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indonli.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.
+
+"""
+IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian.
+IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set.
+It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic
+phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning.
+
+The data is split across train, valid, test_lay, and test_expert.
+
+A small subset of test_expert is used as a diasnostic tool. For more info, please visit https://github.com/ir-nlp-csui/indonli
+
+The premise were collected from Indonesian Wikipedia and from other public Indonesian dataset: Indonesian PUD and GSD treebanks provided by the Universal Dependencies 2.5 and IndoSum
+
+The data was produced by humans.
+
+"""
+
+from pathlib import Path
+from typing import List
+
+import datasets
+import jsonlines
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{mahendra-etal-2021-indonli,
+    title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
+    author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.821",
+    pages = "10511--10527",
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "indonli"
+
+_DESCRIPTION = """\
+This dataset is designed for Natural Language Inference NLP task.  It is designed to provide a challenging test-bed
+for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural
+changes, idioms, or temporal and spatial reasoning.
+"""
+
+_HOMEPAGE = "https://github.com/ir-nlp-csui/indonli"
+
+_LICENSE = "Creative Common Attribution Share-Alike 4.0 International"
+
+# For publicly available datasets you will most likely end up passing these URLs to dl_manager in _split_generators.
+# In most cases the URLs will be the same for the source and nusantara config.
+# However, if you need to access different files for each config you can have multiple entries in this dict.
+# This can be an arbitrarily nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/train.jsonl",
+        "valid": "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/val.jsonl",
+        "test": "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/test.jsonl",
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]
+
+_SOURCE_VERSION = "1.1.0"  # Mentioned in https://github.com/huggingface/datasets/blob/main/datasets/indonli/indonli.py
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class IndoNli(datasets.GeneratorBasedBuilder):
+    """IndoNLI, a human-elicited NLI dataset for Indonesian containing ~18k sentence pairs annotated by crowd workers."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="indonli_source",
+            version=SOURCE_VERSION,
+            description="indonli source schema",
+            schema="source",
+            subset_id="indonli",
+        ),
+        NusantaraConfig(
+            name="indonli_nusantara_pairs",
+            version=NUSANTARA_VERSION,
+            description="indonli Nusantara schema",
+            schema="nusantara_pairs",
+            subset_id="indonli",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indonli_source"
+    labels = ["c", "e", "n"]
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "pair_id": datasets.Value("int32"),
+                    "premise_id": datasets.Value("int32"),
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "annotator_type": datasets.Value("string"),
+                    "sentence_size": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "nusantara_pairs":
+            features = schemas.pairs_features(self.labels)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls = _URLS[_DATASETNAME]
+        train_data_path = Path(dl_manager.download_and_extract(urls["train"]))
+        valid_data_path = Path(dl_manager.download_and_extract(urls["valid"]))
+        test_data_path = Path(dl_manager.download_and_extract(urls["test"]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": train_data_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": valid_data_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": test_data_path},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path):
+
+        if self.config.schema == "source":
+            print(filepath)
+            with jsonlines.open(filepath) as f:
+                skip = []  # To avoid duplicate IDs
+                for example in f.iter():
+                    if example["pair_id"] not in skip:
+                        skip.append(example["pair_id"])
+                        example = {
+                            "pair_id": example["pair_id"],
+                            "premise_id": example["premise_id"],
+                            "premise": example["premise"],
+                            "hypothesis": example["hypothesis"],
+                            "annotator_type": example["annotator_type"],
+                            "sentence_size": example["sentence_size"],
+                            "label": example["label"],
+                        }
+                        yield example["pair_id"], example
+
+        elif self.config.schema == "nusantara_pairs":
+            print(filepath)
+            with jsonlines.open(filepath) as f:
+                skip = []  # To avoid duplicate IDs
+                for example in f.iter():
+                    if example["pair_id"] not in skip:
+                        skip.append(example["pair_id"])
+                        nu_eg = {"id": str(example["pair_id"]), "text_1": example["premise"], "text_2": example["hypothesis"], "label": example["label"]}
+                        yield example["pair_id"], nu_eg
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")