Create scidtb.py

scidtb.py

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+import glob
+import json
+import os
+from dataclasses import dataclass
+
+import datasets
+from datasets import BuilderConfig, SplitGenerator
+
+_CITATION = """\
+@article{yang2018scidtb,
+    title={Scidtb: Discourse dependency treebank for scientific abstracts},
+    author={Yang, An and Li, Sujian},
+    journal={arXiv preprint arXiv:1806.03653},
+    year={2018}
+  }
+"""
+
+_DESCRIPTION = """Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question
+ answering. SciDTB is a domain-specific discourse treebank annotated on scientific articles.
+ Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is
+ flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework,
+ annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating
+ discourse dependency parsers, on which we provide several baselines as fundamental work."""
+
+_URL = "https://codeload.github.com/PKU-TANGENT/SciDTB/zip/refs/heads/master"
+_HOMEPAGE = "https://github.com/PKU-TANGENT/SciDTB"
+
+
+@dataclass
+class SciDTBConfig(BuilderConfig):
+    """BuilderConfig for SciDTB."""
+
+    def __init__(self, subdirectory_mapping, encoding, **kwargs):
+        super(SciDTBConfig, self).__init__(**kwargs)
+        self.subdirectory_mapping = subdirectory_mapping
+        self.encoding = encoding
+
+
+class SciDTBDataset(datasets.GeneratorBasedBuilder):
+    """Scientific Discourse Treebank Dataset"""
+
+    BUILDER_CONFIGS = [
+        SciDTBConfig(
+            name="SciDTB",
+            version=datasets.Version("1.0.0", ""),
+            description=_DESCRIPTION,
+            subdirectory_mapping={
+                "train": "SciDTB-master/dataset/train",
+                "dev": "SciDTB-master/dataset/dev/gold",
+                "test": "SciDTB-master/dataset/test/gold",
+            },
+            encoding="utf-8-sig",
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "root": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int32"),
+                        "parent": datasets.Value("int32"),
+                        "text": datasets.Value("string"),
+                        "relation": datasets.Value("string"),
+                    }
+                ),
+                "file_name": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            SplitGenerator(
+                name=split,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "dir_path": os.path.join(data_dir, subdir),
+                },
+            )
+            for split, subdir in self.config.subdirectory_mapping.items()
+        ]
+
+    def _generate_examples(self, dir_path):
+        _files = glob.glob(f"{dir_path}/*.dep")
+        for file_path in _files:
+            with open(file_path, mode="r", encoding=self.config.encoding) as f:
+                annotations = json.load(f)
+                annotations["file_name"] = os.path.basename(file_path)
+            yield annotations["file_name"], annotations