starting reader from scrolls

fs.py

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+# coding=utf-8
+# Lint as: python3
+"""The SCROLLS benchmark."""
+
+import json
+import os
+import datasets
+
+_FS_CITATION = """
+TBD
+"""
+
+_FS_DESCRIPTION = """
+TBD
+"""
+
+_SUMM_SCREEN_DESCRIPTION = """
+SummScreenFD (Chen  et al., 2021) is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).
+Given a transcript of a specific episode, the goal is to produce the episode's recap.
+The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. 
+For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, 
+making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. 
+Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze."""
+
+
+_GOV_REPORT_DESCRIPTION = """
+GovReport (Huang et al., 2021) is a summarization dataset of reports addressing various national policy issues published by the 
+Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.
+The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; 
+for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively."""
+
+
+
+_SUMM_SCREEN_CITATION = r"""
+@misc{chen2021summscreen,
+      title={SummScreen: A Dataset for Abstractive Screenplay Summarization}, 
+      author={Mingda Chen and Zewei Chu and Sam Wiseman and Kevin Gimpel},
+      year={2021},
+      eprint={2104.07091},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}"""
+
+_GOV_REPORT_CITATION = r"""
+@inproceedings{huang-etal-2021-efficient,
+    title = "Efficient Attentions for Long Document Summarization",
+    author = "Huang, Luyang  and
+      Cao, Shuyang  and
+      Parulian, Nikolaus  and
+      Ji, Heng  and
+      Wang, Lu",
+    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jun,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.naacl-main.112",
+    doi = "10.18653/v1/2021.naacl-main.112",
+    pages = "1419--1436",
+    abstract = "The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further conduct a systematic study of existing efficient self-attentions. Combined with Hepos, we are able to process ten times more tokens than existing models that use full attentions. For evaluation, we present a new dataset, GovReport, with significantly longer documents and summaries. Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed. Human evaluation also shows that our models generate more informative summaries with fewer unfaithful errors.",
+}"""
+
+
+class FSConfig(datasets.BuilderConfig):
+    """BuilderConfig for SCROLLS."""
+
+    def __init__(self, features, data_url, citation, url, **kwargs):
+        """BuilderConfig for SCROLLS.
+        Args:
+          features: `list[string]`, list of the features that will appear in the
+            feature dict. Should not include "label".
+          data_url: `string`, url to download the zip file from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+          label_classes: `list[string]`, the list of classes for the label if the
+            label is present as a string. Non-string labels will be cast to either
+            'False' or 'True'.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FSConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.features = features
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+
+class Scrolls(datasets.GeneratorBasedBuilder):
+    """The SCROLLS benchmark."""
+
+    features = ["id", "pid", "input", "output"]
+    DEFAULT_WRITER_BATCH_SIZE = 1000  # because Narrative QA is a rather large dataset
+    BUILDER_CONFIGS = [
+        FSConfig(
+            name="summ_screen_fd",
+            description=_SUMM_SCREEN_DESCRIPTION,
+            features=features,
+            data_url="https://scrolls-tau.s3.us-east-2.amazonaws.com/summ_screen_fd.zip",
+            citation=_SUMM_SCREEN_CITATION,
+            url="https://github.com/mingdachen/SummScreen",
+        ),
+        FSConfig(
+            name="gov_report",
+            description=_GOV_REPORT_CITATION,
+            features=features,
+            data_url="https://scrolls-tau.s3.us-east-2.amazonaws.com/gov_report.zip",
+            citation=_GOV_REPORT_DESCRIPTION,
+            url="https://gov-report-data.github.io/",
+        ),
+    ]
+
+    def _info(self):
+        features = {feature: datasets.Value("string") for feature in self.config.features}
+
+        return datasets.DatasetInfo(
+            description=_FS_DESCRIPTION + self.config.description,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=self.config.citation + "\n" + _FS_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(self.config.data_url)
+        task_name = _get_task_name_from_data_url(self.config.data_url)
+        dl_dir = os.path.join(dl_dir, task_name)
+
+        data_files = {} if self.config.data_files is not None else None
+        if data_files is not None:
+            for split, paths in self.config.data_files.items():
+                data_files[split] = paths[0]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "train.jsonl"),
+                    "split": datasets.Split.TRAIN,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "validation.jsonl"),
+                    "split": datasets.Split.VALIDATION,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "test.jsonl") if data_files is None else data_files["test"],
+                    "split": datasets.Split.TEST,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_file, split):
+        with open(data_file, encoding="utf-8") as f:
+            for line in f:
+                row = json.loads(line)
+                yield row["pid"], row
+
+
+def _get_task_name_from_data_url(data_url):
+    return data_url.split("/")[-1].split(".")[0]
+
+
+if __name__ == '__main__':
+    builder = Scrolls("scrolls", "summ_screen_fd")
+    builder.download_and_prepare()
+    dataset = builder.as_dataset("validation")
+    x = 5
+