dataset loading script

xlwic.py

@@ -0,0 +1,231 @@
+from dataclasses import dataclass
+import datasets
+from datasets.info import DatasetInfo
+from datasets.utils.download_manager import DownloadManager
+import os
+
+_DESCRIPTION = """A system's task on any of the XL-WiC datasets is to identify the intended meaning of a word in a context of a given language. XL-WiC is framed as a binary classification task. Each instance in XL-WiC has a target word w, either a verb or a noun, for which two contexts are provided. Each of these contexts triggers a specific meaning of w. The task is to identify if the occurrences of w in the two contexts correspond to the same meaning or not.
+
+XL-WiC provides dev and test sets in the following 12 languages:
+
+Bulgarian (BG)
+Danish (DA)
+German (DE)
+Estonian (ET)
+Farsi (FA)
+French (FR)
+Croatian (HR)
+Italian (IT)
+Japanese (JA)
+Korean (KO)
+Dutch (NL)
+Chinese (ZH)
+and training sets in the following 3 languages:
+
+German (DE)
+French (FR)
+Italian (IT)
+"""
+_CITATION = """@inproceedings{raganato-etal-2020-xl-wic,
+  title={XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization},
+  author={Raganato, Alessandro and Pasini, Tommaso and Camacho-Collados, Jose and Pilehvar, Mohammad Taher},
+  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+  pages={7193--7206},
+  year={2020}
+}
+"""
+_DOWNLOAD_URL = "https://pilehvar.github.io/xlwic/data/xlwic_datasets.zip"
+_VERSION = "1.0.0"
+_WN_LANGS = ["EN", "BG", "ZH", "HR", "DA", "NL", "ET", "FA", "JA", "KO"]
+_WIKT_LANGS = ["IT", "FR", "DE"]
+_CODE_TO_LANG_ID = {
+    "EN": "english",
+    "BG": "bulgarian",
+    "ZH": "chinese",
+    "HR": "croatian",
+    "DA": "danish",
+    "NL": "dutch",
+    "ET": "estonian",
+    "FA": "farsi",
+    "JA": "japanese",
+    "KO": "korean",
+    "IT": "italian",
+    "FR": "french",
+    "DE": "german",
+}
+_AVAILABLE_PAIRS = (
+    list(zip(["EN"] * (len(_WN_LANGS) - 1), _WN_LANGS[1:]))
+    + list(zip(["EN"] * len(_WIKT_LANGS), _WIKT_LANGS))
+    + [("IT", "IT"), ("FR", "FR"), ("DE", "DE")]
+)
+
+@dataclass
+class XLWiCConfig(datasets.BuilderConfig):
+    version:str=None
+    training_lang:str = None
+    target_lang:str = None
+    name:str = None
+
+
+class XLWIC(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        XLWiCConfig(
+            name=f"xlwic_{source.lower()}_{target.lower()}",
+            training_lang=source,
+            target_lang=target,
+            version=datasets.Version(_VERSION, ""),
+        )
+        for source, target in _AVAILABLE_PAIRS
+    ]
+
+    def _info(self) -> DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "context_1": datasets.Value("string"),
+                    "context_2": datasets.Value("string"),
+                    "target_word": datasets.Value("string"),
+                    "pos": datasets.Value("string"),
+                    "target_word_location_1":
+                        {
+                            "char_start": datasets.Value("int32"),
+                            "char_end": datasets.Value("int32"),
+                        },
+                    "target_word_location_2": 
+                        {
+                            "char_start": datasets.Value("int32"),
+                            "char_end": datasets.Value("int32"),
+                        },
+                    "language": datasets.Value("string"),
+                    "label": datasets.Value("int32"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://pilehvar.github.io/xlwic/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        downloaded_file = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        dataset_root_folder = os.path.join(downloaded_file, "xlwic_datasets")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "dataset_root": dataset_root_folder,
+                    "lang": self.config.training_lang,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "dataset_root": dataset_root_folder,
+                    "lang": self.config.target_lang,
+                    "split": "valid",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "dataset_root": dataset_root_folder,
+                    "lang": self.config.target_lang,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _yield_from_lines(self, lines, lang):
+
+        for i, (
+            tw,
+            pos,
+            char_start_1,
+            char_end_1,
+            char_start_2,
+            char_end_2,
+            context_1,
+            context_2,
+            label,
+        ) in enumerate(lines):
+            _id = f"{lang}_{i}"
+            yield _id, {
+                "id": _id,
+                "target_word": tw,
+                "context_1": context_1,
+                "context_2": context_2,
+                "label": int(label),
+                "target_word_location_1": {
+                    "char_start": int(char_start_1),
+                    "char_end": int(char_end_1),
+                },
+                "target_word_location_2": {
+                    "char_start": int(char_start_2),
+                    "char_end": int(char_end_2)
+                },
+                "pos": pos,
+                "language": lang,
+            }
+
+    def _from_selfcontained_file(self, dataset_root, lang, split):
+        ext_lang = _CODE_TO_LANG_ID[lang]
+        if lang in _WIKT_LANGS:
+            path = os.path.join(
+                dataset_root,
+                "xlwic_wikt",
+                f"{ext_lang}_{lang.lower()}",
+                f"{lang.lower()}_{split}.txt",
+            )
+        elif lang != "EN" and lang in _WN_LANGS:
+            path = os.path.join(
+                dataset_root,
+                "xlwic_wn",
+                f"{ext_lang}_{lang.lower()}",
+                f"{lang.lower()}_{split}.txt",
+            )
+        elif lang == "EN" and lang in _WN_LANGS:
+            path = os.path.join(
+                dataset_root, "wic_english", f"{split}_{lang.lower()}.txt"
+            )
+        with open(path) as lines:
+            all_lines = [line.strip().split("\t") for line in lines]
+        yield from self._yield_from_lines(all_lines, lang)
+
+    def _from_test_files(self, dataset_root, lang, split):
+        ext_lang = _CODE_TO_LANG_ID[lang]
+        if lang in _WIKT_LANGS:
+            path_data = os.path.join(
+                dataset_root,
+                "xlwic_wikt",
+                f"{ext_lang}_{lang.lower()}",
+                f"{lang.lower()}_{split}_data.txt",
+            )
+        elif lang != "EN" and lang in _WN_LANGS:
+            path_data = os.path.join(
+                dataset_root,
+                "xlwic_wn",
+                f"{ext_lang}_{lang.lower()}",
+                f"{lang.lower()}_{split}_data.txt",
+            )
+        path_gold = path_data.replace('_data.txt', '_gold.txt')
+        with open(path_data) as lines:
+            all_lines = [line.strip().split("\t") for line in lines]
+        with open(path_gold) as lines:
+            all_labels = [line.strip() for line in lines]
+        for line, label in zip(all_lines, all_labels):
+            line.append(label)
+        yield from self._yield_from_lines(all_lines, lang)
+
+
+    def _generate_examples(self, dataset_root, lang, split, **kwargs):
+        if split in {"train", "valid"}:
+            yield from self._from_selfcontained_file(dataset_root, lang, split)
+        else:
+            yield from self._from_test_files(dataset_root, lang, split)  
+