re_dial / re_dial.py
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Annotated dataset of dialogues where users recommend movies to each other."""
import json
import os
import datasets
_CITATION = """\
@inproceedings{li2018conversational,
title={Towards Deep Conversational Recommendations},
author={Li, Raymond and Kahou, Samira Ebrahimi and Schulz, Hannes and Michalski, Vincent and Charlin, Laurent and Pal, Chris},
booktitle={Advances in Neural Information Processing Systems 31 (NIPS 2018)},
year={2018}
}
"""
_DESCRIPTION = """\
ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users
recommend movies to each other. The dataset was collected by a team of researchers working at
Polytechnique Montréal, MILA – Quebec AI Institute, Microsoft Research Montréal, HEC Montreal, and Element AI.
The dataset allows research at the intersection of goal-directed dialogue systems
(such as restaurant recommendation) and free-form (also called “chit-chat”) dialogue systems.
"""
_HOMEPAGE = "https://redialdata.github.io/website/"
_LICENSE = "CC BY 4.0 License."
_DATA_URL = "https://github.com/ReDialData/website/raw/data/redial_dataset.zip"
class ReDial(datasets.GeneratorBasedBuilder):
"""Annotated dataset of dialogues where users recommend movies to each other."""
VERSION = datasets.Version("1.1.0")
def _info(self):
question_features = {
"movieId": datasets.Value("string"),
"suggested": datasets.Value("int32"),
"seen": datasets.Value("int32"),
"liked": datasets.Value("int32"),
}
features = datasets.Features(
{
"movieMentions": [
{
"movieId": datasets.Value("string"),
"movieName": datasets.Value("string"),
},
],
"respondentQuestions": [question_features],
"messages": [
{
"timeOffset": datasets.Value("int32"),
"text": datasets.Value("string"),
"senderWorkerId": datasets.Value("int32"),
"messageId": datasets.Value("int32"),
},
],
"conversationId": datasets.Value("int32"),
"respondentWorkerId": datasets.Value("int32"),
"initiatorWorkerId": datasets.Value("int32"),
"initiatorQuestions": [question_features],
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# 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=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_DATA_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "train_data.jsonl"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "test_data.jsonl"), "split": "test"},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
examples = f.readlines()
for id_, row in enumerate(examples):
data = json.loads(row.strip())
d = {}
movieMentions_list = []
for i in data["movieMentions"]:
d["movieId"] = i
d["movieName"] = data["movieMentions"][i]
movieMentions_list.append(d)
d = {}
respondentQuestions_list = []
for i in data["respondentQuestions"]:
d["movieId"] = i
alpha = data["respondentQuestions"][i]
z = {**d, **alpha} # merging 2 dictionaries
respondentQuestions_list.append(z)
d = {}
initiatorQuestions_list = []
for i in data["initiatorQuestions"]:
d["movieId"] = i
alpha = data["initiatorQuestions"][i]
z = {**d, **alpha} # merging 2 dictionaries
initiatorQuestions_list.append(z)
d = {}
yield id_, {
"movieMentions": movieMentions_list,
"respondentQuestions": respondentQuestions_list,
"messages": data["messages"],
"conversationId": data["conversationId"],
"respondentWorkerId": data["respondentWorkerId"],
"initiatorWorkerId": data["initiatorWorkerId"],
"initiatorQuestions": initiatorQuestions_list,
}