Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- summarization
+---
+
+# Dataset Card for SciTLDR
+
+## Table of Contents
+  - [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+  - [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/allenai/scitldr
+- **Repository:** https://github.com/allenai/scitldr
+- **Paper:** https://arxiv.org/abs/2004.15011
+- **Leaderboard:** 
+- **Point of Contact:** {isabelc,kylel,armanc,danw}@allenai.org
+
+### Dataset Summary
+`SciTLDR`: Extreme Summarization of Scientific Documents
+
+SciTLDR is a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden.
+
+### Supported Tasks and Leaderboards
+
+summarization
+
+### Languages
+
+English
+
+## Dataset Structure
+
+SciTLDR is split in to a 60/20/20 train/dev/test split. For each file, each line is a json, formatted as follows
+```
+{
+   "source":[
+      "sent0",
+      "sent1",
+      "sent2",
+      ...
+   ],
+   "source_labels":[binary list in which 1 is the oracle sentence],
+   "rouge_scores":[precomputed rouge-1 scores],
+   "paper_id":"PAPER-ID",
+   "target":[
+     "author-tldr",
+      "pr-tldr0", 
+      "pr-tldr1",
+      ... 
+   ],
+   "title":"TITLE"
+}
+```
+The keys `rouge_scores` and `source_labels` are not necessary for any code to run, precomputed Rouge scores are provided for future research.
+
+### Data Instances
+
+{
+    "source": [
+        "Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs.",
+        "MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training.",
+        "Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages.",
+        "We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter.",
+        "We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods.",
+        "We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point."
+    ],
+    "source_labels": [
+        0,
+        0,
+        0,
+        1,
+        0,
+        0
+    ],
+    "rouge_scores": [
+        0.2399999958000001,
+        0.26086956082230633,
+        0.19999999531250012,
+        0.38095237636054424,
+        0.2051282003944774,
+        0.2978723360796741
+    ],
+    "paper_id": "rJlnfaNYvB",
+    "target": [
+        "We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.",
+        "Proposal for an adaptive loss scaling method during backpropagation for mix precision training where scale rate is decided automatically to reduce the underflow.",
+        "The authors propose a method to train models in FP16 precision that adopts a more elaborate way to minimize underflow in every layer simultaneously and automatically."
+    ],
+    "title": "Adaptive Loss Scaling for Mixed Precision Training"
+}
+
+### Data Fields
+
+- `source`: The Abstract, Introduction and Conclusion (AIC) or Full text of the paper, with one sentence per line.
+- `source_labels`: Binary 0 or 1, 1 denotes the oracle sentence.
+- `rouge_scores`: Precomputed ROUGE baseline scores for each sentence.
+- `paper_id`: Arxiv Paper ID.
+- `target`: Multiple summaries for each sentence, one sentence per line.
+- `title`: Title of the paper.
+### Data Splits
+
+|                   | train | valid  | test |
+|-------------------|-------|--------|------|
+| SciTLDR-A         | 1992  | 618    | 619  |
+| SciTLDR-AIC       | 1992  | 618    | 619  |
+| SciTLDR-FullText  | 1992  | 618    | 619  |
+
+## Dataset Creation
+
+[More Information Needed]
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+https://allenai.org/
+
+### Annotations
+
+#### Annotation process
+
+Given the title and first 128 words of a reviewer comment about a paper,
+re-write the summary (if it exists) into a single sentence or an incomplete
+phrase. Summaries must be no more than one sentence.
+Most summaries are between 15 and 25 words. The average rewritten summary is
+20 words long.
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+To encourage further research in the area of extreme summarization of scientific documents.
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Apache License 2.0
+
+### Citation Information
+@article{cachola2020tldr,
+  title={{TLDR}: Extreme Summarization of Scientific Documents},
+  author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
+  journal={arXiv:2004.15011},
+  year={2020},
+}

dataset_infos.json

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scitldr.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.
