# 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 datasets from conllu import TokenList 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 Tasks _CITATION = """\ @article {10.3844/jcssp.2020.1585.1597, author = {Alfina, Ika and Budi, Indra and Suhartanto, Heru}, title = {Tree Rotations for Dependency Trees: Converting the Head-Directionality of Noun Phrases}, article_type = {journal}, volume = {16}, number = {11}, year = {2020}, month = {Nov}, pages = {1585-1597}, doi = {10.3844/jcssp.2020.1585.1597}, url = {https://thescipub.com/abstract/jcssp.2020.1585.1597}, journal = {Journal of Computer Science}, publisher = {Science Publications} } """ _LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data) _LOCAL = False _DATASETNAME = "ud_id_csui" _DESCRIPTION = """\ UD Indonesian-CSUI is a conversion from an Indonesian constituency treebank in the Penn Treebank format named Kethu that was also a conversion from a constituency treebank built by Dinakaramani et al. (2015). This treebank is named after the place where treebanks were built: Faculty of Computer Science (CS), Universitas Indonesia (UI). About this treebank: - Genre is news in formal Indonesian (the majority is economic news) - 1030 sentences (28K words) divided into testing and training dataset of around 10K words and around 18K words respectively. - Average of 27.4 words per-sentence. """ _HOMEPAGE = "https://github.com/UniversalDependencies/UD_Indonesian-CSUI" _LICENSE = "CC BY-SA 4.0" _URLS = { _DATASETNAME: { "train": "https://raw.githubusercontent.com/UniversalDependencies/UD_Indonesian-CSUI/master/id_csui-ud-train.conllu", "test": "https://raw.githubusercontent.com/UniversalDependencies/UD_Indonesian-CSUI/master/id_csui-ud-test.conllu", }, } _SUPPORTED_TASKS = [Tasks.DEPENDENCY_PARSING, Tasks.MACHINE_TRANSLATION, Tasks.POS_TAGGING] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class UdIdCsuiDataset(datasets.GeneratorBasedBuilder): """Treebank of formal Indonesian news which consists of 1030 sentences (28K words)""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) # source: https://universaldependencies.org/u/pos/ UPOS_TAGS = ["ADJ", "ADP", "ADV", "AUX", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X"] BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}", ), SEACrowdConfig( name=f"{_DATASETNAME}_seacrowd_kb", version=SEACROWD_VERSION, description=f"{_DATASETNAME} Nusantara KB schema", schema="seacrowd_kb", subset_id=f"{_DATASETNAME}", ), SEACrowdConfig( name=f"{_DATASETNAME}_seacrowd_t2t", version=SEACROWD_VERSION, description=f"{_DATASETNAME} Nusantara Text to Text schema", schema="seacrowd_t2t", subset_id=f"{_DATASETNAME}", ), SEACrowdConfig( name=f"{_DATASETNAME}_seacrowd_seq_label", version=SEACROWD_VERSION, description=f"{_DATASETNAME} Nusantara Seq Label schema", schema="seacrowd_seq_label", subset_id=f"{_DATASETNAME}", ), ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { # metadata "sent_id": datasets.Value("string"), "text": datasets.Value("string"), "text_en": datasets.Value("string"), # tokens "id": [datasets.Value("string")], "form": [datasets.Value("string")], "lemma": [datasets.Value("string")], "upos": [datasets.Value("string")], "xpos": [datasets.Value("string")], "feats": [datasets.Value("string")], "head": [datasets.Value("string")], "deprel": [datasets.Value("string")], "deps": [datasets.Value("string")], "misc": [datasets.Value("string")], } ) elif self.config.schema == "seacrowd_kb": features = schemas.kb_features elif self.config.schema == "seacrowd_t2t": features = schemas.text2text_features elif self.config.schema == "seacrowd_seq_label": features = schemas.seq_label_features(self.UPOS_TAGS) else: raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.") 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.""" urls = _URLS[_DATASETNAME] data_path = dl_manager.download(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_path["train"], }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_path["test"], }, ), ] @staticmethod def _assert_multispan_range_is_one(token_list: TokenList): """ Asserting that all tokens with multiple span can only have 2 span, and \ no field other than form has important information """ for token in token_list.filter(id=lambda i: not isinstance(i, int)): _id = token["id"] assert len(_id) == 3, f"Unexpected length of non-int CONLLU Token's id. Expected 3, found {len(_id)};" assert all(isinstance(a, b) for a, b in zip(_id, [int, str, int])), f"Non-int ID should be in format of '\\d+-\\d+'. Found {_id};" assert _id[2] - _id[0] == 1, f"Token has more than 2 spans. Found {_id[2] - _id[0] + 1} spans;" for key in ["lemma", "upos", "xpos", "feats", "head", "deprel", "deps"]: assert token[key] in {"_", None}, f"Field other than 'form' should not contain extra information. Found: '{key}' = '{token[key]}'" def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` dataset = list(load_ud_data(filepath, filter_kwargs={"id": lambda i: isinstance(i, int)}, assert_fn=self._assert_multispan_range_is_one)) if self.config.schema == "source": pass elif self.config.schema == "seacrowd_kb": dataset = load_ud_data_as_seacrowd_kb(filepath, dataset) elif self.config.schema == "seacrowd_t2t": dataset = list( map( lambda d: { "id": d["sent_id"], "text_1": d["text"], "text_2": d["text_en"], "text_1_name": "ind", "text_2_name": "eng", }, dataset, ) ) elif self.config.schema == "seacrowd_seq_label": dataset = list( map( lambda d: { "id": d["sent_id"], "tokens": d["form"], "labels": d["upos"], }, dataset, ) ) else: raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.") for key, example in enumerate(dataset): yield key, example