albertvillanova
HF staff
Convert dataset sizes from base 2 to base 10 in the dataset card (#1)
7d54593
metadata
language:
- en
paperswithcode_id: cornell-movie-dialogs-corpus
pretty_name: Cornell Movie-Dialogs Corpus
dataset_info:
features:
- name: movieID
dtype: string
- name: movieTitle
dtype: string
- name: movieYear
dtype: string
- name: movieIMDBRating
dtype: string
- name: movieNoIMDBVotes
dtype: string
- name: movieGenres
sequence: string
- name: characterID1
dtype: string
- name: characterID2
dtype: string
- name: characterName1
dtype: string
- name: characterName2
dtype: string
- name: utterance
sequence:
- name: text
dtype: string
- name: LineID
dtype: string
splits:
- name: train
num_bytes: 19548840
num_examples: 83097
download_size: 9916637
dataset_size: 19548840
Dataset Card for "cornell_movie_dialog"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 9.92 MB
- Size of the generated dataset: 19.55 MB
- Total amount of disk used: 29.46 MB
Dataset Summary
This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:
- 220,579 conversational exchanges between 10,292 pairs of movie characters
- involves 9,035 characters from 617 movies
- in total 304,713 utterances
- movie metadata included:
- genres
- release year
- IMDB rating
- number of IMDB votes
- IMDB rating
- character metadata included:
- gender (for 3,774 characters)
- position on movie credits (3,321 characters)
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 9.92 MB
- Size of the generated dataset: 19.55 MB
- Total amount of disk used: 29.46 MB
An example of 'train' looks as follows.
{
"characterID1": "u0 ",
"characterID2": " u2 ",
"characterName1": " m0 ",
"characterName2": " m0 ",
"movieGenres": ["comedy", "romance"],
"movieID": " m0 ",
"movieIMDBRating": " 6.90 ",
"movieNoIMDBVotes": " 62847 ",
"movieTitle": " f ",
"movieYear": " 1999 ",
"utterance": {
"LineID": ["L1"],
"text": ["L1 "]
}
}
Data Fields
The data fields are the same among all splits.
default
movieID
: astring
feature.movieTitle
: astring
feature.movieYear
: astring
feature.movieIMDBRating
: astring
feature.movieNoIMDBVotes
: astring
feature.movieGenres
: alist
ofstring
features.characterID1
: astring
feature.characterID2
: astring
feature.characterName1
: astring
feature.characterName2
: astring
feature.utterance
: a dictionary feature containing:text
: astring
feature.LineID
: astring
feature.
Data Splits
name | train |
---|---|
default | 83097 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@InProceedings{Danescu-Niculescu-Mizil+Lee:11a,
author={Cristian Danescu-Niculescu-Mizil and Lillian Lee},
title={Chameleons in imagined conversations:
A new approach to understanding coordination of linguistic style in dialogs.},
booktitle={Proceedings of the
Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011},
year={2011}
}
Contributions
Thanks to @mariamabarham, @patrickvonplaten, @thomwolf for adding this dataset.