Delete loading script

scicite.py

@@ -1,154 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""TODO(scicite): Add a description here."""
-
-
-import json
-
-import datasets
-
-
-_CITATION = """
-@InProceedings{Cohan2019Structural,
-  author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady},
-  title={Structural Scaffolds for Citation Intent Classification in Scientific Publications},
-  booktitle={NAACL},
-  year={2019}
-}
-"""
-
-_DESCRIPTION = """
-This is a dataset for classifying citation intents in academic papers.
-The main citation intent label for each Json object is specified with the label
-key while the citation context is specified in with a context key. Example:
-{
- 'string': 'In chacma baboons, male-infant relationships can be linked to both
-    formation of friendships and paternity success [30,31].'
- 'sectionName': 'Introduction',
- 'label': 'background',
- 'citingPaperId': '7a6b2d4b405439',
- 'citedPaperId': '9d1abadc55b5e0',
- ...
- }
-You may obtain the full information about the paper using the provided paper ids
-with the Semantic Scholar API (https://api.semanticscholar.org/).
-The labels are:
-Method, Background, Result
-"""
-
-_SOURCE_NAMES = ["properNoun", "andPhrase", "acronym", "etAlPhrase", "explicit", "acronymParen", "nan"]
-
-
-class Scicite(datasets.GeneratorBasedBuilder):
-    """This is a dataset for classifying citation intents in academic papers."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "string": datasets.Value("string"),
-                    "sectionName": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["method", "background", "result"]),
-                    "citingPaperId": datasets.Value("string"),
-                    "citedPaperId": datasets.Value("string"),
-                    "excerpt_index": datasets.Value("int32"),
-                    "isKeyCitation": datasets.Value("bool"),
-                    "label2": datasets.features.ClassLabel(
-                        names=["supportive", "not_supportive", "cant_determine", "none"]
-                    ),
-                    "citeEnd": datasets.Value("int64"),
-                    "citeStart": datasets.Value("int64"),
-                    "source": datasets.features.ClassLabel(names=_SOURCE_NAMES),
-                    "label_confidence": datasets.Value("float32"),
-                    "label2_confidence": datasets.Value("float32"),
-                    "id": datasets.Value("string"),
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://github.com/allenai/scicite",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        archive = dl_manager.download("https://s3-us-west-2.amazonaws.com/ai2-s2-research/scicite/scicite.tar.gz")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": "/".join(["scicite", "train.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": "/".join(["scicite", "dev.jsonl"]), "files": dl_manager.iter_archive(archive)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": "/".join(["scicite", "test.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """Yields examples."""
-        for path, f in files:
-            if path == filepath:
-                unique_ids = {}
-                for line in f:
-                    d = json.loads(line.decode("utf-8"))
-                    unique_id = str(d["unique_id"])
-                    if unique_id in unique_ids:
-                        continue
-                    unique_ids[unique_id] = True
-                    yield unique_id, {
-                        "string": d["string"],
-                        "label": str(d["label"]),
-                        "sectionName": str(d["sectionName"]),
-                        "citingPaperId": str(d["citingPaperId"]),
-                        "citedPaperId": str(d["citedPaperId"]),
-                        "excerpt_index": int(d["excerpt_index"]),
-                        "isKeyCitation": bool(d["isKeyCitation"]),
-                        "label2": str(d.get("label2", "none")),
-                        "citeEnd": _safe_int(d["citeEnd"]),
-                        "citeStart": _safe_int(d["citeStart"]),
-                        "source": str(d["source"]),
-                        "label_confidence": float(d.get("label_confidence", 0.0)),
-                        "label2_confidence": float(d.get("label2_confidence", 0.0)),
-                        "id": str(d["id"]),
-                    }
-                break
-
-
-def _safe_int(a):
-    try:
-        # skip NaNs
-        return int(a)
-    except ValueError:
-        return -1