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"""
Copyright (c) 2023, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: Apache License 2.0
For full license text, see the LICENSE file in the repo root or https://www.apache.org/licenses/LICENSE-2.0
"""
#!/usr/bin/env python3
import os
import json
import datasets
from datasets import (GeneratorBasedBuilder,
BuilderConfig,
SplitGenerator,
DatasetInfo,
Features,
Sequence,
Value,
Version)
logger = datasets.logging.get_logger(__name__)
datasets.logging.disable_progress_bar()
_VERSION = Version("1.0.0")
_CITATION = """
@misc{zhang2023dialogstudio,
title={DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI},
author={Jianguo Zhang and Kun Qian and Zhiwei Liu and Shelby Heinecke and Rui Meng and Ye Liu and Zhou Yu and and Huan Wang and Silvio Savarese and Caiming Xiong},
year={2023},
eprint={2307.10172},
archivePrefix={arXiv},
primaryClass={cs.CL}
"""
DATASETS = {
# not pass: "CANARD"
"open_domain": [
"chitchat-dataset", "ConvAI2", "AntiScam", "Empathetic", "HH-RLHF",
"PLACES3.5", "Prosocial", "SODA"
],
"knowledge_grounded": [
"CompWebQ", "CoQA", "CoSQL", "DART", "FeTaQA",
"GrailQA", "HybridQA", "MTOP", "MultiModalQA", "SParC",
"Spider", "SQA", "ToTTo", "WebQSP", "WikiSQL",
"WikiTQ", "wizard_of_internet", "wizard_of_wikipedia"
],
"dialogue_summarization": [
"AMI", "CRD3", "DialogSum", "ECTSum", "ICSI",
"MediaSum", "QMSum", "SAMSum", "TweetSumm", "ConvoSumm",
"SummScreen_ForeverDreaming", "SummScreen_TVMegaSite"
],
"natural_language_understanding": [
"ATIS", "ATIS-NER", "BANKING77", "BANKING77-OOS", "CLINC-Single-Domain-OOS-banking",
"CLINC-Single-Domain-OOS-credit_cards", "CLINC150", "DSTC8-SGD", "HWU64", "MIT-Movie",
"MIT-Restaurant", "RESTAURANTS8K", "SNIPS", "SNIPS-NER", "TOP", "TOP-NER"
],
"task_oriented": [
"ABCD", "AirDialogue", "BiTOD", "CaSiNo", "CraigslistBargains",
"Disambiguation", "DSTC2-Clean", "FRAMES", "GECOR", "HDSA-Dialog",
"KETOD", "KVRET", "MetaLWOZ", "MS-DC", "MuDoCo",
"MulDoGO", "MultiWOZ_2.1", "MULTIWOZ2_2", "SGD", "SimJointGEN",
"SimJointMovie", "SimJointRestaurant", "STAR", "Taskmaster1", "Taskmaster2",
"Taskmaster3", "WOZ2_0"
],
"conversational_recommendation": [
"Redial", "DuRecDial-2.0", "OpenDialKG", "SalesBot",
]
}
_URL = "https://huggingface.co/datasets/Salesforce/dialogstudio/tree/main/"
class DialogStudioConfig(BuilderConfig):
"""BuilderConfig for DialogStudio."""
def __init__(self, extra_features, category, data_name, citation, url, **kwargs):
"""BuilderConfig for DialogStudio.
Args:
extra_features: `list[string]`, list of the features that will appear in the
feature dict. Should not include "label".
data_url: `string`, url to download the zip file from.
citation: `string`, citation for the data set.
url: `string`, url for information about the data set.
label_classes: `list[string]`, the list of classes for the label if the
label is present as a string. Non-string labels will be cast to either
'False' or 'True'.
