Remove enem.p

enem.py

@@ -1,108 +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.
-"""ENEM dataset."""
-
-
-import json
-
-import datasets
-
-
-_CITATION = """\
-"""
-
-_DESCRIPTION = """\
-The study explores the capabilities of Language Models (LMs) in solving 
-high-stakes multiple-choice tests, using the Exame Nacional do Ensino Médio 
-(ENEM) as a case study. The ENEM is a multidisciplinary entrance examination 
-widely adopted by Brazilian universities, which poses challenging tasks for 
-LMs since its questions may span multiple fields of knowledge, requiring 
-understanding of information from diverse domains.
-"""
-
-_HOMEPAGE = "https://github.com/piresramon/gpt-4-enem"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-_URL_2022 = "https://huggingface.co/datasets/maritaca-ai/enem/raw/main/2022.jsonl"
-_URL_2023 = "https://huggingface.co/datasets/maritaca-ai/enem/raw/main/2023.jsonl"
-
-class ENEM(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="2022", data_dir=_URL_2022,  version=VERSION, description="The ENEM 2022 dataset"),
-        datasets.BuilderConfig(name="2023", data_dir=_URL_2023,  version=VERSION, description="The ENEM 2023 dataset"),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "exam": datasets.Value("string"),
-                "IU": datasets.Value("bool"),
-                "question": datasets.Value("string"),
-                "alternatives": datasets.features.Sequence(
-                    datasets.Value("string")
-                ),
-                "figures": datasets.features.Sequence(
-                    datasets.Value("string")
-                ),
-                "description": datasets.features.Sequence(
-                    datasets.Value("string")
-                ),
-                "label": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=f"{_DESCRIPTION}\n{self.config.description}",
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(self.config.data_dir) or ""
-        print(f'{dl_dir}')
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": dl_manager.download_and_extract(dl_dir),
-                    # "data_path": dl_manager.download_and_extract(dl_dir),
-                    "split": datasets.Split.TRAIN,
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                print(f'{data=}')
-                yield key, {
-                    "id": data["id"],
-                    "exam": data["exam"],
-                    "IU": data["IU"],
-                    "question": data["question"],
-                    "alternatives": data["alternatives"],
-                    "figures": data["figures"],
-                    "description": data["description"],
-                    "label": data["label"],
-                }