adding dataset loader file

propsegment.py

@@ -0,0 +1,181 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+
+"""PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@inproceedings{chen2023propsegment,
+    title = "{PropSegmEnt}: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition",
+    author = "Chen, Sihao and Buthpitiya, Senaka and Fabrikant, Alex and Roth, Dan  and Schuster, Tal",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
+    year = "2023",
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This is a reproduced (i.e. after web-crawling) and processed version of the "PropSegment" dataset from Google Research.
+
+Since the News portion of the dataset is released only via urls, we reconstruct the dataset by crawling. Overall, ~96% 
+of the dataset can be reproduced, and the rest ~4% either have url no longer valid, or sentences that have been edited 
+(i.e. cannot be aligned with the orignial dataset).
+
+PropSegment (Proposition-level Segmentation and Entailment) is a large-scale, human annotated dataset for segmenting 
+English text into propositions, and recognizing proposition-level entailment relations --- whether a different, related 
+document entails each proposition, contradicts it, or neither.
+
+The original dataset features >45k human annotated propositions, i.e. individual semantic units within sentences, as 
+well as >35k entailment labels between propositions and documents.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/PropSegmEnt"
+
+_LICENSE = "CC-BY-4.0"
+
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://raw.githubusercontent.com/schen149/PropSegmEnt/main/"
+_URLS = {
+    "segmentation": {
+        "train": _URL + "proposition_segmentation.train.jsonl",
+        "dev": _URL + "proposition_segmentation.dev.jsonl",
+        "test": _URL + "proposition_segmentation.test.jsonl",
+    },
+    "nli": {
+        "train": _URL + "propnli.train.jsonl",
+        "dev": _URL + "propnli.dev.jsonl",
+        "test": _URL + "propnli.test.jsonl",
+    }
+}
+
+_CONFIG_TO_FILENAME = {
+    "segmentation": "proposition_segmentation",
+    "nli": "propnli"
+}
+
+class PropSegment(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="segmentation", version=VERSION, description="This part of my dataset covers a first domain"),
+        datasets.BuilderConfig(name="nli", version=VERSION, description="This part of my dataset covers a second domain"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "segmentation"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        if self.config.name == "segmentation":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "propositions": datasets.Value("string"),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "hypothesis": datasets.Value("string"),
+                    "premise": datasets.Value("string"),
+                    "label": datasets.Value("string")
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        config_name = self.config.name
+        urls = _URLS[config_name]
+        data_dir = dl_manager.download_and_extract(urls)
+        file_prefix = _CONFIG_TO_FILENAME[config_name]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "{}.train.jsonl".format(file_prefix)),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "{}.dev.jsonl".format(file_prefix)),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "{}.test.jsonl".format(file_prefix)),
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "segmentation":
+                    yield key, {
+                        "sentence": data["sentence"],
+                        "propositions": data["propositions"],
+                    }
+                else:
+                    yield key, {
+                        "hypothesis": data["hypothesis"],
+                        "premise": data["premise"],
+                        "label": data["label"],
+                    }