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  1. scicite.py +0 -154
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- # coding=utf-8
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- # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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-
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- # Lint as: python3
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- """TODO(scicite): Add a description here."""
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-
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-
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- import json
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-
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- import datasets
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-
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-
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- _CITATION = """
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- @InProceedings{Cohan2019Structural,
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- author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady},
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- title={Structural Scaffolds for Citation Intent Classification in Scientific Publications},
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- booktitle={NAACL},
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- year={2019}
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- }
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- """
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-
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- _DESCRIPTION = """
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- This is a dataset for classifying citation intents in academic papers.
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- The main citation intent label for each Json object is specified with the label
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- key while the citation context is specified in with a context key. Example:
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- {
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- 'string': 'In chacma baboons, male-infant relationships can be linked to both
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- formation of friendships and paternity success [30,31].'
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- 'sectionName': 'Introduction',
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- 'label': 'background',
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- 'citingPaperId': '7a6b2d4b405439',
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- 'citedPaperId': '9d1abadc55b5e0',
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- ...
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- }
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- You may obtain the full information about the paper using the provided paper ids
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- with the Semantic Scholar API (https://api.semanticscholar.org/).
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- The labels are:
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- Method, Background, Result
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- """
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-
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- _SOURCE_NAMES = ["properNoun", "andPhrase", "acronym", "etAlPhrase", "explicit", "acronymParen", "nan"]
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-
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-
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- class Scicite(datasets.GeneratorBasedBuilder):
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- """This is a dataset for classifying citation intents in academic papers."""
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-
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- VERSION = datasets.Version("1.0.0")
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-
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- def _info(self):
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- return datasets.DatasetInfo(
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- # This is the description that will appear on the datasets page.
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- description=_DESCRIPTION,
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- # datasets.features.FeatureConnectors
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- features=datasets.Features(
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- {
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- "string": datasets.Value("string"),
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- "sectionName": datasets.Value("string"),
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- "label": datasets.features.ClassLabel(names=["method", "background", "result"]),
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- "citingPaperId": datasets.Value("string"),
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- "citedPaperId": datasets.Value("string"),
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- "excerpt_index": datasets.Value("int32"),
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- "isKeyCitation": datasets.Value("bool"),
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- "label2": datasets.features.ClassLabel(
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- names=["supportive", "not_supportive", "cant_determine", "none"]
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- ),
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- "citeEnd": datasets.Value("int64"),
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- "citeStart": datasets.Value("int64"),
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- "source": datasets.features.ClassLabel(names=_SOURCE_NAMES),
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- "label_confidence": datasets.Value("float32"),
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- "label2_confidence": datasets.Value("float32"),
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- "id": datasets.Value("string"),
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- }
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- ),
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- # If there's a common (input, target) tuple from the features,
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- # specify them here. They'll be used if as_supervised=True in
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- # builder.as_dataset.
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- supervised_keys=None,
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- # Homepage of the dataset for documentation
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- homepage="https://github.com/allenai/scicite",
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- """Returns SplitGenerators."""
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- archive = dl_manager.download("https://s3-us-west-2.amazonaws.com/ai2-s2-research/scicite/scicite.tar.gz")
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- gen_kwargs={
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- "filepath": "/".join(["scicite", "train.jsonl"]),
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- "files": dl_manager.iter_archive(archive),
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- },
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION,
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- gen_kwargs={"filepath": "/".join(["scicite", "dev.jsonl"]), "files": dl_manager.iter_archive(archive)},
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.TEST,
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- gen_kwargs={
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- "filepath": "/".join(["scicite", "test.jsonl"]),
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- "files": dl_manager.iter_archive(archive),
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- },
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- ),
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- ]
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-
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- def _generate_examples(self, filepath, files):
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- """Yields examples."""
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- for path, f in files:
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- if path == filepath:
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- unique_ids = {}
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- for line in f:
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- d = json.loads(line.decode("utf-8"))
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- unique_id = str(d["unique_id"])
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- if unique_id in unique_ids:
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- continue
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- unique_ids[unique_id] = True
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- yield unique_id, {
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- "string": d["string"],
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- "label": str(d["label"]),
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- "sectionName": str(d["sectionName"]),
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- "citingPaperId": str(d["citingPaperId"]),
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- "citedPaperId": str(d["citedPaperId"]),
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- "excerpt_index": int(d["excerpt_index"]),
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- "isKeyCitation": bool(d["isKeyCitation"]),
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- "label2": str(d.get("label2", "none")),
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- "citeEnd": _safe_int(d["citeEnd"]),
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- "citeStart": _safe_int(d["citeStart"]),
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- "source": str(d["source"]),
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- "label_confidence": float(d.get("label_confidence", 0.0)),
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- "label2_confidence": float(d.get("label2_confidence", 0.0)),
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- "id": str(d["id"]),
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- }
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- break
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-
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-
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- def _safe_int(a):
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- try:
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- # skip NaNs
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- return int(a)
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- except ValueError:
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- return -1