File size: 4,774 Bytes
e077548 807227e e077548 957a67e e077548 957a67e e077548 957a67e e077548 957a67e e077548 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
import csv
import json
import os
import datasets
_CITATION = """\
@inproceedings{cs_restaurants,
address = {Tokyo, Japan},
title = {Neural {Generation} for {Czech}: {Data} and {Baselines}},
shorttitle = {Neural {Generation} for {Czech}},
url = {https://www.aclweb.org/anthology/W19-8670/},
urldate = {2019-10-18},
booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
author = {Dušek, Ondřej and Jurčíček, Filip},
month = oct,
year = {2019},
pages = {563--574},
}
"""
_DESCRIPTION = """\
The task is generating responses in the context of a (hypothetical) dialogue
system that provides information about restaurants. The input is a basic
intent/dialogue act type and a list of slots (attributes) and their values.
The output is a natural language sentence.
"""
_URLs = {
"train": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/train.json",
"validation": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/devel.json",
"test": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/test.json",
"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/cs_restaurants.zip",
}
class CSRestaurants(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DEFAULT_CONFIG_NAME = "cs_restaurants"
def _info(self):
features = datasets.Features(
{
"gem_id": datasets.Value("string"),
"gem_parent_id": datasets.Value("string"),
"dialog_act": datasets.Value("string"),
"dialog_act_delexicalized": datasets.Value("string"),
"target_delexicalized": datasets.Value("string"),
"target": datasets.Value("string"),
"references": [datasets.Value("string")],
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=datasets.info.SupervisedKeysData(
input="dialog_act", output="target"
),
homepage="https://github.com/UFAL-DSG/cs_restaurant_dataset",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_URLs)
challenge_sets = [
("challenge_train_sample", "train_cs_restaurants_RandomSample500.json"),
(
"challenge_validation_sample",
"validation_cs_restaurants_RandomSample500.json",
),
(
"challenge_test_scramble",
"test_cs_restaurants_ScrambleInputStructure500.json",
),
]
return [
datasets.SplitGenerator(
name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}
)
for spl in ["train", "validation", "test"]
] + [
datasets.SplitGenerator(
name=challenge_split,
gen_kwargs={
"filepath": os.path.join(
dl_dir["challenge_set"], "cs_restaurants", filename
),
"split": challenge_split,
},
)
for challenge_split, filename in challenge_sets
]
def _generate_examples(self, filepath, split, filepaths=None, lang=None):
"""Yields examples."""
if split.startswith("challenge"):
exples = json.load(open(filepath, encoding="utf-8"))
if isinstance(exples, dict):
assert len(exples) == 1, "multiple entries found"
exples = list(exples.values())[0]
for id_, exple in enumerate(exples):
if len(exple) == 0:
continue
exple["gem_parent_id"] = exple["gem_id"]
exple["gem_id"] = f"cs_restaurants-{split}-{id_}"
yield id_, exple
else:
with open(filepath, encoding="utf8") as f:
data = json.load(f)
for id_, instance in enumerate(data):
yield id_, {
"gem_id": f"cs_restaurants-{split}-{id_}",
"gem_parent_id": f"cs_restaurants-{split}-{id_}",
"dialog_act": instance["da"],
"dialog_act_delexicalized": instance["delex_da"],
"target": instance["text"],
"target_delexicalized": instance["delex_text"],
"references": [] if split == "train" else [instance["text"]],
}
|