Delete bsard.py

bsard.py

@@ -1,169 +0,0 @@
-# Copyright 2020 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.
-"""BSARD: A Statutory Article Retrieval Dataset in French"""
-
-import csv
-import json
-import datasets
-
-
-_CITATION = """\
-@inproceedings{louis-spanakis-2022-statutory,
-    title = "A Statutory Article Retrieval Dataset in {F}rench",
-    author = "Louis, Antoine  and Spanakis, Gerasimos",
-    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
-    month = may,
-    year = "2022",
-    address = "Dublin, Ireland",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2022.acl-long.468",
-    doi = "10.18653/v1/2022.acl-long.468",
-    pages = "6789--6803",
-}
-"""
-_DESCRIPTION = """\
-The Belgian Statutory Article Retrieval Dataset (BSARD) is a French native dataset for studying legal information retrieval. 
-BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens 
-and labeled by experienced jurists with relevant articles from the corpus.
-"""
-_HOMEPAGE = "https://github.com/maastrichtlawtech/bsard"
-_LICENSE = "CC BY-NC-SA 4.0"
-_URLS = {
-    "corpus": "https://huggingface.co/datasets/maastrichtlawtech/bsard/resolve/main/articles.csv",
-    "test-questions": "https://huggingface.co/datasets/maastrichtlawtech/bsard/resolve/main/questions_test.csv",
-    "train-questions": "https://huggingface.co/datasets/maastrichtlawtech/bsard/resolve/main/questions_train.csv",
-    "synthetic-questions": "https://huggingface.co/datasets/maastrichtlawtech/bsard/resolve/main/questions_synthetic.csv",
-    "train-negatives": "https://huggingface.co/datasets/maastrichtlawtech/bsard/resolve/main/negatives/bm25_negatives_train.json",
-    "synthetic-negatives": "https://huggingface.co/datasets/maastrichtlawtech/bsard/resolve/main/negatives/bm25_negatives_synthetic.json",
-}
-
-
-class BSARD(datasets.GeneratorBasedBuilder):
-    """BSARD: A Statutory Article Retrieval Dataset in French"""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="corpus", version=VERSION, description="Knowledge corpus of statutory articles"),
-        datasets.BuilderConfig(name="questions", version=VERSION, description="Questions labeled with relevant articles"),
-        datasets.BuilderConfig(name="negatives", version=VERSION, description="Questions labeled with (hard to tell) irrelevant articles"),
-    ]
-    DEFAULT_CONFIG_NAME = "questions"
-
-    def _info(self):
-        if self.config.name == "corpus":
-            features = {
-                "id": datasets.Value("int32"),
-                "article": datasets.Value("string"),
-                "reference": datasets.Value("string"),
-                "law_type": datasets.Value("string"),
-                "description": datasets.Value("string"),
-                "code": datasets.Value("string"),
-                "book": datasets.Value("string"),
-                "part": datasets.Value("string"),
-                "act": datasets.Value("string"),
-                "chapter": datasets.Value("string"),
-                "section": datasets.Value("string"),
-                "subsection": datasets.Value("string"),
-            }
-        elif self.config.name == "questions":
-            features = {
-                "id": datasets.Value("int32"),
-                "question": datasets.Value("string"),
-                "article_ids": datasets.Sequence(datasets.Value("int32")),
-                "category": datasets.Value("string"),
-                "subcategory": datasets.Value("string"),
-                "extra_description": datasets.Value("string"),
-            }
-        elif self.config.name == "negatives":
-            features = {
-                "question_id": datasets.Value("int32"),
-                "article_ids": datasets.Sequence(datasets.Value("int32")),
-            }
-        else:
-            raise ValueError(f"Unknown config name {self.config.name}")
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(features),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        if self.config.name == "corpus":
-            dl_path = dl_manager.download_and_extract(_URLS["corpus"])
-            return [datasets.SplitGenerator(name="corpus", gen_kwargs={"filepath": dl_path})]
-        elif self.config.name == "questions":
-            splits = ["train-questions", "test-questions", "synthetic-questions"]
-            dl_paths = dl_manager.download_and_extract({split: _URLS[split] for split in splits})
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_paths["train-questions"], "split": "train"}),
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": dl_paths["test-questions"], "split": "test"}),
-                datasets.SplitGenerator(name="synthetic", gen_kwargs={"filepath": dl_paths["synthetic-questions"], "split": "synthetic"}),
-            ]
-        elif self.config.name == "negatives":
-            splits = ["train-negatives", "synthetic-negatives"]
-            dl_paths = dl_manager.download_and_extract({split: _URLS[split] for split in splits})
-            return [
-                datasets.SplitGenerator(name="train", gen_kwargs={"filepath": dl_paths["train-negatives"], "split": "train"}),
-                datasets.SplitGenerator(name="synthetic", gen_kwargs={"filepath": dl_paths["synthetic-negatives"], "split": "synthetic"}),
-            ]
-        else:
-            raise ValueError(f"Unknown config name {self.config.name}")
-
-
-    def _generate_examples(self, filepath, split=None):
-        if self.config.name in ["corpus", "questions"]:
-            with open(filepath, encoding="utf-8") as f:
-                data = csv.DictReader(f)
-                for key, row in enumerate(data):
-                    if self.config.name == "corpus":
-                        features = {
-                            "id": int(row["id"]),
-                            "article": row["article"],
-                            "reference": row["reference"],
-                            "law_type": row["law_type"],
-                            "description": row["description"],
-                            "code": row["code"],
-                            "book": row["book"],
-                            "part": row["part"],
-                            "act": row["act"],
-                            "chapter": row["chapter"],
-                            "section": row["section"],
-                            "subsection": row["subsection"],
-                        }
-                    elif self.config.name == "questions":
-                        features = {
-                            "id": int(row["id"]),
-                            "question": row["question"],
-                            "article_ids": [int(num) for num in row["article_ids"].split(",")],
-                            "category": "" if split == "synthetic" else row["category"],
-                            "subcategory": "" if split == "synthetic" else row["subcategory"],
-                            "extra_description": "" if split == "synthetic" else row["extra_description"],
-                        }
-                    else:
-                        raise ValueError(f"Unknown config name {self.config.name}")
-                    yield key, features
-        elif self.config.name == "negatives":
-            with open(filepath, encoding="utf-8") as f:
-                data = json.load(f)
-                for key, (qid, article_ids) in enumerate(data.items()):
-                    features = {
-                        "question_id": int(qid),
-                        "article_ids": article_ids,
-                    }
-                    yield key, features
-        else:
-            raise ValueError(f"Unknown config name {self.config.name}")