|
import json |
|
import os |
|
|
|
import datasets |
|
|
|
|
|
class ERRNewsConfig(datasets.BuilderConfig): |
|
def __init__(self, data_url, features, **kwargs): |
|
super().__init__(version=datasets.Version("1.0.0"), **kwargs) |
|
self.features = features |
|
self.data_url = data_url |
|
|
|
|
|
class ERRNews(datasets.GeneratorBasedBuilder): |
|
features = ["transcript", "summary", "id"] |
|
data_url = "https://cs.taltech.ee/staff/heharm/AMIsum/" |
|
|
|
BUILDER_CONFIGS = [ |
|
ERRNewsConfig( |
|
name="full", |
|
features=features, |
|
data_url=data_url |
|
) |
|
] |
|
|
|
DEFAULT_CONFIG_NAME = "full" |
|
|
|
def _info(self): |
|
features = datasets.Features( |
|
{ |
|
"transcript": datasets.Value("string"), |
|
"summary": datasets.Value("string"), |
|
"id": datasets.Value("string"), |
|
}) |
|
|
|
description = """\ |
|
AMI Summarization Dataset (AMIsum) is a meeting summarization dataset, consisting of meeting transcripts \ |
|
and abstract summaries from the AMI Corpus: https://groups.inf.ed.ac.uk/ami/corpus/. |
|
""" |
|
|
|
return datasets.DatasetInfo( |
|
features=features, |
|
description=description, |
|
supervised_keys=None, |
|
version=self.config.version, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
train = "train.json" |
|
test = "test.json" |
|
val = "val.json" |
|
|
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"file_path": dl_manager.download(self.config.data_url + train), |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.VALIDATION, |
|
gen_kwargs={ |
|
"file_path": dl_manager.download(self.config.data_url + val), |
|
}, |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, |
|
gen_kwargs={ |
|
"file_path": dl_manager.download(self.config.data_url + test), |
|
}, |
|
), |
|
] |
|
|
|
def create_dict(self, data): |
|
res = dict() |
|
for key in self.config.features: |
|
res[key] = data[key] |
|
return res |
|
|
|
def _generate_examples(self, file_path): |
|
with open(file_path) as f: |
|
data = json.load(f) |
|
for idx, transcript in enumerate(data["transcript"]): |
|
id_ = data["id"][idx] |
|
yield id_, { |
|
"transcript": transcript, |
|
"summary": data["summary"][idx], |
|
"id": id_, |
|
} |
|
|