**kwargs: keyword arguments forwarded to super.
"""
super(DialogStudioConfig, self).__init__(version=_VERSION, **kwargs)
self.extra_features = extra_features
self.category = category
self.data_name = data_name
self.compressed_file = f"{data_name}.zip"
self.citation = citation
self.url = url
class DialogStudio(GeneratorBasedBuilder):
"""DialogStudio"""
BUILDER_CONFIGS = []
for category, dataset_list in DATASETS.items():
if category in ["task_oriented", "conversational_recommendation"]:
extra_features = {
"dialog":[
"external knowledge non-flat",
"external knowledge",
"dst knowledge",
"intent knowledge",
],
"turn":[
"dst",
"dst accumulated",
"intent",
"external knowledge",
"external knowledge non-flat"
]
}
elif category in ["natural_language_understanding"]:
extra_features = {"dialog":[], "turn":["external knowledge", "external knowledge non-flat"]}
else:
extra_features = {"dialog":[], "turn":[]}
for data_name in dataset_list:
BUILDER_CONFIGS.append(
DialogStudioConfig(
name=data_name,
data_name=data_name,
description="",
extra_features=extra_features,
category=category,
citation=_CITATION,
url="https://github.com/salesforce/DialogStudio/tree/main",
))
DEFAULT_CONFIG_NAME = "WOZ2_0"
def _info(self):
features = {
"original dialog id": Value("string"),
"new dialog id": Value("string"),
"dialog index": Value("int32"),
"original dialog info": Value("string"),
"log": [
{
"turn id": Value("int32"),
"user utterance": Value("string"),
"system response": Value("string"),
"dialog history": Value("string"),
"original user side information": Value("string"),
"original system side information": Value("string"),
}
],
"prompt": [Value("string")]
}
if self.config.extra_features["dialog"]:
for name in self.config.extra_features["dialog"]:
features[name] = Value("string")
if self.config.extra_features["turn"]:
log_config = {
"turn id": Value("int32"),
"user utterance": Value("string"),
"system response": Value("string"),
"dialog history": Value("string"),
"original user side information": Value("string"),
"original system side information": Value("string"),
}
for name in self.config.extra_features["turn"]:
log_config[name] = Value("string")
features["log"] = [log_config]
return DatasetInfo(
description="",
features=Features(features),
homepage=self.config.url,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""
This script assume the datset is not stored in zip file
Instead, data is stored in format:
.
|-task-oriented
|-WOZ2.0
|-train
|-dialogues_1.json
|-dialogues_2.json
|-...
this script would download the json file one-by-one
"""
splits = []
file_path_list = {"train":[], "val":[], "test":[]}
print("❤️Attention❤️: Dataset download may take some time. We appreciate your patience!")
for mode in ["train", "val", "test"]:
for file_idx in range(1, 1000000):
file_to_download = os.path.join(self.config.category, self.config.data_name, mode, f"dialogues_{file_idx}.json")
try:
dl_path = dl_manager.download(file_to_download)
except:
break
file_path_list[mode].append(dl_path)
if file_path_list["train"]:
if any(split.name == datasets.Split.TRAIN for split in splits):
raise ValueError("Split 'train' already added.")
splits.append(
SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"file_path_list": file_path_list["train"],
"split": datasets.Split.TRAIN,
},
)
)
if file_path_list["val"]:
if any(split.name == datasets.Split.VALIDATION for split in splits):
raise ValueError("Split 'validation' already added.")
splits.append(
SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"file_path_list": file_path_list["val"],
"split": datasets.Split.VALIDATION,
},
)
)
if file_path_list["test"]:
if any(split.name == datasets.Split.TEST for split in splits):
raise ValueError("Split 'test' already added.")
splits.append(
SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"file_path_list": file_path_list["test"],
"split": datasets.Split.TEST,
},
)
)
return splits
def _load_json(self, file_path):
with open(file_path, encoding="utf-8") as f:
data = json.loads(f.read())
return data
def _generate_examples(self, file_path_list, split):
"""This function returns the examples in the raw (text) form."""
data = {}
for filepath in file_path_list:
data.update(self._load_json(filepath))
logger.info(f"generating {len(data)} examples from = {split}")
for dial_id, dial in data.items():
if type(dial["log"]) == dict:
dial["log"] = [dial["log"]]
example = {
"original dialog id": dial["original dialog id"],
"new dialog id": dial_id,
"dialog index": dial["dialog index"],
"original dialog info": json.dumps(dial["original dialog info"]),
"log": [{
"turn id": turn["turn id"],
"user utterance": turn["user utterance"],
"system response": turn["system response"],
"dialog history": turn["dialog history"],
"original user side information": json.dumps(turn["original user side information"]),
"original system side information": json.dumps(turn["original system side information"]),
} for turn in dial["log"]],
"prompt": dial["prompt"] if "prompt" in dial and dial["prompt"] else [""]
}
if self.config.extra_features["dialog"]:
for name in self.config.extra_features["dialog"]:
example[name] = json.dumps(dial[name]) if name in dial else ""
if self.config.extra_features["turn"]:
for idx, turn in enumerate(example["log"]):
for name in self.config.extra_features["turn"]:
example["log"][idx][name] = json.dumps(dial["log"][idx][name]) if name in dial["log"][idx] else ""
yield dial["dialog index"], example
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