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": "\nNEWSROOM is a large dataset for training and evaluating summarization systems.\nIt contains 1.3 million articles and summaries written by authors and\neditors in the newsrooms of 38 major publications.\n\nDataset features includes:\n  - text: Input news text.\n  - summary: Summary for the news.\nAnd additional features:\n  - title: news title.\n  - url: url of the news.\n  - date: date of the article.\n  - density: extractive density.\n  - coverage: extractive coverage.\n  - compression: compression ratio.\n  - density_bin: low, medium, high.\n  - coverage_bin: extractive, abstractive.\n  - compression_bin: low, medium, high.\n\nThis dataset can be downloaded upon requests. Unzip all the contents\n\"train.jsonl, dev.josnl, test.jsonl\" to the tfds folder.\n\n", "citation": "\n@inproceedings{N18-1065,\n  author    = {Grusky, Max and Naaman, Mor and Artzi, Yoav},\n  title     = {NEWSROOM: A Dataset of 1.3 Million Summaries\n               with Diverse Extractive Strategies},\n  booktitle = {Proceedings of the 2018 Conference of the\n               North American Chapter of the Association for\n               Computational Linguistics: Human Language Technologies},\n  year      = {2018},\n}\n", "homepage": "https://summari.es", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "date": {"dtype": "string", "id": null, "_type": "Value"}, "density_bin": {"dtype": "string", "id": null, "_type": "Value"}, "coverage_bin": {"dtype": "string", "id": null, "_type": "Value"}, "compression_bin": {"dtype": "string", "id": null, "_type": "Value"}, "density": {"dtype": "float32", "id": null, "_type": "Value"}, "coverage": {"dtype": "float32", "id": null, "_type": "Value"}, "compression": {"dtype": "float32", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "text", "output": "summary"}, "builder_name": "newsroom", "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": 472446866, "num_examples": 108862, "dataset_name": "newsroom"}, "train": {"name": "train", "num_bytes": 4357506078, "num_examples": 995041, "dataset_name": "newsroom"}, "validation": {"name": "validation", "num_bytes": 473206951, "num_examples": 108837, "dataset_name": "newsroom"}}, "download_checksums": {}, "download_size": 0, "dataset_size": 5303159895, "size_in_bytes": 5303159895}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a083c8805b149f1d7d3fedf6073e7c56828eb1666a2d6a69e43f18a8740b674
+size 1209

newsroom.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
+"""NEWSROOM Dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+@inproceedings{N18-1065,
+  author    = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
+  title     = {NEWSROOM: A Dataset of 1.3 Million Summaries
+               with Diverse Extractive Strategies},
+  booktitle = {Proceedings of the 2018 Conference of the
+               North American Chapter of the Association for
+               Computational Linguistics: Human Language Technologies},
+  year      = {2018},
+}
+"""
+
+_DESCRIPTION = """
+NEWSROOM is a large dataset for training and evaluating summarization systems.
+It contains 1.3 million articles and summaries written by authors and
+editors in the newsrooms of 38 major publications.
+
+Dataset features includes:
+  - text: Input news text.
+  - summary: Summary for the news.
+And additional features:
+  - title: news title.
+  - url: url of the news.
+  - date: date of the article.
+  - density: extractive density.
+  - coverage: extractive coverage.
+  - compression: compression ratio.
+  - density_bin: low, medium, high.
+  - coverage_bin: extractive, abstractive.
+  - compression_bin: low, medium, high.
+
+This dataset can be downloaded upon requests. Unzip all the contents
+"train.jsonl, dev.josnl, test.jsonl" to the tfds folder.
+
+"""
+
+_DOCUMENT = "text"
+_SUMMARY = "summary"
+_ADDITIONAL_TEXT_FEATURES = [
+    "title",
+    "url",
+    "date",
+    "density_bin",
+    "coverage_bin",
+    "compression_bin",
+]
+_ADDITIONAL_FLOAT_FEATURES = [
+    "density",
+    "coverage",
+    "compression",
+]
+
+
+class Newsroom(datasets.GeneratorBasedBuilder):
+    """NEWSROOM Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+  You should download the dataset from http://lil.datasets.cornell.edu/newsroom/
+  The webpage requires registration.
+  To unzip the .tar file run `tar -zxvf complete.tar`. To unzip the .gz files
+  run `gunzip train.json.gz` , ...
+  After downloading, please put the files under the following names
+  dev.jsonl, test.jsonl and train.jsonl in a dir of your choice,
+  which will be used as a manual_dir, e.g. `~/.manual_dirs/newsroom`
+  Newsroom can then be loaded via:
+  `datasets.load_dataset("newsroom", data_dir="~/.manual_dirs/newsroom")`.
+  """
+
+    def _info(self):
+        features = {k: datasets.Value("string") for k in [_DOCUMENT, _SUMMARY] + _ADDITIONAL_TEXT_FEATURES}
+        features.update({k: datasets.Value("float32") for k in _ADDITIONAL_FLOAT_FEATURES})
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=(_DOCUMENT, _SUMMARY),
+            homepage="http://lil.datasets.cornell.edu/newsroom/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('newsroom', data_dir=...)` that includes files unzipped from the reclor zip. Manual download instructions: {}".format(
+                    data_dir, self.manual_download_instructions
+                )
+            )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"input_file": os.path.join(data_dir, "train.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"input_file": os.path.join(data_dir, "dev.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"input_file": os.path.join(data_dir, "test.jsonl")},
+            ),
+        ]
+
+    def _generate_examples(self, input_file=None):
+        """Yields examples."""
+        with open(input_file, encoding="utf-8") as f:
+            for i, line in enumerate(f):
+                d = json.loads(line)
+                # fields are "url", "archive", "title", "date", "text",
+                #  "compression_bin", "density_bin", "summary", "density",
+                #  "compression', "coverage", "coverage_bin",
+                yield i, {
+                    k: d[k] for k in [_DOCUMENT, _SUMMARY] + _ADDITIONAL_TEXT_FEATURES + _ADDITIONAL_FLOAT_FEATURES
+                }