init commit for old wiki_lingua splits

wiki_lingua.py

@@ -0,0 +1,294 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""WikiLingua: A benchmark dataset for multilingual abstractive summarization."""
+
+import os
+import datasets
+
+
+_CITATION = """\
+@article{ladhak-wiki-2020,
+  title   = {WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization},
+  authors = {Faisal Ladhak, Esin Durmus, Claire Cardie and Kathleen McKeown},
+  journal = {arXiv preprint arXiv:2010.03093},
+  year    = {2020},
+  url     = {https://arxiv.org/abs/2010.03093}
+}
+"""
+
+_DESCRIPTION = """\
+WikiLingua is a large-scale multilingual dataset for the evaluation of
+crosslingual abstractive summarization systems. The dataset includes ~770k
+article and summary pairs in 18 languages from WikiHow. The gold-standard
+article-summary alignments across languages was done by aligning the images
+that are used to describe each how-to step in an article.
+"""
+
+_HOMEPAGE = "https://github.com/esdurmus/Wikilingua"
+
+_LICENSE = "CC BY-NC-SA 3.0"
+
+# TODO update script with new splits
+_URLs = {
+    "wiki_lingua_es_en_v0": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip",
+    },
+    "wiki_lingua_ru_en_v0": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip",
+    },
+    "wiki_lingua_tr_en_v0": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip",
+    },
+    "wiki_lingua_vi_en_v0": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua.zip",
+    },
+    "wiki_lingua_arabic_ar": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/arabic.zip",
+    },
+    "wiki_lingua_chinese_zh": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/chinese.zip",
+    },
+    "wiki_lingua_czech_cs": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/czech.zip",
+    },
+    "wiki_lingua_dutch_nl": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/dutch.zip",
+    },
+    "wiki_lingua_english_en": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/english.zip",
+    },
+    "wiki_lingua_french_fr": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/french.zip",
+    },
+    "wiki_lingua_german_de": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/german.zip",
+    },
+    "wiki_lingua_hindi_hi": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/hindi.zip",
+    },
+    "wiki_lingua_indonesian_id": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/indonesian.zip",
+    },
+    "wiki_lingua_italian_it": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/italian.zip",
+    },
+    "wiki_lingua_japanese_ja": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/japanese.zip",
+    },
+    "wiki_lingua_korean_ko": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/korean.zip",
+    },
+    "wiki_lingua_portuguese_pt": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/portuguese.zip",
+    },
+    "wiki_lingua_russian_ru": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/russian.zip",
+    },
+    "wiki_lingua_spanish_es": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/spanish.zip",
+    },
+    "wiki_lingua_thai_th": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/thai.zip",
+    },
+    "wiki_lingua_turkish_tr": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/turkish.zip",
+    },
+    "wiki_lingua_vietnamese_vi": {
+        "data": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_wikilingua_full/vietnamese.zip",
+    },
+}
+
+VERSION = datasets.Version("1.1.0")
+
+
+class WikilinguaConfig(datasets.BuilderConfig):
+    """BuilderConfig for WikiLingua."""
+
+    def __init__(self, name, **kwargs):
+
+        eles = name.split("_")
+        is_v0 = "v0" in name
+        if is_v0:
+            source_lang, target_lang = eles[-3], eles[-2]
+        else:
+            target_lang = eles[-1]
+            source_lang = target_lang
+
+        super().__init__(
+            name=name,
+            description=f"Wikilingua summarisation data ({source_lang} to {target_lang})",
+            **kwargs,
+        )
+        self.is_v0 = is_v0
+        self.source_lang = source_lang
+        self.target_lang = target_lang
+
+
+class WikiLingua(datasets.GeneratorBasedBuilder):
+    """WikiLingua: A benchmark dataset for multilingual abstractive summarization."""
