def _split_generators(self, dl_manager: datasets.DownloadManager):
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
dev_path = dl_manager.download_and_extract(_DEV_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN,
gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": dev_path}),
]
def _split_generators(
self, dl_manager: datasets.DownloadManager
) -> List[datasets.SplitGenerator]:
SPLITS = self.SPLITS
_URL = self.info["raw_url"]
urls_to_download = {}
for split in SPLITS:
if split not in urls_to_download:
urls_to_download[split] = {}
for key, url in self.generate_urls(split):
if not url.startswith("http"):
url = _URL + "/" + url
urls_to_download[split][key] = url
downloaded_files = {}
for k, v in urls_to_download.items():
downloaded_files[k] = dl_manager.download_and_extract(v)
return [
datasets.SplitGenerator(
name=SPLITS[k],
gen_kwargs={
"split_name": k,
"file_paths": downloaded_files[k]
},
) for k in SPLITS
]
def _split_generators(
self, dl_manager: datasets.DownloadManager
) -> List[datasets.SplitGenerator]:
images_path = Path(dl_manager.download_and_extract(_URL)) / "PetImages"
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN,
gen_kwargs={"images_path": images_path}),
]
def _split_generators(
self, dl_manager: datasets.DownloadManager
) -> List[datasets.SplitGenerator]:
images_path = os.path.join(dl_manager.download_and_extract(_URL),
"PetImages")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"files": dl_manager.iter_files([images_path])}),
]
def _split_generators(self, dl_manager: datasets.DownloadManager):
"""Return the split generators for the uniref datasets."""
downloaded_file = dl_manager.download_and_extract(self.config.url)
return [
datasets.SplitGenerator(
name=str(self.config.name),
# These kwargs will be passed to _generate_examples
gen_kwargs={"file_paths": [downloaded_file]},
),
]
def _split_generators(
self, dl_manager: datasets.DownloadManager
) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
urls_to_download = {
"train": self.config.data_dir + "train.csv",
"test": self.config.data_dir + "test.csv"
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": downloaded_files["test"]})
]
def _split_generators(self, dl_manager: datasets.DownloadManager):
"""Returns SplitGenerators."""
# 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, "ijcnlp_dailydialog")
# The splits are nested inside the zip
for name in ("train", "validation", "test"):
zip_fpath = os.path.join(data_dir, f"{name}.zip")
with ZipFile(zip_fpath) as zip_file:
zip_file.extractall(path=data_dir)
zip_file.close()
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"file_path": os.path.join(data_dir, "train", "dialogues_train.txt"),
"act_path": os.path.join(data_dir, "train", "dialogues_act_train.txt"),
"emotion_path": os.path.join(data_dir, "train", "dialogues_emotion_train.txt"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"file_path": os.path.join(data_dir, "test", "dialogues_test.txt"),
"act_path": os.path.join(data_dir, "test", "dialogues_act_test.txt"),
"emotion_path": os.path.join(data_dir, "test", "dialogues_emotion_test.txt"),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"file_path": os.path.join(data_dir, "validation", "dialogues_validation.txt"),
"act_path": os.path.join(data_dir, "validation", "dialogues_act_validation.txt"),
"emotion_path": os.path.join(data_dir, "validation", "dialogues_emotion_validation.txt"),
"split": "dev",
},
),
]
def _split_generators(self, dl_manager: datasets.DownloadManager):
dl_dir = dl_manager.download_and_extract(_URL)
data_dir = os.path.join(dl_dir, "ijcnlp_dailydialog")
splits = [
datasets.Split.TRAIN, datasets.Split.VALIDATION,
datasets.Split.TEST
]
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"data_zip": os.path.join(data_dir, f"{split}.zip"),
"dialog_path": f"{split}/dialogues_{split}.txt",
"act_path": f"{split}/dialogues_act_{split}.txt",
"emotion_path": f"{split}/dialogues_emotion_{split}.txt",
},
) for split in splits
]
def _split_generators(self, dl_manager: datasets.DownloadManager):
dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
task_name = _get_task_name_from_data_url(self.config.data_url)
dl_dir = os.path.join(dl_dir, task_name)
if self.config.name == "lidirus":
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": os.path.join(dl_dir, f"{task_name}.jsonl"),
"split": datasets.Split.TEST,
},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": os.path.join(dl_dir, "train.jsonl"),
"split": datasets.Split.TRAIN,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": os.path.join(dl_dir, "val.jsonl"),
"split": datasets.Split.VALIDATION,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": os.path.join(dl_dir, "test.jsonl"),
"split": datasets.Split.TEST,
},
),
]
def _split_generators(self, dl_manager: datasets.DownloadManager):
"""Returns SplitGenerators.
Uses local files if a data_dir is specified. Otherwise downloads the files from their official url."""
if self.config.data_dir:
data_dir = self.config.data_dir
else:
url = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(url)
if self.config.name in ["NER", "ROLES", "BORING"]:
data_dir += "/sd_panels"
elif self.config.name == "PANELIZATION":
data_dir += "/sd_figs"
else:
raise ValueError(f"unkonwn config name: {self.config.name}")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir + "/train.jsonl",
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir + "/test.jsonl",
"split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir + "/eval.jsonl",
"split": "eval",
},
),
]