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import json |
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import os |
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import datasets |
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from tqdm import tqdm |
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_ARTICLE_ID = "article_id" |
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_ARTICLE_WORDS = "article_words" |
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_ARTICLE_BBOXES = "article_bboxes" |
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_ARTICLE_NORM_BBOXES = "article_norm_bboxes" |
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_ABSTRACT = "abstract" |
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_ARTICLE_PDF_URL = "article_pdf_url" |
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def normalize_bbox(bbox, size): |
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return [ |
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int(1000 * bbox[0] / size[0]), |
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int(1000 * bbox[1] / size[1]), |
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int(1000 * bbox[2] / size[0]), |
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int(1000 * bbox[3] / size[1]), |
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] |
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class HALSummarizationConfig(datasets.BuilderConfig): |
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"""BuilderConfig for HALSummarization.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for ArxivSummarization. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(HALSummarizationConfig, self).__init__(**kwargs) |
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class HALSummarizationDataset(datasets.GeneratorBasedBuilder): |
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"""HALSummarization Dataset.""" |
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_TRAIN_ARCHIVE = "train.zip" |
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_VAL_ARCHIVE = "val.zip" |
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_TEST_ARCHIVE = "test.zip" |
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_TRAIN_ABSTRACTS = "train.txt" |
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_VAL_ABSTRACTS = "validation.txt" |
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_TEST_ABSTRACTS = "test.txt" |
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BUILDER_CONFIGS = [ |
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HALSummarizationConfig( |
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name="hal", |
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version=datasets.Version("1.0.0"), |
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description="HAL dataset for summarization", |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=datasets.Features( |
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{ |
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_ARTICLE_ID: datasets.Value("string"), |
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_ARTICLE_WORDS: datasets.Sequence(datasets.Value("string")), |
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_ARTICLE_BBOXES: datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), |
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_ARTICLE_NORM_BBOXES: datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), |
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_ABSTRACT: datasets.Value("string"), |
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_ARTICLE_PDF_URL: datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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) |
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def _split_generators(self, dl_manager): |
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train_dir = os.path.join(dl_manager.download_and_extract(self._TRAIN_ARCHIVE), "train") |
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val_dir = os.path.join(dl_manager.download_and_extract(self._VAL_ARCHIVE), "val") |
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test_dir = os.path.join(dl_manager.download_and_extract(self._TEST_ARCHIVE), "test") |
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train_abstracts = dl_manager.download_and_extract(self._TRAIN_ABSTRACTS) |
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val_abstracts = dl_manager.download_and_extract(self._VAL_ABSTRACTS) |
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test_abstracts = dl_manager.download_and_extract(self._TEST_ABSTRACTS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"data_path": train_dir, "abstract_path": train_abstracts} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"data_path": val_dir, "abstract_path": val_abstracts} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"data_path": test_dir, "abstract_path": test_abstracts} |
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), |
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] |
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def _generate_examples(self, data_path, abstract_path): |
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"""Generate HALSummarization examples.""" |
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filenames = sorted(os.listdir(data_path)) |
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guid = 0 |
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with open(abstract_path, 'r') as abstract_file: |
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for line in tqdm(abstract_file, total=len(filenames), desc=f"Reading files in {data_path}"): |
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guid += 1 |
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item = json.loads(line) |
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fname = item["id"] + ".txt" |
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filepath = os.path.join(data_path, fname) |
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words = [] |
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bboxes = [] |
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norm_bboxes = [] |
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with open(filepath, encoding="utf-8") as f: |
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for line in f: |
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splits = line.split("\t") |
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word = splits[0] |
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bbox = splits[1:5] |
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bbox = [int(b) for b in bbox] |
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page_width, page_height = int(splits[5]), int(splits[6]) |
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norm_bbox = normalize_bbox(bbox, (page_width, page_height)) |
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words.append(word) |
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bboxes.append(bbox) |
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norm_bboxes.append(norm_bbox) |
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assert len(words) == len(bboxes) |
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assert len(bboxes) == len(norm_bboxes) |
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yield guid, { |
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_ARTICLE_ID: item["id"], |
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_ARTICLE_WORDS: words, |
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_ARTICLE_BBOXES: bboxes, |
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_ARTICLE_NORM_BBOXES: norm_bboxes, |
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_ABSTRACT: item["abstract"], |
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_ARTICLE_PDF_URL: item["pdf_url"], |
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} |
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