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vndt.py

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+# 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.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import conllu
+import datasets
+
+from seacrowd.sea_datasets.vndt.utils import parse_token_and_impute_metadata
+from seacrowd.utils import schemas
+from seacrowd.utils.common_parser import (load_ud_data,
+                                          load_ud_data_as_seacrowd_kb)
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@InProceedings{Nguyen2014NLDB,
+  author = {Nguyen, Dat Quoc  and  Nguyen, Dai Quoc  and  Pham, Son Bao and Nguyen, Phuong-Thai and Nguyen, Minh Le},
+  title = {{From Treebank Conversion to Automatic Dependency Parsing for Vietnamese}},
+  booktitle = {{Proceedings of 19th International Conference on Application of Natural Language to Information Systems}},
+  year = {2014},
+  pages = {196-207},
+  url = {https://github.com/datquocnguyen/VnDT},
+}
+"""
+
+_DATASETNAME = "vndt"
+
+_DESCRIPTION = """\
+VnDT is a Vietnamese dependency treebank, consisting of 10K+ sentences (219k words). The VnDT Treebank is automatically
+converted from the input Vietnamese Treebank.
+"""
+
+_HOMEPAGE = "https://github.com/datquocnguyen/VnDT"
+
+_LANGUAGES = {"vie": "vi"}
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    "gold-dev": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-gold-POS-tags-dev.conll",
+    "gold-test": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-gold-POS-tags-test.conll",
+    "gold-train": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-gold-POS-tags-train.conll",
+    "predicted-dev": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-predicted-POS-tags-dev.conll",
+    "predicted-test": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-predicted-POS-tags-test.conll",
+    "predicted-train": "https://raw.githubusercontent.com/datquocnguyen/VnDT/master/VnDTv1.1-predicted-POS-tags-train.conll",
+}
+
+_SUPPORTED_TASKS = [Tasks.DEPENDENCY_PARSING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+class VnDTDataset(datasets.GeneratorBasedBuilder):
+    """
+    VnDT is a Vietnamese dependency treebank from https://github.com/datquocnguyen/VnDT.
+    """
+
+    # Override conllu.parse_token_and_metadata via monkey patching
+    conllu.parse_token_and_metadata = parse_token_and_impute_metadata
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_gold_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} gold standard source schema",
+            schema="source",
+            subset_id="gold",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_gold_seacrowd_kb",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} gold standard SEACrowd schema",
+            schema="seacrowd_kb",
+            subset_id="gold",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_predicted_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} predicted source schema",
+            schema="source",
+            subset_id="predicted",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_predicted_seacrowd_kb",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} predicted SEACrowd schema",
+            schema="seacrowd_kb",
+            subset_id="predicted",
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Sequence(datasets.Value("int8")),
+                    "form": datasets.Sequence(datasets.Value("string")),
+                    "lemma": datasets.Sequence(datasets.Value("string")),
+                    "upos": datasets.Sequence(datasets.Value("string")),
+                    "xpos": datasets.Sequence(datasets.Value("string")),
+                    "feats": datasets.Sequence(datasets.Value("string")),
+                    "head": datasets.Sequence(datasets.Value("int8")),
+                    "deprel": datasets.Sequence(datasets.Value("string")),
+                    "deps": datasets.Sequence(datasets.Value("string")),
+                    "misc": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+        elif self.config.schema == "seacrowd_kb":
+            features = schemas.kb_features
+        else:
+            raise ValueError(f"Invalid schema: '{self.config.schema}'")
+
+        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.
+        """
+
+        paths = {key: dl_manager.download_and_extract(value) for key, value in _URLS.items()}
+
+        if self.config.subset_id == "gold":
+            filtered_paths = {key: value for key, value in paths.items() if "gold" in key}
+        elif self.config.subset_id == "predicted":
+            filtered_paths = {key: value for key, value in paths.items() if "predicted" in key}
+        else:
+            raise NotImplementedError(f"Invalid subset: '{self.config.subset_id}'.")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepaths": [value for key, value in filtered_paths.items() if "dev" in key],
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": [value for key, value in filtered_paths.items() if "test" in key],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": [value for key, value in filtered_paths.items() if "train" in key],
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepaths: Path, split: str) -> Tuple[int, Dict]:
+        """
+        Yields examples as (key, example) tuples.
+        """
+
+        dataset = None
+        for file in filepaths:
+            if self.config.schema == "source":
+                dataset = list(load_ud_data(file))
+            elif self.config.schema == "seacrowd_kb":
+                dataset = list(load_ud_data_as_seacrowd_kb(file, dataset))
+            else:
+                raise ValueError(f"Invalid config: '{self.config.name}'")
+
+        for idx, example in enumerate(dataset):
+            if self.config.schema == "source":
+                example.pop('sent_id', None)
+                example.pop('text', None)
+            yield idx, example