add dataloader

ans-stance.py

@@ -0,0 +1,101 @@
+# 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.
+"""DataLoader for ANS, an Arabic News Stance corpus"""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@inproceedings{,
+    title = "Stance Prediction and Claim Verification: An {A}rabic Perspective", 
+    author = "Khouja, Jude",
+    booktitle = "Proceedings of the Third Workshop on Fact Extraction and {VER}ification ({FEVER})",
+    year = "2020",
+    address = "Seattle, USA",
+    publisher = "Association for Computational Linguistics",
+}
+"""
+
+_DESCRIPTION = """\
+The dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = "apache-2.0"
+
+class ANSStanceConfig(datasets.BuilderConfig):
+
+    def __init__(self, **kwargs):
+        super(ANSStanceConfig, self).__init__(**kwargs)
+
+class ANSStance(datasets.GeneratorBasedBuilder):
+    """ANS dataset made in triples of (s1, s2, stance)"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        ANSStanceConfig(name="stance", version=VERSION, description=""),
+    ]
+    
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "s1": datasets.Value("string"),
+                "s2": datasets.Value("string"),
+                "stance": datasets.features.ClassLabel(
+                    names=[
+                        "disagree",
+                        "agree",
+                        "other"
+                    ]
+                )
+            }
+        )
+
+        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("ans_train.csv")
+        valid_text = dl_manager.download_and_extract("ans_dev.csv")
+        test_text  = dl_manager.download_and_extract("ans_test.csv")
+        
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,      gen_kwargs={"filepath": train_text, "split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_text, "split": "validation"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST,       gen_kwargs={"filepath": test_text,  "split": "test"}),
+        ]
+
+    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["s1"] = instance.pop("s1")
+                instance["s2"] = instance.pop("s2")
+                instance["stance"] = instance.pop("stance")
+                instance['id'] = str(guid)
+                yield guid, instance
+                guid += 1