Datasets:
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"""TODO(empathetic_dialogues): Add a description here."""
import csv
import os
import datasets
_CITATION = """\
@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},
}
"""
_DESCRIPTION = """\
PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
"""
_URL = "https://dl.fbaipublicfiles.com/parlai/empatheticdialogues/empatheticdialogues.tar.gz"
class EmpatheticDialogues(datasets.GeneratorBasedBuilder):
"""TODO(empathetic_dialogues): Short description of my dataset."""
# TODO(empathetic_dialogues): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(empathetic_dialogues): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"conv_id": datasets.Value("string"),
"utterance_idx": datasets.Value("int32"),
"context": datasets.Value("string"),
"prompt": datasets.Value("string"),
"speaker_idx": datasets.Value("int32"),
"utterance": datasets.Value("string"),
"selfeval": datasets.Value("string"),
"tags": datasets.Value("string")
# These are the features of your dataset like images, labels ...
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://github.com/facebookresearch/EmpatheticDialogues",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(empathetic_dialogues): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
dl_dir = dl_manager.download_and_extract(_URL)
data_dir = os.path.join(dl_dir, "empatheticdialogues")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "train.csv")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "valid.csv")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(empathetic_dialogues): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = csv.DictReader(f)
for id_, row in enumerate(data):
utterance = row["utterance"]
speaker_id = int(row["speaker_idx"])
context = row["context"]
conv_id = row["conv_id"]
tags = row["tags"] if row["tags"] else ""
selfeval = row["selfeval"] if row["selfeval"] else ""
utterance_id = int(row["utterance_idx"])
prompt = row["prompt"]
yield id_, {
"utterance": utterance,
"utterance_idx": utterance_id,
"context": context,
"speaker_idx": speaker_id,
"conv_id": conv_id,
"selfeval": selfeval,
"prompt": prompt,
"tags": tags,
}
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