Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"default": {"description": "\nThis is a dataset for classifying citation intents in academic papers.\nThe main citation intent label for each Json object is specified with the label\nkey while the citation context is specified in with a context key. Example:\n{\n 'string': 'In chacma baboons, male-infant relationships can be linked to both\n    formation of friendships and paternity success [30,31].'\n 'sectionName': 'Introduction',\n 'label': 'background',\n 'citingPaperId': '7a6b2d4b405439',\n 'citedPaperId': '9d1abadc55b5e0',\n ...\n }\nYou may obtain the full information about the paper using the provided paper ids\nwith the Semantic Scholar API (https://api.semanticscholar.org/).\nThe labels are:\nMethod, Background, Result\n", "citation": "\n@InProceedings{Cohan2019Structural,\n  author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady},\n  title={Structural Scaffolds for Citation Intent Classification in Scientific Publications},\n  booktitle=\"NAACL\",\n  year=\"2019\"\n}\n", "homepage": "https://github.com/allenai/scicite", "license": "", "features": {"string": {"dtype": "string", "id": null, "_type": "Value"}, "sectionName": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["method", "background", "result"], "names_file": null, "id": null, "_type": "ClassLabel"}, "citingPaperId": {"dtype": "string", "id": null, "_type": "Value"}, "citedPaperId": {"dtype": "string", "id": null, "_type": "Value"}, "excerpt_index": {"dtype": "int32", "id": null, "_type": "Value"}, "isKeyCitation": {"dtype": "bool", "id": null, "_type": "Value"}, "label2": {"num_classes": 4, "names": ["supportive", "not_supportive", "cant_determine", "none"], "names_file": null, "id": null, "_type": "ClassLabel"}, "citeEnd": {"dtype": "int64", "id": null, "_type": "Value"}, "citeStart": {"dtype": "int64", "id": null, "_type": "Value"}, "source": {"num_classes": 7, "names": ["properNoun", "andPhrase", "acronym", "etAlPhrase", "explicit", "acronymParen", "nan"], "names_file": null, "id": null, "_type": "ClassLabel"}, "label_confidence": {"dtype": "float32", "id": null, "_type": "Value"}, "label2_confidence": {"dtype": "float32", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scicite", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 870809, "num_examples": 1859, "dataset_name": "scicite"}, "train": {"name": "train", "num_bytes": 3843904, "num_examples": 8194, "dataset_name": "scicite"}, "validation": {"name": "validation", "num_bytes": 430296, "num_examples": 916, "dataset_name": "scicite"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/ai2-s2-research/scicite/scicite.tar.gz": {"num_bytes": 23189911, "checksum": "711ece2c4e61d116c8ae5bb07e9fbb2ee9ff7bba004b4cab7fbd0ac3af499193"}}, "download_size": 23189911, "dataset_size": 5145009, "size_in_bytes": 28334920}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4fc8243e61ee200c6f7b6c4c3859e234dd07ad7bcd4f789edd17435ca514fe82
+size 3118

scicite.py

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+# 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."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+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."""
+        dl_paths = dl_manager.download_and_extract(
+            {
+                "scicite": "https://s3-us-west-2.amazonaws.com/ai2-s2-research/scicite/scicite.tar.gz",
+            }
+        )
+        path = os.path.join(dl_paths["scicite"], "scicite")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"path": os.path.join(path, "train.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"path": os.path.join(path, "dev.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"path": os.path.join(path, "test.jsonl")},
+            ),
+        ]
+
+    def _generate_examples(self, path=None):
+        """Yields examples."""
+        with open(path, encoding="utf-8") as f:
+            unique_ids = {}
+            for line in f:
+                d = json.loads(line)
+                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"]),
+                }
+
+
+def _safe_int(a):
+    try:
+        # skip NaNs
+        return int(a)
+    except ValueError:
+        return -1