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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+{"short": {"description": "\nReddit dataset, where TIFU denotes the name of subbreddit /r/tifu.\nAs defined in the publication, styel \"short\" uses title as summary and\n\"long\" uses tldr as summary.\n\nFeatures includes:\n  - document: post text without tldr.\n  - tldr: tldr line.\n  - title: trimmed title without tldr.\n  - ups: upvotes.\n  - score: score.\n  - num_comments: number of comments.\n  - upvote_ratio: upvote ratio.\n", "citation": "\n@misc{kim2018abstractive,\n    title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},\n    author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},\n    year={2018},\n    eprint={1811.00783},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/ctr4si/MMN", "license": "", "features": {"ups": {"dtype": "float32", "id": null, "_type": "Value"}, "num_comments": {"dtype": "float32", "id": null, "_type": "Value"}, "upvote_ratio": {"dtype": "float32", "id": null, "_type": "Value"}, "score": {"dtype": "float32", "id": null, "_type": "Value"}, "documents": {"dtype": "string", "id": null, "_type": "Value"}, "tldr": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "documents", "output": "title"}, "builder_name": "reddit_tifu", "config_name": "short", "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": 137755713, "num_examples": 79740, "dataset_name": "reddit_tifu"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF": {"num_bytes": 670607856, "checksum": "f175cafe348e0521c2424cd419c934d10c6af613ed8cbe8eaa8cfbaa06377f1a"}}, "download_size": 670607856, "dataset_size": 137755713, "size_in_bytes": 808363569}, "long": {"description": "\nReddit dataset, where TIFU denotes the name of subbreddit /r/tifu.\nAs defined in the publication, styel \"short\" uses title as summary and\n\"long\" uses tldr as summary.\n\nFeatures includes:\n  - document: post text without tldr.\n  - tldr: tldr line.\n  - title: trimmed title without tldr.\n  - ups: upvotes.\n  - score: score.\n  - num_comments: number of comments.\n  - upvote_ratio: upvote ratio.\n", "citation": "\n@misc{kim2018abstractive,\n    title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},\n    author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},\n    year={2018},\n    eprint={1811.00783},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/ctr4si/MMN", "license": "", "features": {"ups": {"dtype": "float32", "id": null, "_type": "Value"}, "num_comments": {"dtype": "float32", "id": null, "_type": "Value"}, "upvote_ratio": {"dtype": "float32", "id": null, "_type": "Value"}, "score": {"dtype": "float32", "id": null, "_type": "Value"}, "documents": {"dtype": "string", "id": null, "_type": "Value"}, "tldr": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "documents", "output": "tldr"}, "builder_name": "reddit_tifu", "config_name": "long", "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": 92005782, "num_examples": 42139, "dataset_name": "reddit_tifu"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF": {"num_bytes": 670607856, "checksum": "f175cafe348e0521c2424cd419c934d10c6af613ed8cbe8eaa8cfbaa06377f1a"}}, "download_size": 670607856, "dataset_size": 92005782, "size_in_bytes": 762613638}}

dummy/short/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0983e1244341f452576a6867edaeba637d30d0d73c84faab801b31a5321e57cd
+size 430

reddit_tifu.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
+"""Reddit TIFU dataset using tifu or tldr from subreddit tifu."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+_CITATION = """
+@misc{kim2018abstractive,
+    title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},
+    author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},
+    year={2018},
+    eprint={1811.00783},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """
+Reddit dataset, where TIFU denotes the name of subbreddit /r/tifu.
+As defined in the publication, styel "short" uses title as summary and
+"long" uses tldr as summary.
+
+Features includes:
+  - document: post text without tldr.
+  - tldr: tldr line.
+  - title: trimmed title without tldr.
+  - ups: upvotes.
+  - score: score.
+  - num_comments: number of comments.
+  - upvote_ratio: upvote ratio.
+"""
+
+_URL = "https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF"
+
+_DOCUMENT = "documents"
+_TITLE = "title"
+_TLDR = "tldr"
+_ADDITIONAL_FEATURES = ["ups", "num_comments", "score", "upvote_ratio"]
+
+
+class RedditTifuConfig(datasets.BuilderConfig):
+    """BuilderConfig for RedditTifu."""
+
+    def __init__(self, summary_key=None, **kwargs):
+        """BuilderConfig for RedditTifu.
+
+        Args:
+          summary_key: key string of summary in downloaded json file.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        # Version 1.1.0 remove empty document and summary strings.
+        super(RedditTifuConfig, self).__init__(version=datasets.Version("1.1.0"), **kwargs)
+        self.summary_key = summary_key
+
+
+class RedditTifu(datasets.GeneratorBasedBuilder):
+    """Reddit TIFU Dataset."""
+
+    BUILDER_CONFIGS = [
+        RedditTifuConfig(
+            name="short",
+            summary_key=_TITLE,
+            description="Using title as summary.",
+        ),
+        RedditTifuConfig(
+            name="long",
+            summary_key=_TLDR,
+            description="Using TLDR as summary.",
+        ),
+    ]
+
+    def _info(self):
+        features = {
+            "ups": datasets.Value("float32"),
+            "num_comments": datasets.Value("float32"),
+            "upvote_ratio": datasets.Value("float32"),
+            "score": datasets.Value("float32"),
+        }
+        features.update({k: datasets.Value("string") for k in [_DOCUMENT, _TLDR, _TITLE]})
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=(_DOCUMENT, self.config.summary_key),
+            homepage="https://github.com/ctr4si/MMN",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_path = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"path": dl_path},
+            )
+        ]
+
+    def _generate_examples(self, path=None):
+        """Yields examples."""
+        with open(path, "rb") as f:
+            for i, line in enumerate(f):
+                # keys are 'title_tokenized','permalink','title','url','num_comments',
+                #   'tldr'(optional),'created_utc','trimmed_title_tokenized','ups',
+                #   'selftext_html','score','upvote_ratio','tldr_tokenized'(optional),
+                #   'selftext','trimmed_title','selftext_without_tldr_tokenized',
+                #   'id','selftext_without_tldr'
+                d = json.loads(line)
+                r = {
+                    _DOCUMENT: d["selftext_without_tldr"].strip(),
+                    _TITLE: d["trimmed_title"].strip(),
+                    _TLDR: (d["tldr"] or "").strip(),
+                }
+                r.update({k: d[k] for k in _ADDITIONAL_FEATURES})
+                # skip if document or summary is empty
+                if r[_DOCUMENT] and r[self.config.summary_key]:
+                    yield i, r