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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+{"WOS5736": {"description": "Copyright (c) 2017 Kamran Kowsari\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the \"Dataset\"), to deal\nin the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset.\n\nIf you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification\n\nDescription of Dataset:\n\nHere is three datasets which include WOS-11967 , WOS-46985, and WOS-5736\nEach folder contains:\n-X.txt\n-Y.txt\n-YL1.txt\n-YL2.txt\n\nX is input data that include text sequences\nY is target value\nYL1 is target value of level one (parent label)\nYL2 is target value of level one (child label)\nWeb of Science Dataset WOS-5736\n                        -This dataset contains 5,736 documents with 11 categories which include 3 parents categories.", "citation": "@inproceedings{kowsari2017HDLTex,\ntitle={HDLTex: Hierarchical Deep Learning for Text Classification},\nauthor={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E},\nbooktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on},\nyear={2017},\norganization={IEEE}\n}\n", "homepage": "https://data.mendeley.com/datasets/9rw3vkcfy4/6", "license": "", "features": {"input_data": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}, "label_level_1": {"dtype": "int32", "id": null, "_type": "Value"}, "label_level_2": {"dtype": "int32", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "web_of_science", "config_name": "WOS5736", "version": {"version_str": "6.0.0", "description": "", "datasets_version_to_prepare": null, "major": 6, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8055118, "num_examples": 5736, "dataset_name": "web_of_science"}}, "download_checksums": {"https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1": {"num_bytes": 60222421, "checksum": "b787d484bff88b0dcdb3fa291d06ec9d2f025dc2a67ce1045d0c688cd96ccf8a"}}, "download_size": 60222421, "dataset_size": 8055118, "size_in_bytes": 68277539}, "WOS11967": {"description": "Copyright (c) 2017 Kamran Kowsari\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the \"Dataset\"), to deal\nin the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset.\n\nIf you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification\n\nDescription of Dataset:\n\nHere is three datasets which include WOS-11967 , WOS-46985, and WOS-5736\nEach folder contains:\n-X.txt\n-Y.txt\n-YL1.txt\n-YL2.txt\n\nX is input data that include text sequences\nY is target value\nYL1 is target value of level one (parent label)\nYL2 is target value of level one (child label)\nWeb of Science Dataset WOS-11967\n                        -This dataset contains 11,967 documents with 35 categories which include 7 parents categories.", "citation": "@inproceedings{kowsari2017HDLTex,\ntitle={HDLTex: Hierarchical Deep Learning for Text Classification},\nauthor={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E},\nbooktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on},\nyear={2017},\norganization={IEEE}\n}\n", "homepage": "https://data.mendeley.com/datasets/9rw3vkcfy4/6", "license": "", "features": {"input_data": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}, "label_level_1": {"dtype": "int32", "id": null, "_type": "Value"}, "label_level_2": {"dtype": "int32", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "web_of_science", "config_name": "WOS11967", "version": {"version_str": "6.0.0", "description": "", "datasets_version_to_prepare": null, "major": 6, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 16255871, "num_examples": 11967, "dataset_name": "web_of_science"}}, "download_checksums": {"https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1": {"num_bytes": 60222421, "checksum": "b787d484bff88b0dcdb3fa291d06ec9d2f025dc2a67ce1045d0c688cd96ccf8a"}}, "download_size": 60222421, "dataset_size": 16255871, "size_in_bytes": 76478292}, "WOS46985": {"description": "Copyright (c) 2017 Kamran Kowsari\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the \"Dataset\"), to deal\nin the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset.\n\nIf you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification\n\nDescription of Dataset:\n\nHere is three datasets which include WOS-11967 , WOS-46985, and WOS-5736\nEach folder contains:\n-X.txt\n-Y.txt\n-YL1.txt\n-YL2.txt\n\nX is input data that include text sequences\nY is target value\nYL1 is target value of level one (parent label)\nYL2 is target value of level one (child label)\n\n                        Web of Science Dataset WOS-46985\n                        -This dataset contains 46,985 documents with 134 categories which include 7 parents categories.", "citation": "@inproceedings{kowsari2017HDLTex,\ntitle={HDLTex: Hierarchical Deep Learning for Text