remove underscore dataloader

nlpcc_stance.py

@@ -1,97 +0,0 @@
-# Copyright 2022 Mads Kongsbak and Leon Derczynski
-#
-# 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.
-"""NLPCC Shared Task 4, Stance Detection in Chinese Microblogs (Task A)"""
-
-
-import csv
-import json
-import os
-
-import datasets
-
-_CITATION = """\
-@incollection{xu2016overview,
-  title={Overview of nlpcc shared task 4: Stance detection in chinese microblogs},
-  author={Xu, Ruifeng and Zhou, Yu and Wu, Dongyin and Gui, Lin and Du, Jiachen and Xue, Yun},
-  booktitle={Natural language understanding and intelligent applications},
-  pages={907--916},
-  year={2016},
-  publisher={Springer}
-}
-"""
-
-_DESCRIPTION = """\
-This is a stance prediction dataset in Chinese.
-The data is that from a shared task, stance detection in Chinese microblogs, in NLPCC-ICCPOL 2016. It covers Task A, a mandatory supervised task which detects stance towards five targets of interest with given labeled data. 
-"""
-
-_HOMEPAGE = "http://tcci.ccf.org.cn/conference/2016/pages/page05_evadata.html"
-
-_LICENSE = "cc-by-4.0"
-
-class NLPCCConfig(datasets.BuilderConfig):
-
-    def __init__(self, **kwargs):
-        super(NLPCCConfig, self).__init__(**kwargs)
-
-class NLPCCStance(datasets.GeneratorBasedBuilder):
-    """The NLPCC Shared Task 4 dataset regarding Stance Detection in Chinese Microblogs (Task A)"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        NLPCCConfig(name="task_a", version=VERSION, description="Task A, the supervised learning task."),
-    ]
-    
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "target": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "stance": datasets.features.ClassLabel(
-                    names=[
-                        "AGAINST",
-                        "FAVOR",
-                        "NONE",
-                    ]
-                )
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        train_text = dl_manager.download_and_extract("taska_train.csv")
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN,      gen_kwargs={"filepath": train_text, "split": "train"}),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter=",")
-            guid = 0
-            for instance in reader:
-                instance["target"] = instance.pop("target")
-                instance["text"] = instance.pop("text")
-                instance["stance"] = instance.pop("stance")
-                instance['id'] = str(guid)
-                yield guid, instance
-                guid += 1