upload hubscripts/scicite_hub.py to hub from bigbio repo

scicite.py

@@ -0,0 +1,235 @@
+# coding=utf-8
+# Copyright 2022 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.
+
+"""
+A dataset loader for the SciCite dataset.
+
+SciCite is a dataset of 11K manually annotated citation intents based on
+citation context in the computer science and biomedical domains.
+
+Some of the code in this module is based on the corresponding module in the
+datasets library.
+https://github.com/huggingface/datasets/blob/master/datasets/scicite/scicite.py
+
+In the source schema, we follow the datasets implementation and replace
+missing values.
+TODO: Use standard BigBio missing values.
+"""
+
+import json
+import os
+from typing import Dict, List, Tuple
+
+import datasets
+import numpy as np
+
+from .bigbiohub import text.features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@inproceedings{cohan:naacl19,
+  author    = {Arman Cohan and Waleed Ammar and Madeleine van Zuylen and Field Cady},
+  title     = {Structural Scaffolds for Citation Intent Classification in Scientific Publications},
+  booktitle = {Conference of the North American Chapter of the Association for Computational Linguistics},
+  year      = {2019},
+  url       = {https://aclanthology.org/N19-1361/},
+  doi       = {10.18653/v1/N19-1361},
+}
+"""
+
+_DATASETNAME = "scicite"
+_DISPLAYNAME = "SciCite"
+
+_DESCRIPTION = """\
+SciCite is a dataset of 11K manually annotated citation intents based on
+citation context in the computer science and biomedical domains.
+"""
+
+_HOMEPAGE = "https://allenai.org/data/scicite"
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {
+    _DATASETNAME: "https://s3-us-west-2.amazonaws.com/ai2-s2-research/scicite/scicite.tar.gz",
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class SciciteDataset(datasets.GeneratorBasedBuilder):
+    """SciCite is a dataset of 11K manually annotated citation intents based on
+    citation context in the computer science and biomedical domains."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    # You will be able to load the "source" or "bigbio" configurations with
+    # ds_source = datasets.load_dataset('scicite', name='source')
+    # ds_bigbio = datasets.load_dataset('scicite', name='bigbio')
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="scicite_source",
+            version=SOURCE_VERSION,
+            description="SciCite source schema",
+            schema="source",
+            subset_id="scicite",
+        ),
+        BigBioConfig(
+            name="scicite_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="SciCite BigBio schema",
+            schema="bigbio_text",
+            subset_id="scicite",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "scicite_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "source": datasets.Value("string"),
+                    "citeStart": datasets.Value("int64"),
+                    "sectionName": datasets.Value("string"),
+                    "string": datasets.Value("string"),
+                    "citeEnd": datasets.Value("int64"),
+                    "label": datasets.features.ClassLabel(
+                        names=["method", "background", "result"]
+                    ),
+                    "label_confidence": datasets.Value("float"),
+                    "label2": datasets.features.ClassLabel(
+                        names=["supportive", "not_supportive", "cant_determine", "none"]
+                    ),
+                    "label2_confidence": datasets.Value("float"),
+                    "citingPaperId": datasets.Value("string"),
+                    "citedPaperId": datasets.Value("string"),
+                    "isKeyCitation": datasets.Value("bool"),
+                    "id": datasets.Value("string"),
+                    "unique_id": datasets.Value("string"),
+                    "excerpt_index": datasets.Value("int64"),
+                }
+            )
+        elif self.config.schema == "bigbio_text":
+            features = text.features
+        else:
+            raise ValueError("Unrecognized schema: %s" % self.config.schema)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "scicite", "train.jsonl"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "scicite", "test.jsonl"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "scicite", "dev.jsonl"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        with open(filepath, "r") as data_file:
+            examples = [json.loads(line) for line in data_file]
+
+            # Preprocesses examples
+            keys = set()
+            for example in examples:
+                # Fixes duplicate keys
+                if example["unique_id"] in keys:
+                    example["unique_id"] = example["unique_id"] + "_duplicate"
+                else:
+                    keys.add(example["unique_id"])
+
+            if self.config.schema == "source":
+                for example in examples:
+                    yield str(example["unique_id"]), {
+                        "string": example["string"],
+                        "label": str(example["label"]),
+                        "sectionName": str(example["sectionName"]),
+                        "citingPaperId": str(example["citingPaperId"]),
+                        "citedPaperId": str(example["citedPaperId"]),
+                        "excerpt_index": int(example["excerpt_index"]),
+                        "isKeyCitation": bool(example["isKeyCitation"]),
+                        "label2": str(example.get("label2", "none")),
+                        "citeEnd": _safe_int(example["citeEnd"]),
+                        "citeStart": _safe_int(example["citeStart"]),
+                        "source": str(example["source"]),
+                        "label_confidence": float(
+                            example.get("label_confidence", np.nan)
+                        ),
+                        "label2_confidence": float(
+                            example.get("label2_confidence", np.nan)
+                        ),
+                        "id": str(example["id"]),
+                        "unique_id": str(example["unique_id"]),
+                    }
+
+            elif self.config.schema == "bigbio_text":
+                for example in examples:
+                    if "label2" in example:
+                        labels = [example["label"], example["label2"]]
+                    else:
+                        labels = [example["label"]]
+
+                    yield str(example["unique_id"]), {
+                        "id": example["unique_id"],
+                        "document_id": example["citingPaperId"],
+                        "text": example["string"],
+                        "labels": labels,
+                    }
+
+
+def _safe_int(a):
+    try:
+        # skip NaNs
+        return int(a)
+    except ValueError:
+        return -1