Classification},\nauthor={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E},\nbooktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on},\nyear={2017},\norganization={IEEE}\n}\n", "homepage": "https://data.mendeley.com/datasets/9rw3vkcfy4/6", "license": "", "features": {"input_data": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}, "label_level_1": {"dtype": "int32", "id": null, "_type": "Value"}, "label_level_2": {"dtype": "int32", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "web_of_science", "config_name": "WOS46985", "version": {"version_str": "6.0.0", "description": "", "datasets_version_to_prepare": null, "major": 6, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 65501096, "num_examples": 46985, "dataset_name": "web_of_science"}}, "download_checksums": {"https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1": {"num_bytes": 60222421, "checksum": "b787d484bff88b0dcdb3fa291d06ec9d2f025dc2a67ce1045d0c688cd96ccf8a"}}, "download_size": 60222421, "dataset_size": 65501096, "size_in_bytes": 125723517}}

dummy/WOS5736/6.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a7a3c4d0d31861a9297db8bc69ae0d5716fade5168c8b2caa92dfe7cd4d0e0dd
+size 2030

web_of_science.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
+"""Web of science"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{kowsari2017HDLTex,
+title={HDLTex: Hierarchical Deep Learning for Text Classification},
+author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E},
+booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on},
+year={2017},
+organization={IEEE}
+}
+"""
+
+_DESCRIPTION = """\
+The Web Of Science (WOS) dataset is a collection of data  of published papers
+available from the Web of Science. WOS has been released in three versions: WOS-46985, WOS-11967 and WOS-5736. WOS-46985 is the
+full dataset. WOS-11967 and WOS-5736 are two subsets of WOS-46985.
+
+"""
+
+_DATA_URL = (
+    "https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1"
+)
+
+
+class WebOfScienceConfig(datasets.BuilderConfig):
+    """BuilderConfig for WebOfScience."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for WebOfScience.
+
+        Args:
+        **kwargs: keyword arguments forwarded to super.
+        """
+        super(WebOfScienceConfig, self).__init__(version=datasets.Version("6.0.0", ""), **kwargs)
+
+
+class WebOfScience(datasets.GeneratorBasedBuilder):
+    """Web of Science"""
+
+    BUILDER_CONFIGS = [
+        WebOfScienceConfig(
+            name="WOS5736",
+            description="""Web of Science Dataset WOS-5736: This dataset contains 5,736 documents with 11 categories which include 3 parents categories.""",
+        ),
+        WebOfScienceConfig(
+            name="WOS11967",
+            description="""Web of Science Dataset WOS-11967: This dataset contains 11,967 documents with 35 categories which include 7 parents categories.""",
+        ),
+        WebOfScienceConfig(
+            name="WOS46985",
+            description="""Web of Science Dataset WOS-46985: This dataset contains 46,985 documents with 134 categories which include 7 parents categories.""",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION + self.config.description,
+            features=datasets.Features(
+                {
+                    "input_data": datasets.Value("string"),
+                    "label": datasets.Value("int32"),
+                    "label_level_1": datasets.Value("int32"),
+                    "label_level_2": datasets.Value("int32"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both premise
+            # and hypothesis as input).
+            supervised_keys=None,
+            homepage="https://data.mendeley.com/datasets/9rw3vkcfy4/6",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+
+        dl_path = dl_manager.download_and_extract(_DATA_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "input_file": os.path.join(dl_path, self.config.name, "X.txt"),
+                    "label_file": os.path.join(dl_path, self.config.name, "Y.txt"),
+                    "label_level_1_file": os.path.join(dl_path, self.config.name, "YL1.txt"),
+                    "label_level_2_file": os.path.join(dl_path, self.config.name, "YL2.txt"),
+                },
+            )
+        ]
+
+    def _generate_examples(self, input_file, label_file, label_level_1_file, label_level_2_file):
+        """Yields examples."""
+        with open(input_file, encoding="utf-8") as f:
+            input_data = f.readlines()
+        with open(label_file, encoding="utf-8") as f:
+            label_data = f.readlines()
+        with open(label_level_1_file, encoding="utf-8") as f:
+            label_level_1_data = f.readlines()
+        with open(label_level_2_file, encoding="utf-8") as f:
+            label_level_2_data = f.readlines()
+        for i in range(len(input_data)):
+            yield i, {
+                "input_data": input_data[i],
+                "label": label_data[i],
+                "label_level_1": label_level_1_data[i],
+                "label_level_2": label_level_2_data[i],
+            }