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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""Multi-News dataset."""
import datasets
_LICENSE = "For non-commercial research and educational purposes only"
_DESCRIPTION = """
Financial documents
"""
_REPO = "https://huggingface.co/datasets/searde/dataset-financial-documents-2/blob/main/data"
_URLs = {
"train": [
f"{_REPO}/train.src.cleaned",
f"{_REPO}/train.tgt",
],
"val": [
f"{_REPO}/val.src.cleaned",
f"{_REPO}/val.tgt",
],
"test": [
f"{_REPO}/test.src.cleaned",
f"{_REPO}/test.tgt",
],
}
_DOCUMENT = "document"
_SUMMARY = "summary"
class DatasetFinancialDocuments(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
supervised_keys=(_DOCUMENT, _SUMMARY),
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
files = dl_manager.download(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"src_file": files["train"][0], "tgt_file": files["train"][1]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"src_file": files["val"][0], "tgt_file": files["val"][1]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"src_file": files["test"][0], "tgt_file": files["test"][1]},
),
]
def _generate_examples(self, src_file, tgt_file):
"""Yields examples."""
with open(src_file, encoding="utf-8") as src_f, open(tgt_file, encoding="utf-8") as tgt_f:
for i, (src_line, tgt_line) in enumerate(zip(src_f, tgt_f)):
yield i, {
# In original file, each line has one example and natural newline
# tokens "\n" are being replaced with "NEWLINE_CHAR". Here restore
# the natural newline token to avoid special vocab "NEWLINE_CHAR".
_DOCUMENT: src_line.strip().replace("NEWLINE_CHAR", "\n"),
_SUMMARY: tgt_line.strip(),
}
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