+
+    BUILDER_CONFIG_CLASS = WikilinguaConfig
+
+    BUILDER_CONFIGS = [
+        WikilinguaConfig(
+            name=lang,
+            version=VERSION,
+        )
+        for lang in _URLs
+    ]
+
+    DEFAULT_CONFIG_NAME = "wiki_lingua_es_en_v0"
+
+    def _info(self):
+        if self.config.is_v0:
+            features = datasets.Features(
+                {
+                    "gem_id": datasets.Value("string"),
+                    "gem_parent_id": datasets.Value("string"),
+                    "source": datasets.Value("string"),
+                    "target": datasets.Value("string"),
+                    "references": [datasets.Value("string")],
+                }
+            )
+        else:
+            lang = self.config.source_lang
+            features = datasets.Features(
+                {
+                    "gem_id": datasets.Value("string"),
+                    "gem_parent_id": datasets.Value("string"),
+                    "source_aligned": datasets.Translation(languages=[lang, "en"]),
+                    "target_aligned": datasets.Translation(languages=[lang, "en"]),
+                    "source": datasets.Value("string"),
+                    "target": datasets.Value("string"),
+                    "references": [datasets.Value("string")],
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
+        if self.config.is_v0:
+
+            lang = self.config.source_lang
+            base_dir = os.path.join(
+                dl_dir["data"], "GEM_data_crosslingual", f"{lang}_en"
+            )
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": base_dir,
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": base_dir,
+                        "split": "val",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": base_dir,
+                        "split": "test",
+                    },
+                ),
+            ]
+        else:
+            lang = self.config.source_lang
+            lang_name = self.config.name.split("_")[-2]
+            base_dir = os.path.join(dl_dir["data"], lang_name)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": base_dir,
+                        "split": "train",
+                        "lang": lang,
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": base_dir,
+                        "split": "val",
+                        "lang": lang,
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": base_dir,
+                        "split": "test",
+                        "lang": lang,
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepath, split, lang=None):
+        """Yields examples."""
+        if self.config.is_v0:
+            source_path = os.path.join(filepath, f"{split}.src")
+            target_path = os.path.join(filepath, f"{split}.tgt")
+            with open(source_path, encoding="utf-8") as f_in:
+                with open(target_path, encoding="utf-8") as f_out:
+                    for id_, (src, tgt) in enumerate(zip(f_in, f_out)):
+                        yield id_, {
+                            "gem_id": f"{self.config.name}-{split}-{id_}",
+                            "gem_parent_id": f"{self.config.name}-{split}-{id_}",
+                            "source": src.strip(),
+                            "target": tgt.strip(),
+                            "references": [] if split == "train" else [tgt.strip()],
+                        }
+        else:
+            source_path = os.path.join(filepath, f"{split}.src.{lang}")
+            source_path_en = os.path.join(filepath, f"{split}.src.en")
+            target_path = os.path.join(filepath, f"{split}.tgt.{lang}")
+            target_path_en = os.path.join(filepath, f"{split}.tgt.en")
+
+            with open(source_path, encoding="utf-8") as f_in_ln:
+                with open(source_path_en, encoding="utf-8") as f_in_en:
+                    with open(target_path, encoding="utf-8") as f_out_ln:
+                        with open(target_path_en, encoding="utf-8") as f_out_en:
+                            for id_, (src_ln, src_en, tgt_ln, tgt_en) in enumerate(
+                                zip(f_in_ln, f_in_en, f_out_ln, f_out_en)
+                            ):
+                                yield id_, {
+                                    "gem_id": f"{self.config.name}-{split}-{id_}",
+                                    "gem_parent_id": f"{self.config.name}-{split}-{id_}",
+                                    "source_aligned": {
+                                        lang: src_ln.strip(),
+                                        "en": src_en.strip(),
+                                    },
+                                    "target_aligned": {
+                                        lang: tgt_ln.strip(),
+                                        "en": tgt_en.strip(),
+                                    },
+                                    "source": src_ln.strip(),
+                                    "target": tgt_en.strip(),
+                                    "references": []
+                                    if split == "train"
+                                    else [tgt_en.strip()],
+                                }