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README.md

@@ -1,305 +0,0 @@
----
-annotations_creators:
-- no-annotation
-language_creators:
-- found
-language:
-- en
-license:
-- unknown
-multilinguality:
-- monolingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-task_categories:
-- summarization
-task_ids: []
-paperswithcode_id: scitldr
-pretty_name: SciTLDR
-tags:
-- scientific-documents-summarization
-dataset_info:
-- config_name: Abstract
-  features:
-  - name: source
-    sequence: string
-  - name: source_labels
-    sequence:
-      class_label:
-        names:
-          0: non-oracle
-          1: oracle
-  - name: rouge_scores
-    sequence: float32
-  - name: paper_id
-    dtype: string
-  - name: target
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 2738065
-    num_examples: 1992
-  - name: test
-    num_bytes: 1073656
-    num_examples: 618
-  - name: validation
-    num_bytes: 994876
-    num_examples: 619
-  download_size: 5483987
-  dataset_size: 4806597
-- config_name: AIC
-  features:
-  - name: source
-    sequence: string
-  - name: source_labels
-    sequence:
-      class_label:
-        names:
-          0: 0
-          1: 1
-  - name: rouge_scores
-    sequence: float32
-  - name: paper_id
-    dtype: string
-  - name: ic
-    dtype: bool_
-  - name: target
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 14473822
-    num_examples: 1992
-  - name: test
-    num_bytes: 4822026
-    num_examples: 618
-  - name: validation
-    num_bytes: 4476237
-    num_examples: 619
-  download_size: 25545108
-  dataset_size: 23772085
-- config_name: FullText
-  features:
-  - name: source
-    sequence: string
-  - name: source_labels
-    sequence:
-      class_label:
-        names:
-          0: non-oracle
-          1: oracle
-  - name: rouge_scores
-    sequence: float32
-  - name: paper_id
-    dtype: string
-  - name: target
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 66917363
-    num_examples: 1992
-  - name: test
-    num_bytes: 20182554
-    num_examples: 618
-  - name: validation
-    num_bytes: 18790651
-    num_examples: 619
-  download_size: 110904552
-  dataset_size: 105890568
----
-
-# Dataset Card for SciTLDR
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [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)
-  - [Contributions](#contributions)
-
-## 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},
-}
-
-### Contributions
-
-Thanks to [@Bharat123rox](https://github.com/Bharat123rox) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
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scitldr.py

@@ -1,169 +0,0 @@
-# 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"""
-
-
-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."""
-        urls = {
-            "train": _URLs[self.config.name] + _TRAIN_DATA,
-            "valid": _URLs[self.config.name] + _VALID_DATA,
-            "test": _URLs[self.config.name] + _TEST_DATA,
-        }
-        data_dir = dl_manager.download(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": os.path.join(data_dir["train"])},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": os.path.join(data_dir["test"])},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": os.path.join(data_dir["valid"])},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        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"],
-                    }