rename dataloader

ans_stance.py

@@ -1,101 +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.
-"""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

dataset_infos.json

@@ -1 +0,0 @@
-{"stance": {"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:\n", "citation": "@inproceedings{,\n    title = \"Stance Prediction and Claim Verification: An {A}rabic Perspective\", \n    author = \"Khouja, Jude\",\n    booktitle = \"Proceedings of the Third Workshop on Fact Extraction and {VER}ification ({FEVER})\",\n    year = \"2020\",\n    address = \"Seattle, USA\",\n    publisher = \"Association for Computational Linguistics\",\n}\n", "homepage": "", "license": "apache-2.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "s1": {"dtype": "string", "id": null, "_type": "Value"}, "s2": {"dtype": "string", "id": null, "_type": "Value"}, "stance": {"num_classes": 3, "names": ["disagree", "agree", "other"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ans_stance", "config_name": "stance", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 531232, "num_examples": 2652, "dataset_name": "ans_stance"}, "validation": {"name": "validation", "num_bytes": 153125, "num_examples": 755, "dataset_name": "ans_stance"}, "test": {"name": "test", "num_bytes": 76099, "num_examples": 379, "dataset_name": "ans_stance"}}, "download_checksums": {"ans_train.csv": {"num_bytes": 507122, "checksum": "b5486ca4d5f434aaab5025953aeccebffc47181e7918695456fbc26af5e5c7d4"}, "ans_dev.csv": {"num_bytes": 146237, "checksum": "ca7fca0ecaaf5b248d002b104bee54e2d56c32aab9f5fbb7d3ee71d4d88a7621"}, "ans_test.csv": {"num_bytes": 72660, "checksum": "371622ff09260064297197a439fd62ac91c932d8975f3333815746db57f82920"}}, "download_size": 726019, "post_processing_size": null, "dataset_size": 760456, "size_in_bytes": 1486475}}