Closes #629 | Add/Update Dataloader VLSP2020 MT

seacrowd/sea_datasets/vlsp2020_mt_envi/vlsp2020_mt_envi.py

@@ -0,0 +1,162 @@
+# coding=utf-8
+# Copyright 2022 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.
+
+"""
+Parallel and monolingual data for training machine translation systems translating English texts into Vietnamese, with a focus on news domain. The data was crawled from high-quality bilingual or multilingual websites of news and one-speaker educational talks on various topics, mostly technology, entertainment, and design (hereby referred to as TED-like talks). The dataset also includes noisy movie subtitles from the OpenSubtitle dataset.
+"""
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+
+_CITATION = """\
+@inproceedings{vlsp2020-mt,
+title     = {{Goals, Challenges and Findings of the VLSP 2020 English-Vietnamese News Translation Shared Task}},
+author    = {Thanh-Le Ha and Van-Khanh Tran and Kim-Anh Nguyen},
+booktitle = {{Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing - VLSP 2020}},
+year      = {2020}
+}
+"""
+
+_DATASETNAME = "vlsp2020_mt_envi"
+
+_DESCRIPTION = """\
+Parallel and monolingual data for training machine translation systems translating English texts into Vietnamese, with a focus on news domain. The data was crawled from high-quality bilingual or multilingual websites of news and one-speaker educational talks on various topics, mostly technology, entertainment, and design (hereby referred to as TED-like talks). The dataset also includes noisy movie subtitles from the OpenSubtitle dataset.
+"""
+
+_HOMEPAGE = "https://github.com/thanhleha-kit/EnViCorpora"
+
+_LANGUAGES = ["vie"] 
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = "https://github.com/thanhleha-kit/EnViCorpora/archive/refs/heads/master.zip"
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+class Vlsp2020MtEnviDataset(datasets.GeneratorBasedBuilder):
+    """Parallel and monolingual data for training machine translation systems translating English texts into Vietnamese, with a focus on news domain. The data was crawled from high-quality bilingual or multilingual websites of news and one-speaker educational talks on various topics, mostly technology, entertainment, and design (hereby referred to as TED-like talks). The dataset also includes noisy movie subtitles from the OpenSubtitle dataset."""
+
+    # Skipping openSub & mono-vi for future development (Large Drive file download bottleneck)
+    subsets = {
+        # key: subset_id, value: subset_filename
+        "EVBCorpus" : ["bitext"],
+        "VLSP20-official" : ["offi_test"],
+        "basic": ["data"],
+        "indomain-news": ["train", "dev", "tst"],
+        "iwslt15": ["train", "dev", "test"],
+        "ted-like": ["data"],
+        "wiki-alt": ["data"]
+    }
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME}_{subset} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in list(subsets.keys())
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_seacrowd_t2t",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME}_{subset} SEACrowd schema",
+            schema="seacrowd_t2t",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in list(subsets.keys())
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_VLSP20-official_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features({
+                "id": datasets.Value("string"),
+                "text_en": datasets.Value("string"),
+                "text_vi": datasets.Value("string"),
+            })
+
+        elif self.config.schema == "seacrowd_t2t":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        subset_id = self.config.subset_id.split("_")[-1]
+
+        splitnames = [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
+        filenames = self.subsets[subset_id]
+
+        data_dir = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=splitnames[i],
+                gen_kwargs={
+                    "filepath": {
+                        "en": os.path.join(data_dir, "EnViCorpora-master", subset_id, f"{filenames[i]}.en"),
+                        "vi": os.path.join(data_dir, "EnViCorpora-master", subset_id, f"{filenames[i]}.vi"),
+                    },
+                },
+            )
+            for i in range(len(filenames))
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(filepath["en"], 'r') as f:
+            en = f.readlines()
+        with open(filepath["vi"], 'r') as f:
+            vi = f.readlines()
+
+        if self.config.schema == "source":
+            for i, (en_text, vi_text) in enumerate(zip(en, vi)):
+                yield i, {
+                        "id": str(i),
+                        "text_en": en_text.strip(), 
+                        "text_vi": vi_text.strip()
+                    }
+
+        elif self.config.schema == "seacrowd_t2t":
+            for i, (en_text, vi_text) in enumerate(zip(en, vi)):
+                yield i, {
+                        "id": str(i),
+                        "text_1": en_text.strip(),
+                        "text_2": vi_text.strip(),
+                        "text_1_name": "en",
+                        "text_2_name": "vi",
+                    }