit_movie_trivia	6816	1000	1953