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 *.pb filter=lfs diff=lfs merge=lfs -text
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+xlwic_de_de/xlwic-train.parquet filter=lfs diff=lfs merge=lfs -text
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+xlwic_de_de/xlwic-test.parquet filter=lfs diff=lfs merge=lfs -text
+xlwic_en_de/xlwic-validation.parquet filter=lfs diff=lfs merge=lfs -text
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+xlwic_en_fr/xlwic-validation.parquet filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,69 +0,0 @@
----
-annotations_creators:
-- expert-generated
-extended:
-- original
-language_creators:
-- found
-language:
-- en
-- bg
-- zh
-- hr
-- da
-- nl
-- et
-- fa
-- ja
-- ko
-- it
-- fr
-- de
-license:
-- cc-by-nc-4.0
-multilinguality:
-- multilingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- semantic-similarity-classification
----
-
-# XL-WiC
-Huggingface dataset for the XL-WiC paper [https://www.aclweb.org/anthology/2020.emnlp-main.584.pdf](https://www.aclweb.org/anthology/2020.emnlp-main.584.pdf).
-Please refer to the official [website](https://pilehvar.github.io/xlwic/) for more information.
-
-
-## Configurations
-When loading one of the XL-WSD datasets one has to specify the training language and the target language (on which dev and test will be performed). 
-Please refer to [Languages](#languages) section to see in which languages training data is available.
-For example, we can load the dataset having English as training language and Italian as target language as follows:
-```python
-from datasets import load_dataset
-dataset = load_dataset('pasinit/xlwic', 'en_it')
-```
-
-## Languages
-**Training data**
-- en (English)
-- fr (French)
-- de (German)
-- it (Italian)
-
-**Dev & Test data**
-- fr (French)
-- de (German)
-- it (Italian)
-- bg (Bulgarian)
-- zh (Chinese)
-- hr (Croatian)
-- da (Danish)
-- nl (Dutch)
-- et (Estonian)
-- fa (Farsi)
-- ja (Japanesse)
-- ko (Korean)

dataset_infos.json

@@ -1 +0,0 @@
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The task is to identify if the occurrences of w in the two contexts correspond to the same meaning or not.\n\nXL-WiC provides dev and test sets in the following 12 languages:\n\nBulgarian (BG)\nDanish (DA)\nGerman (DE)\nEstonian (ET)\nFarsi (FA)\nFrench (FR)\nCroatian (HR)\nItalian (IT)\nJapanese (JA)\nKorean (KO)\nDutch (NL)\nChinese (ZH)\nand training sets in the following 3 languages:\n\nGerman (DE)\nFrench (FR)\nItalian (IT)\n", "citation": "@inproceedings{raganato-etal-2020-xl-wic,\n  title={XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization},\n  author={Raganato, Alessandro and Pasini, Tommaso and Camacho-Collados, Jose and Pilehvar, Mohammad Taher},\n  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},\n  pages={7193--7206},\n  year={2020}\n}\n", "homepage": "https://pilehvar.github.io/xlwic/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context_1": {"dtype": "string", "id": null, "_type": "Value"}, "context_2": {"dtype": "string", "id": null, "_type": "Value"}, "target_word": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "target_word_location_1": {"char_start": {"dtype": "int32", "id": null, "_type": "Value"}, "char_end": {"dtype": "int32", "id": null, "_type": "Value"}}, "target_word_location_2": {"char_start": {"dtype": "int32", "id": null, "_type": "Value"}, "char_end": {"dtype": "int32", "id": null, "_type": "Value"}}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xlwic", "config_name": "xlwic_fr_fr", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10210148, "num_examples": 39428, "dataset_name": "xlwic"}, "validation": {"name": "validation", "num_bytes": 2045677, "num_examples": 8588, "dataset_name": "xlwic"}, "test": {"name": "test", "num_bytes": 5615634, "num_examples": 22232, "dataset_name": "xlwic"}}, "download_checksums": {"https://pilehvar.github.io/xlwic/data/xlwic_datasets.zip": {"num_bytes": 18748158, "checksum": "40d12d338c90937eab820f24206c06d58b21d996b1d5c7bf69b3f8c65cf7eaff"}}, "download_size": 18748158, "post_processing_size": null, "dataset_size": 17871459, "size_in_bytes": 36619617}, "xlwic_de_de": {"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. 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The task is to identify if the occurrences of w in the two contexts correspond to the same meaning or not.\n\nXL-WiC provides dev and test sets in the following 12 languages:\n\nBulgarian (BG)\nDanish (DA)\nGerman (DE)\nEstonian (ET)\nFarsi (FA)\nFrench (FR)\nCroatian (HR)\nItalian (IT)\nJapanese (JA)\nKorean (KO)\nDutch (NL)\nChinese (ZH)\nand training sets in the following 3 languages:\n\nGerman (DE)\nFrench (FR)\nItalian (IT)\n", "citation": "@inproceedings{raganato-etal-2020-xl-wic,\n  title={XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization},\n  author={Raganato, Alessandro and Pasini, Tommaso and Camacho-Collados, Jose and Pilehvar, Mohammad Taher},\n  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},\n  pages={7193--7206},\n  year={2020}\n}\n", "homepage": "https://pilehvar.github.io/xlwic/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context_1": {"dtype": "string", "id": null, "_type": "Value"}, "context_2": {"dtype": "string", "id": null, "_type": "Value"}, "target_word": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "target_word_location_1": {"char_start": {"dtype": "int32", "id": null, "_type": "Value"}, "char_end": {"dtype": "int32", "id": null, "_type": "Value"}}, "target_word_location_2": {"char_start": {"dtype": "int32", "id": null, "_type": "Value"}, "char_end": {"dtype": "int32", "id": null, "_type": "Value"}}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xlwic", "config_name": "xlwic_de_de", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 12625723, "num_examples": 48042, "dataset_name": "xlwic"}, "validation": {"name": "validation", "num_bytes": 2277398, "num_examples": 8870, "dataset_name": "xlwic"}, "test": {"name": "test", "num_bytes": 6360304, "num_examples": 24268, "dataset_name": "xlwic"}}, "download_checksums": {"https://pilehvar.github.io/xlwic/data/xlwic_datasets.zip": {"num_bytes": 18748158, "checksum": "40d12d338c90937eab820f24206c06d58b21d996b1d5c7bf69b3f8c65cf7eaff"}}, "download_size": 18748158, "post_processing_size": null, "dataset_size": 21263425, "size_in_bytes": 40011583}}

xlwic.py

@@ -1,231 +0,0 @@
-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)  
-

xlwic_de_de/xlwic-test.parquet

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