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": "\nHeadline-generation on a corpus of article pairs from Gigaword consisting of\naround 4 million articles. Use the 'org_data' provided by\nhttps://github.com/microsoft/unilm/ which is identical to\nhttps://github.com/harvardnlp/sent-summary but with better format.\n\nThere are two features:\n  - document: article.\n  - summary: headline.\n\n", "citation": "\n@article{graff2003english,\n  title={English gigaword},\n  author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},\n  journal={Linguistic Data Consortium, Philadelphia},\n  volume={4},\n  number={1},\n  pages={34},\n  year={2003}\n}\n\n@article{Rush_2015,\n   title={A Neural Attention Model for Abstractive Sentence Summarization},\n   url={http://dx.doi.org/10.18653/v1/D15-1044},\n   DOI={10.18653/v1/d15-1044},\n   journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},\n   publisher={Association for Computational Linguistics},\n   author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},\n   year={2015}\n}\n", "homepage": "https://github.com/harvardnlp/sent-summary", "license": "", "features": {"document": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "document", "output": "summary"}, "builder_name": "gigaword", "config_name": "default", "version": {"version_str": "1.2.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 2, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 451514, "num_examples": 1951, "dataset_name": "gigaword"}, "train": {"name": "train", "num_bytes": 916673137, "num_examples": 3803957, "dataset_name": "gigaword"}, "validation": {"name": "validation", "num_bytes": 45838081, "num_examples": 189651, "dataset_name": "gigaword"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1USoQ8lJgN8kAWnUnRrupMGrPMLlDVqlV": {"num_bytes": 578402958, "checksum": "bc0c4a2e1aa19cf2123688b87bc2d778c0d8fc24a4090e3c10a27c5faa1b898b"}}, "download_size": 578402958, "dataset_size": 962962732, "size_in_bytes": 1541365690}}

dummy/1.2.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:60a43d59a4975055ba265da13bf83659f0ec63a2828ea14a6a5c71cc68e94687
+size 1564

gigaword.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
+"""Gigaword summarization dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_CITATION = """
+@article{graff2003english,
+  title={English gigaword},
+  author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
+  journal={Linguistic Data Consortium, Philadelphia},
+  volume={4},
+  number={1},
+  pages={34},
+  year={2003}
+}
+
+@article{Rush_2015,
+   title={A Neural Attention Model for Abstractive Sentence Summarization},
+   url={http://dx.doi.org/10.18653/v1/D15-1044},
+   DOI={10.18653/v1/d15-1044},
+   journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
+   publisher={Association for Computational Linguistics},
+   author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},
+   year={2015}
+}
+"""
+
+_DESCRIPTION = """
+Headline-generation on a corpus of article pairs from Gigaword consisting of
+around 4 million articles. Use the 'org_data' provided by
+https://github.com/microsoft/unilm/ which is identical to
+https://github.com/harvardnlp/sent-summary but with better format.
+
+There are two features:
+  - document: article.
+  - summary: headline.
+
+"""
+
+_URL = "https://drive.google.com/uc?export=download&id=1USoQ8lJgN8kAWnUnRrupMGrPMLlDVqlV"
+
+_DOCUMENT = "document"
+_SUMMARY = "summary"
+
+
+class Gigaword(datasets.GeneratorBasedBuilder):
+    """Gigaword summarization dataset."""
+
+    # 1.0.0 contains a bug that uses validation data as training data.
+    # 1.1.0 Update to the correct train, validation and test data.
+    # 1.2.0 Replace <unk> with <UNK> in train/val to be consistent with test.
+    VERSION = datasets.Version("1.2.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
+            supervised_keys=(_DOCUMENT, _SUMMARY),
+            homepage="https://github.com/harvardnlp/sent-summary",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_path = dl_manager.download_and_extract(_URL)
+        pattern = os.path.join(dl_path, "org_data", "%s.%s.txt")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "src_path": pattern % ("train", "src"),
+                    "tgt_path": pattern % ("train", "tgt"),
+                    "replace_unk": True,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "src_path": pattern % ("dev", "src"),
+                    "tgt_path": pattern % ("dev", "tgt"),
+                    "replace_unk": True,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "src_path": pattern % ("test", "src"),
+                    "tgt_path": pattern % ("test", "tgt"),
+                    "replace_unk": False,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, src_path=None, tgt_path=None, replace_unk=None):
+        """Yields examples."""
+        with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s:
+            for i, (doc_text, sum_text) in enumerate(zip(f_d, f_s)):
+                if replace_unk:
+                    yield i, {
+                        _DOCUMENT: doc_text.strip().replace("<unk>", "UNK"),
+                        _SUMMARY: sum_text.strip().replace("<unk>", "UNK"),
+                    }
+                else:
+                    yield i, {_DOCUMENT: doc_text.strip(), _SUMMARY: sum_text.strip()}