+"""Dataset for TLDR: Extreme Summarization of Scientific Documents"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_SOURCE = "source"
+_TARGET = "target"
+
+_CITATION = """\
+@article{cachola2020tldr,
+  title={{TLDR}: Extreme Summarization of Scientific Documents},
+  author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
+  journal={arXiv:2004.15011},
+  year={2020},
+}
+"""
+
+_DESCRIPTION = """\
+A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
+SCITLDR contains both author-written and expert-derived TLDRs,
+where the latter are collected using a novel annotation protocol
+that produces high-quality summaries while minimizing annotation burden.
+"""
+
+
+_LICENSE = "Apache License 2.0"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLs = {
+    "Abstract": "https://raw.githubusercontent.com/allenai/scitldr/master/SciTLDR-Data/SciTLDR-A/",
+    "AIC": "https://raw.githubusercontent.com/allenai/scitldr/master/SciTLDR-Data/SciTLDR-AIC/",
+    "FullText": "https://raw.githubusercontent.com/allenai/scitldr/master/SciTLDR-Data/SciTLDR-FullText/",
+}
+
+_TRAIN_DATA = "train.jsonl"
+_TEST_DATA = "test.jsonl"
+_VALID_DATA = "dev.jsonl"
+
+
+# There are several preprocessing scripts given in the original SciTLDR GitHub repository to preprocess this data.
+class Scitldr(datasets.GeneratorBasedBuilder):
+    """Dataset for TLDR: Extreme Summarization of Scientific Documents."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('scitldr', 'Abstract')
+    # data = datasets.load_dataset('scitldr', 'AIC')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="Abstract", description="This part contains only abstracts of the paper"),
+        datasets.BuilderConfig(
+            name="AIC",
+            description="This part contains Abstracts, Introduction and Conclusion (AIC) sections of the paper",
+        ),
+        datasets.BuilderConfig(name="FullText", description="This part contains the full text of the paper"),
+    ]
+
+    DEFAULT_CONFIG_NAME = (
+        "Abstract"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    )
+
+    def _info(self):
+        if self.config.name == "AIC":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "source": datasets.Sequence(datasets.Value("string")),
+                    "source_labels": datasets.Sequence(datasets.ClassLabel(num_classes=2, names=[0, 1])),
+                    "rouge_scores": datasets.Sequence(datasets.Value("float32")),
+                    "paper_id": datasets.Value("string"),
+                    "ic": datasets.Value("bool_"),
+                    "target": datasets.features.Sequence(datasets.Value("string"))
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "source": datasets.Sequence(datasets.Value("string")),
+                    "source_labels": datasets.Sequence(
+                        datasets.ClassLabel(num_classes=2, names=["non-oracle", "oracle"])
+                    ),
+                    "rouge_scores": datasets.Sequence(datasets.Value("float32")),
+                    "paper_id": datasets.Value("string"),
+                    "target": datasets.Sequence(datasets.Value("string"))
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # 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=(_SOURCE, _TARGET),
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/allenai/scitldr",
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = {
+            "train": os.path.join(_URLs[self.config.name], _TRAIN_DATA),
+            "valid": os.path.join(_URLs[self.config.name], _VALID_DATA),
+            "test": os.path.join(_URLs[self.config.name], _TEST_DATA),
+        }
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir["train"]), "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir["test"]), "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir["valid"]), "split": "dev"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        # The key is not important, it's more here for legacy reason (legacy from tfds)
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "AIC":
+                    yield id_, {
+                        "source": data["source"],
+                        "source_labels": data["source_labels"],
+                        "rouge_scores": data["rouge_scores"],
+                        "paper_id": data["paper_id"],
+                        "ic": True if data["ic"] else False,
+                        "target": data["target"],
+                    }
+                else:
+                    yield id_, {
+                        "source": data["source"],
+                        "source_labels": data["source_labels"],
+                        "rouge_scores": data["rouge_scores"],
+                        "paper_id": data["paper_id"],
+                        "target": data["target"],
+                    }