rename dataloadeR

dataset_infos.json

@@ -1 +0,0 @@
-{"task_a": {"description": "This is a stance prediction dataset in Chinese.\nThe 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. \n", "citation": "@incollection{xu2016overview,\n  title={Overview of nlpcc shared task 4: Stance detection in chinese microblogs},\n  author={Xu, Ruifeng and Zhou, Yu and Wu, Dongyin and Gui, Lin and Du, Jiachen and Xue, Yun},\n  booktitle={Natural language understanding and intelligent applications},\n  pages={907--916},\n  year={2016},\n  publisher={Springer}\n}\n", "homepage": "", "license": "cc-by-4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "stance": {"num_classes": 3, "names": ["AGAINST", "FAVOR", "NONE"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "nlpcc_stance", "config_name": "task_a", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 775152, "num_examples": 2986, "dataset_name": "nlpcc_stance"}}, "download_checksums": {"taska_train.csv": {"num_bytes": 744329, "checksum": "8d27fe452e00bbcb5f20c3d7eaac21ef9a6df27598db6c87b79353e2fdd96e41"}}, "download_size": 744329, "post_processing_size": null, "dataset_size": 775152, "size_in_bytes": 1519481}}

nlpcc-stance.py

@@ -0,0 +1,97 @@
+# 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 = ""
+
+_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