Create propsegment-retrieval.py

propsegment-retrieval.py

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+# 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 = """\
+@article{chen2023subsentence,
+  title={Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations},
+  author={Sihao Chen and Hongming Zhang and Tong Chen and Ben Zhou and Wenhao Yu and Dian Yu and Baolin Peng and Hongwei Wang and Dan Roth and Dong Yu},
+  journal={arXiv preprint arXiv:2311.04335},
+  year={2023},
+  URL = {https://arxiv.org/pdf/2311.04335.pdf}
+}
+
+@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 contains the processed dataset for the atomic fact retrieval task of the "PropSegment" dataset.
+
+The task features a test set of 8,865 queries propositions. 
+Each query proposition corresponds to 1-2 ground truth propositions from another document. 
+In total, there are 43,299 target candidate propositions. 
+Note that the query propositions are also included in the target set, so during evaluation, the query needs to be removed from the retrieved candidates.    
+
+Check out more details in our paper -- https://arxiv.org/pdf/2311.04335.pdf. 
+"""
+
+_HOMEPAGE = "https://github.com/schen149/sub-sentence-encoder"
+
+_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)
+_URLS = {
+    "targets": {
+        "test": "propsegment_targets_all.jsonl",
+    },
+    "queries": {
+        "test": "propsegment_queries_all.jsonl",
+    }
+}
+
+_CONFIG_TO_FILENAME = {
+    "targets": "propsegment_targets_all",
+    "queries": "propsegment_queries_all"
+}
+
+class PropSegmentRetrieval(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="targets", version=VERSION, description="Query propositions of the atomic fact retrieval task"),
+        datasets.BuilderConfig(name="queries", version=VERSION, description="Target candidate propositions of the atomic fact retrieval task"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "queries"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        if self.config.name == "queries":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "sentence_text": datasets.Value("string"),
+                    "spans": datasets.Value("string"),
+                    "labels": datasets.features.Sequence(datasets.Value("string")),
+                    "tokens": datasets.features.Sequence(
+                        {"text": datasets.Value("string"), "character_offset_of_token_in_sentence": datasets.Value("int32"),}
+                    ),
+                    "token_indices": datasets.features.Sequence(datasets.Value("int32"))
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "sentence_text": datasets.Value("string"),
+                    "spans": datasets.Value("string"),
+                    "tokens": datasets.features.Sequence(
+                        {"text": datasets.Value("string"), "character_offset_of_token_in_sentence": datasets.Value("int32"),}
+                    ),
+                    "token_indices": datasets.features.Sequence(datasets.Value("int32"))
+                }
+            )
+        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(urls)
+        file_prefix = _CONFIG_TO_FILENAME[config_name]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "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 == "queries":
+                    yield key, {
+                        "id": data["id"],
+                        "sentence_text": data["sentence_text"],
+                        "spans": data["spans"],
+                        "label": data["label"],
+                        "tokens": data["tokens"],
+                        "token_indices": data["token_indices"],
+                    }
+                else:
+                    yield key, {
+                        "id": data["id"],
+                        "sentence_text": data["sentence_text"],
+                        "spans": data["spans"],
+                        "tokens": data["tokens"],
+                        "token_indices": data["token_indices"],
+                    }