Delete tmlu.py

tmlu.py

@@ -1,121 +0,0 @@
-# coding=utf-8
-# 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.
-
-
-import json
-
-import datasets
-
-
-_CITATION = """\
-@misc{taiwanllama,
-    author={Lin, Yen-Ting and Chen, Yun-Nung},
-    title={Language Models for Taiwanese Culture},
-    year={2023},
-    url={https://github.com/MiuLab/Taiwan-LLaMa},
-    note={Code and models available at https://github.com/MiuLab/Taiwan-LLaMa},
-}
-"""
-
-_DESCRIPTION = """\
-This is a Traditional Mandarin multitask test consisting of multiple-choice questions from various branches of knowledge in Taiwan.
-"""
-
-_HOMEPAGE = "https://github.com/MiuLab/Taiwan-LLaMa"
-
-_URL = "https://huggingface.co/datasets/miulab/TMLU/raw/main/{subject}.jsonl"
-
-# TODO: add "all" subject
-_SUBJECTS = [
-    "AST_biology",
-    "AST_chemistry",
-    "AST_chinese",
-    "AST_physics",
-    "GSAT_chinese",
-]
-
-
-class Tmlu(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name=sub, version=datasets.Version("0.1.0"), description=f"TMLU Subject {sub}"
-        )
-        for sub in _SUBJECTS
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "question": datasets.Value("string"),
-                "correct_choices": datasets.features.Sequence(datasets.Value("string")),
-                "incorrect_choices": datasets.features.Sequence(datasets.Value("string")),
-                "metadata": {
-                    "timestamp": datasets.Value("string"),
-                    "source": datasets.Value("string"),
-                },
-                "human_evaluation": {
-                    "quality": datasets.Value("string"),
-                    "comments": datasets.Value("string"),
-                },
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        archive = dl_manager.download(
-            {
-                self.config.name: _URL.format(subject=self.config.name),
-            }
-        )
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"path": archive[self.config.name], "split": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, path, split):
-        with open(path, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                yield id_, {
-                    "id": data["id"],
-                    "question": data["question"],
-                    "correct_choices": data["correct_choices"],
-                    "incorrect_choices": data["incorrect_choices"],
-                    "metadata": data["metadata"],
-                    "human_evaluation": data["human_evaluation"],
-                }
-    # def _generate_examples(self, iter_archive, split):
-    #     """Yields examples as (key, example) tuples."""
-    #     n_yielded_files = 0
-    #     for id_file, (path, file) in enumerate(iter_archive):
-    #         if f"data/{split}/" in path:
-    #             if split == "auxiliary_train" or f"{self.config.name}_{split}.csv" in path or self.config.name == "all":
-    #                 subset = path.split("/")[-1].rsplit("_",1)[0] if split != "auxiliary_train" else ""
-    #                 n_yielded_files += 1
-    #                 lines = (line.decode("utf-8") for line in file)
-    #                 reader = csv.reader(lines)
-    #                 for id_line, data in enumerate(reader):
-    #                     yield f"{id_file}_{id_line}", {"question": data[0], "choices": data[1:5], "answer": data[5], "subject": subset}
-    #                 if (n_yielded_files == 8 or split != "auxiliary_train") and self.config.name != "all":
-    #                     break