add reader for DAST

dataset_infos.json

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+{"dkstance": {"description": "This dataset presents a series of stories on Reddit and the conversation around\nthem, annotated for stance. Stories are also annotated for veracity.\n\nFor more details see https://aclanthology.org/W19-6122/\n", "citation": "@inproceedings{lillie-etal-2019-joint,\n    title = \"Joint Rumour Stance and Veracity Prediction\",\n    author = \"Lillie, Anders Edelbo  and\n      Middelboe, Emil Refsgaard  and\n      Derczynski, Leon\",\n    booktitle = \"Proceedings of the 22nd Nordic Conference on Computational Linguistics\",\n    month = sep # \"{--}\" # oct,\n    year = \"2019\",\n    address = \"Turku, Finland\",\n    publisher = {Link{\"o}ping University Electronic Press},\n    url = \"https://aclanthology.org/W19-6122\",\n    pages = \"208--221\",\n}\n", "homepage": "https://aclanthology.org/W19-6122/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "native_id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "parent_id": {"dtype": "string", "id": null, "_type": "Value"}, "parent_text": {"dtype": "string", "id": null, "_type": "Value"}, "parent_stance": {"num_classes": 4, "names": ["Supporting", "Denying", "Querying", "Commenting"], "id": null, "_type": "ClassLabel"}, "source_id": {"dtype": "string", "id": null, "_type": "Value"}, "source_text": {"dtype": "string", "id": null, "_type": "Value"}, "source_stance": {"num_classes": 4, "names": ["Supporting", "Denying", "Querying", "Commenting"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "dast", "config_name": "dkstance", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2446983, "num_examples": 3122, "dataset_name": "dast"}, "validation": {"name": "validation", "num_bytes": 694731, "num_examples": 1066, "dataset_name": "dast"}, "test": {"name": "test", "num_bytes": 730762, "num_examples": 1060, "dataset_name": "dast"}}, "download_checksums": {"dast.jsonl": {"num_bytes": 4944186, "checksum": "a397a77469fc7fe11c7621f2e619f928a28ece934bd6c11b179b58154a70b450"}}, "download_size": 4944186, "post_processing_size": null, "dataset_size": 3872476, "size_in_bytes": 8816662}}

dkstance.py

@@ -0,0 +1,199 @@
+# coding=utf-8
+# Copyright 2022 Leon Derczynski, HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Danish Stance Dataset DAST"""
+
+from collections import defaultdict
+import glob
+import json
+import os
+import sys
+
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@inproceedings{lillie-etal-2019-joint,
+    title = "Joint Rumour Stance and Veracity Prediction",
+    author = "Lillie, Anders Edelbo  and
+      Middelboe, Emil Refsgaard  and
+      Derczynski, Leon",
+    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
+    month = sep # "{--}" # oct,
+    year = "2019",
+    address = "Turku, Finland",
+    publisher = {Link{\"o}ping University Electronic Press},
+    url = "https://aclanthology.org/W19-6122",
+    pages = "208--221",
+}
+"""
+
+_DESCRIPTION = """\
+This dataset presents a series of stories on Reddit and the conversation around
+them, annotated for stance. Stories are also annotated for veracity.
+
+For more details see https://aclanthology.org/W19-6122/
+"""
+
+_URL = "dast.jsonl"
+
+
+class DastConfig(datasets.BuilderConfig):
+    """BuilderConfig for IPM NEL"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for IPM NEL.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(DastConfig, self).__init__(**kwargs)
+
+
+
+class Dast(datasets.GeneratorBasedBuilder):
+
+
+    """Dast dataset."""
+
+    BUILDER_CONFIGS = [
+        DastConfig(name="dkstance", version=datasets.Version("1.0.0"), description="Danish Stance"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "native_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "parent_id": datasets.Value("string"),
+                    "parent_text": datasets.Value("string"),
+                    "parent_stance": datasets.features.ClassLabel(
+                        names=[
+                            "Supporting",
+                            "Denying",
+                            "Querying",
+                            "Commenting",
+                        ]
+                    ),
+                    "source_id": datasets.Value("string"),
+                    "source_text": datasets.Value("string"),
+                    "source_stance": datasets.features.ClassLabel(
+                        names=[
+                            "Supporting",
+                            "Denying",
+                            "Querying",
+                            "Commenting",
+                        ]
+                    ),
+
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://aclanthology.org/W19-6122/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(_URL)
+        print(downloaded_file)
+        data_files = {
+            "dast": downloaded_file,
+        }
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files['dast'], "split":"train"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files['dast'], "split":"validation"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files['dast'], "split":"test"}),
+        ]
+
+
+    def unpack(self, entry, parent_id = None, source_id = None):
+
+        if isinstance(entry, dict):
+            e = entry['comment']
+            original_id = e['comment_id']
+            text = e['text']
+            parent_id = e['parent_id']
+            parent_stance = e['SDQC_Parent']
+            source_id = e['submission_id']
+            source_stance = e['SDQC_Submission']
+
+            self.texts[original_id] = text
+
+            instance = {
+                "id":self.guid,
+                "native_id":original_id, 
+                "text":text, 
+                "parent_id":parent_id, 
+                "parent_text":self.texts[parent_id], 
+                "parent_stance":parent_stance, 
+                "source_id":source_id, 
+                "source_text":self.texts[source_id], 
+                "source_stance":source_stance,
+            }
+            
+            self.id_mapper[e['comment_id']] = self.guid
+            self.guid += 1
+            yield instance
+
+        elif isinstance(entry, list):
+            for sub_entry in entry:
+                yield from self.unpack(sub_entry, parent_id=parent_id, source_id=source_id)
+
+
+    def process_block(self, block):
+
+        j = json.loads(block)
+        s = j['redditSubmission']
+        descr = s['RumourDescription']
+        source_id = s['submission_id']
+        #print(i, '', descr, '', '', s['title'], s['SourceSDQC'])
+        self.id_mapper[source_id] = self.guid
+        self.guid += 1
+        self.texts[source_id] = s['title']
+
+        yield from self.unpack(j['branches'], source_id = 0, parent_id = 0)
+
+
+    def _generate_examples(self, filepath, split):
+        logger.info("⏳ Generating %s examples from = %s", (split, filepath))
+
+        def _deleted():
+            return "[deleted]"
+
+        self.guid = 0
+        self.id_mapper = {}
+        self.texts = defaultdict(_deleted)
+
+        partition_sources = ()
+        if split == 'train':
+            partition_sources = ('8sjevz', 'a0954m', 'a1gsmt', 'a2fpjr', 'a6o3us', 'ax70y5', 'axnshu', 'b23eat', 'b2xrgd', 'b72gok', 'b7aybw', 'b7ohqt', 'bb9iqt')
+        elif split == 'validation':
+            partition_sources = ('6v1ivh', '76y6rb', '7r9ouo', '8192oe', '83l9nm', '8agt1s', '8clb74', '8k6lcb')            
+        elif split == 'test':
+            partition_sources = ('3qc12m', '3ud5z9', '53u5j7', '5emjyw', '5pfq1r', '5t1h6y', '60il0b', '67c2zf', '6jqtkm', '6nz7dy', '6szxwj', '6tm5kp')
+
+        with open(filepath, 'r', encoding="utf-8") as dastfile:
+            for line in dastfile:
+                instances = self.process_block(line.strip())
+                for instance in instances:
+                    if instance['source_id'] in partition_sources:
+                        yield instance['id'], instance