Convert dataset to Parquet

README.md

@@ -72,17 +72,26 @@ dataset_info:
   - name: id
     dtype: string
   splits:
-  - name: test
-    num_bytes: 870809
-    num_examples: 1859
   - name: train
-    num_bytes: 3843904
+    num_bytes: 3828509
     num_examples: 8194
   - name: validation
-    num_bytes: 430296
+    num_bytes: 428551
     num_examples: 916
-  download_size: 23189911
-  dataset_size: 5145009
+  - name: test
+    num_bytes: 867294
+    num_examples: 1859
+  download_size: 3561554
+  dataset_size: 5124354
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for "scicite"

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c43b73ca4544d5aabd57145ca734108e88d817c47746bc9e096090896bd60a58
+size 625439

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:515531960693e29ae248e21f2568cc6a542ed9d1d42c98e0572859d65b59144b
+size 2637978

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fac10c6583d69c3a7646950754c4b8b582ca2b45378722699675e10dab225d78
+size 298137

dataset_infos.json

@@ -1 +0,0 @@
-{"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}}

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