first upload

csabstruct.py

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+"""
+Dataset from https://github.com/allenai/sequential_sentence_classification
+
+Dataset maintainer: @soldni
+"""
+
+
+import json
+from typing import Iterable, Sequence, Tuple
+
+import datasets
+from datasets.builder import BuilderConfig, GeneratorBasedBuilder
+from datasets.info import DatasetInfo
+from datasets.splits import Split, SplitGenerator
+from datasets.utils.logging import get_logger
+
+LOGGER = get_logger(__name__)
+
+
+_NAME = "CSAbstruct"
+_CITATION = """\
+@inproceedings{Cohan2019EMNLP,
+  title={Pretrained Language Models for Sequential Sentence Classification},
+  author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld},
+  year={2019},
+  booktitle={EMNLP},
+}
+"""
+_LICENSE = "Apache License 2.0"
+_DESCRIPTION = """\
+As a step toward better document-level understanding, we explore \
+classification of a sequence of sentences into their corresponding \
+categories, a task that requires understanding sentences in context \
+of the document. Recent successful models for this task have used \
+hierarchical models to contextualize sentence representations, and \
+Conditional Random Fields (CRFs) to incorporate dependencies between \
+subsequent labels. In this work, we show that pretrained language \
+models, BERT (Devlin et al., 2018) in particular, can be used for \
+this task to capture contextual dependencies without the need for \
+hierarchical encoding nor a CRF. Specifically, we construct a joint \
+sentence representation that allows BERT Transformer layers to \
+directly utilize contextual information from all words in all \
+sentences. Our approach achieves state-of-the-art results on four \
+datasets, including a new dataset of structured scientific abstracts.
+"""
+_HOMEPAGE = "https://github.com/allenai/sequential_sentence_classification"
+_VERSION = "1.0.0"
+
+_URL = (
+    "https://raw.githubusercontent.com/allenai/"
+    "sequential_sentence_classification/master/"
+)
+
+_SPLITS = {
+    Split.TRAIN: _URL + "data/CSAbstruct/train.jsonl",
+    Split.VALIDATION: _URL + "data/CSAbstruct/dev.jsonl",
+    Split.TEST: _URL + "data/CSAbstruct/test.jsonl",
+}
+
+
+class CSAbstruct(GeneratorBasedBuilder):
+    """CSAbstruct"""
+
+    BUILDER_CONFIGS = [
+        BuilderConfig(
+            name=_NAME,
+            version=datasets.Version(_VERSION),
+            description=_DESCRIPTION,
+        )
+    ]
+
+    def _info(self) -> DatasetInfo:
+        class_labels = ["background", "method", "objective", "other", "result"]
+
+        features = datasets.Features(
+            {
+                "abstract_id": datasets.Value("string"),
+                "sentences": [datasets.Value("string")],
+                "labels": [datasets.ClassLabel(names=class_labels)],
+                "confs": [datasets.Value("float")],
+            }
+        )
+
+        return DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> Sequence[SplitGenerator]:
+        archive = dl_manager.download(_SPLITS)
+
+        return [
+            SplitGenerator(
+                name=split_name,  # type: ignore
+                gen_kwargs={
+                    "split_name": split_name,
+                    "filepath": archive[split_name],  # type: ignore
+                },
+            )
+            for split_name in _SPLITS
+        ]
+
+    def _generate_examples(
+        self, split_name: str, filepath: str
+    ) -> Iterable[Tuple[str, dict]]:
+        """This function returns the examples in the raw (text) form."""
+
+        LOGGER.info(f"generating examples from documents in {filepath}...")
+
+        with open(filepath, mode="r", encoding="utf-8") as f:
+            data = [json.loads(ln) for ln in f]
+
+        for i, row in enumerate(data):
+            row["abstract_id"] = f"{split_name}_{i:04d}"
+            yield row["abstract_id"], row