fs.py

# coding=utf-8
# Lint as: python3
"""The SCROLLS benchmark."""

import json
import os
import datasets
from citations_and_descriptions import (
    _SUMM_SCREEN_DESCRIPTION, _SUMM_SCREEN_CITATION,
    _GOV_REPORT_CITATION, _GOV_REPORT_DESCRIPTION,
    _ARXIV_CITATION, _ARXIV_DESCRIPTION,
    _FS_DESCRIPTION, _FS_CITATION
)


class FSConfig(datasets.BuilderConfig):
    """BuilderConfig for FS."""

    def __init__(self, features, data_url, citation, url, max_source_length, tokenizer, **kwargs):
        """BuilderConfig for FS.
        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
        self.max_source_length = max_source_length
        self.tokenizer = tokenizer
        self.prompt = None
        self.input_key = None
        self.output_key = None
        self.redundant_fields = []

        self.train_file = "train.jsonl"
        self.validation_file = "validation.jsonl"
        self.test_file = "test.jsonl"

    def remove_redundant_fields(self, example):
        for field in self.redundant_fields:
            del example[field]

    def process_input(self, s):
        return s.strip()

    def process_output(self, s):
        return s


class ScrollsConfig(FSConfig):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.train_file = "train.jsonl"
        self.validation_file = "validation.jsonl"
        self.test_file = "test.jsonl"

        self.input_key = "input"  # TODO I think that we should keep the original fields
        self.output_key = "output"
        self.id_key = "pid"
        self.redundant_fields = [self.input_key, self.output_key, "id"]

    def process_input(self, s):
        prefix = s.strip()
        suffix = "\nSummarize the above:"
        prefix = _truncate_prefix(prefix, suffix, self.max_source_length, self.tokenizer)
        return prefix + suffix


class ArxivConfig(FSConfig):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.train_file = "train.txt"
        self.validation_file = "val.txt"
        self.test_file = "test.txt"

        self.input_key = "article_text"
        self.output_key = "abstract_text"
        self.id_key = "article_id"
        self.redundant_fields = [self.input_key, self.output_key, self.id_key, 'labels', 'section_names', 'sections']

    def process_input(self, s):
        prefix = ' '.join(s)
        suffix = "\nSummarize the above:"
        prefix = _truncate_prefix(prefix, suffix, self.max_source_length, self.tokenizer)
        return prefix + suffix

    def process_output(self, s):
        # TODO remove "<S>" and "</S>" ?
        return ' '.join(s).replace("<S>", "").replace("</S>", "")


def _truncate_prefix(prefix, suffix, max_source_length, tokenizer):
    encoded_input = tokenizer.encode(prefix + suffix)

    while len(encoded_input) > max_source_length:
        overflow = len(encoded_input) - max_source_length
        tokenized_prefix = tokenizer.encode(prefix, add_special_tokens=False)
        if overflow > 0:
            tokenized_prefix = tokenized_prefix[:-overflow]
        prefix = tokenizer.decode(tokenized_prefix, skip_special_tokens=False).strip()
        encoded_input = tokenizer.encode(prefix + suffix)

    return prefix


class Fs(datasets.GeneratorBasedBuilder):
    """The SCROLLS benchmark."""

    features = ["pid", "source", "target"]
    DEFAULT_WRITER_BATCH_SIZE = 1000  # because Narrative QA is a rather large dataset
    BUILDER_CONFIGS = [
        ScrollsConfig(
            name="summ_screen_fd_debug",
            description=_SUMM_SCREEN_DESCRIPTION,
            features=features,
            data_url="https://huggingface.co/datasets/tau/fs/resolve/main/data/summ_screen_fd_debug.zip",
            citation=_SUMM_SCREEN_CITATION,
            url="https://github.com/mingdachen/SummScreen",
            max_source_length=None,
            tokenizer=None,
        ),
        ScrollsConfig(
            name="gov_report",
            description=_GOV_REPORT_CITATION,
            features=features,
            data_url="https://huggingface.co/datasets/tau/fs/resolve/main/data/gov_report.zip",
            citation=_GOV_REPORT_DESCRIPTION,
            url="https://gov-report-data.github.io/",
            max_source_length=None,
            tokenizer=None,
        ),
        ArxivConfig(
            name="arxiv_debug",
            description=_ARXIV_CITATION,
            features=features,
            data_url="https://huggingface.co/datasets/tau/fs/resolve/main/data/arxiv_debug.zip",
            citation=_ARXIV_DESCRIPTION,
            url="https://github.com/armancohan/long-summarization",
            max_source_length=None,
            tokenizer=None,
        ),
    ]

    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)

        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, self.config.train_file),
                    "split": datasets.Split.TRAIN,
                    "max_source_length": self.config.max_source_length,
                    "tokenizer": self.config.tokenizer,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, self.config.validation_file),
                    "split": datasets.Split.VALIDATION,
                    "max_source_length": self.config.max_source_length,
                    "tokenizer": self.config.tokenizer,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, self.config.test_file) if data_files is None else data_files[
                        "test"],
                    "split": datasets.Split.TEST,
                    "max_source_length": self.config.max_source_length,
                    "tokenizer": self.config.tokenizer,
                },
            ),
        ]

    def _generate_examples(self, data_file, split, max_source_length, tokenizer):
        with open(data_file, encoding="utf-8") as f:
            for line in f:
                row = json.loads(line)

                row["pid"] = row[self.config.id_key]
                row["source"] = self.config.process_input(row[self.config.input_key])
                row["target"] = self.config.process_output(row[self.config.output_key])

                self.config.remove_redundant_fields(row)
                yield row["pid"], row


def _get_task_name_from_data_url(data_url):
    return data_url.split("/")[-1].split(".")[0]