Datasets:
metadata
languages:
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paperswithcode_id: empatheticdialogues
Dataset Card for "empathetic_dialogues"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/facebookresearch/EmpatheticDialogues
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 26.72 MB
- Size of the generated dataset: 23.97 MB
- Total amount of disk used: 50.69 MB
Dataset Summary
PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
Supported Tasks and Leaderboards
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
default
- Size of downloaded dataset files: 26.72 MB
- Size of the generated dataset: 23.97 MB
- Total amount of disk used: 50.69 MB
An example of 'train' looks as follows.
{
"context": "sentimental",
"conv_id": "hit:0_conv:1",
"prompt": "I remember going to the fireworks with my best friend. There was a lot of people_comma_ but it only felt like us in the world.",
"selfeval": "5|5|5_2|2|5",
"speaker_idx": 1,
"tags": "",
"utterance": "I remember going to see the fireworks with my best friend. It was the first time we ever spent time alone together. Although there was a lot of people_comma_ we felt like the only people in the world.",
"utterance_idx": 1
}
Data Fields
The data fields are the same among all splits.
default
conv_id
: astring
feature.utterance_idx
: aint32
feature.context
: astring
feature.prompt
: astring
feature.speaker_idx
: aint32
feature.utterance
: astring
feature.selfeval
: astring
feature.tags
: astring
feature.
Data Splits
name | train | validation | test |
---|---|---|---|
default | 76673 | 12030 | 10943 |
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{rashkin2019towards,
title = {Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset},
author = {Hannah Rashkin and Eric Michael Smith and Margaret Li and Y-Lan Boureau},
booktitle = {ACL},
year = {2019},
}
Contributions
Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.