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": "The Text REtrieval Conference (TREC) Question Classification dataset contains 5500 labeled questions in training set and another 500 for test set. The dataset has 6 labels, 47 level-2 labels. Average length of each sentence is 10, vocabulary size of 8700.\n\nData are collected from four sources: 4,500 English questions published by USC (Hovy et al., 2001), about 500 manually constructed questions for a few rare classes, 894 TREC 8 and TREC 9 questions, and also 500 questions from TREC 10 which serves as the test set.\n", "citation": "@inproceedings{li-roth-2002-learning,\n    title = \"Learning Question Classifiers\",\n    author = \"Li, Xin  and\n      Roth, Dan\",\n    booktitle = \"{COLING} 2002: The 19th International Conference on Computational Linguistics\",\n    year = \"2002\",\n    url = \"https://www.aclweb.org/anthology/C02-1150\",\n}\n@inproceedings{hovy-etal-2001-toward,\n    title = \"Toward Semantics-Based Answer Pinpointing\",\n    author = \"Hovy, Eduard  and\n      Gerber, Laurie  and\n      Hermjakob, Ulf  and\n      Lin, Chin-Yew  and\n      Ravichandran, Deepak\",\n    booktitle = \"Proceedings of the First International Conference on Human Language Technology Research\",\n    year = \"2001\",\n    url = \"https://www.aclweb.org/anthology/H01-1069\",\n}\n", "homepage": "https://cogcomp.seas.upenn.edu/Data/QA/QC/", "license": "", "features": {"label-coarse": {"num_classes": 6, "names": ["DESC", "ENTY", "ABBR", "HUM", "NUM", "LOC"], "names_file": null, "id": null, "_type": "ClassLabel"}, "label-fine": {"num_classes": 47, "names": ["manner", "cremat", "animal", "exp", "ind", "gr", "title", "def", "date", "reason", "event", "state", "desc", "count", "other", "letter", "religion", "food", "country", "color", "termeq", "city", "body", "dismed", "mount", "money", "product", "period", "substance", "sport", "plant", "techmeth", "volsize", "instru", "abb", "speed", "word", "lang", "perc", "code", "dist", "temp", "symbol", "ord", "veh", "weight", "currency"], "names_file": null, "id": null, "_type": "ClassLabel"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "trec", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 385090, "num_examples": 5452, "dataset_name": "trec"}, "test": {"name": "test", "num_bytes": 27983, "num_examples": 500, "dataset_name": "trec"}}, "download_checksums": {"http://cogcomp.org/Data/QA/QC/train_5500.label": {"num_bytes": 335858, "checksum": "9e4c8bdcaffb96ed61041bd64b564183d52793a8e91d84fc3a8646885f466ec3"}, "http://cogcomp.org/Data/QA/QC/TREC_10.label": {"num_bytes": 23354, "checksum": "033f22c028c2bbba9ca682f68ffe204dc1aa6e1cf35dd6207f2d4ca67f0d0e8e"}}, "download_size": 359212, "dataset_size": 413073, "size_in_bytes": 772285}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cceb4211ca3ed001c69c675b3123d7ffc3ec6679e9c28a53f110024ff8d2dd85
+size 861

trec.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+""" The Text REtrieval Conference (TREC) Question Classification dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{li-roth-2002-learning,
+    title = "Learning Question Classifiers",
+    author = "Li, Xin  and
+      Roth, Dan",
+    booktitle = "{COLING} 2002: The 19th International Conference on Computational Linguistics",
+    year = "2002",
+    url = "https://www.aclweb.org/anthology/C02-1150",
+}
+@inproceedings{hovy-etal-2001-toward,
+    title = "Toward Semantics-Based Answer Pinpointing",
+    author = "Hovy, Eduard  and
+      Gerber, Laurie  and
+      Hermjakob, Ulf  and
+      Lin, Chin-Yew  and
+      Ravichandran, Deepak",
+    booktitle = "Proceedings of the First International Conference on Human Language Technology Research",
+    year = "2001",
+    url = "https://www.aclweb.org/anthology/H01-1069",
+}
+"""
+
+_DESCRIPTION = """\
+The Text REtrieval Conference (TREC) Question Classification dataset contains 5500 labeled questions in training set and another 500 for test set. The dataset has 6 labels, 47 level-2 labels. Average length of each sentence is 10, vocabulary size of 8700.
+
+Data are collected from four sources: 4,500 English questions published by USC (Hovy et al., 2001), about 500 manually constructed questions for a few rare classes, 894 TREC 8 and TREC 9 questions, and also 500 questions from TREC 10 which serves as the test set.
+"""
+
+_URLs = {
+    "train": "http://cogcomp.org/Data/QA/QC/train_5500.label",
+    "test": "http://cogcomp.org/Data/QA/QC/TREC_10.label",
+}
+
+_COARSE_LABELS = ["DESC", "ENTY", "ABBR", "HUM", "NUM", "LOC"]
+
+_FINE_LABELS = [
+    "manner",
+    "cremat",
+    "animal",
+    "exp",
+    "ind",
+    "gr",
+    "title",
+    "def",
+    "date",
+    "reason",
+    "event",
+    "state",
+    "desc",
+    "count",
+    "other",
+    "letter",
+    "religion",
+    "food",
+    "country",
+    "color",
+    "termeq",
+    "city",
+    "body",
+    "dismed",
+    "mount",
+    "money",
+    "product",
+    "period",
+    "substance",
+    "sport",
+    "plant",
+    "techmeth",
+    "volsize",
+    "instru",
+    "abb",
+    "speed",
+    "word",
+    "lang",
+    "perc",
+    "code",
+    "dist",
+    "temp",
+    "symbol",
+    "ord",
+    "veh",
+    "weight",
+    "currency",
+]
+
+
+class Trec(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        # TODO: Specifies the datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "label-coarse": datasets.ClassLabel(names=_COARSE_LABELS),
+                    "label-fine": datasets.ClassLabel(names=_FINE_LABELS),
+                    "text": 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://cogcomp.seas.upenn.edu/Data/QA/QC/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_files = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": dl_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": dl_files["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+        # TODO: Yields (key, example) tuples from the dataset
+        with open(filepath, "rb") as f:
+            for id_, row in enumerate(f):
+                # One non-ASCII byte: sisterBADBYTEcity. We replace it with a space
+                label, _, text = row.replace(b"\xf0", b" ").strip().decode().partition(" ")
+                coarse_label, _, fine_label = label.partition(":")
+                yield id_, {
+                    "label-coarse": coarse_label,
+                    "label-fine": fine_label,
+                    "text": text,
+                }