--- dataset_info: - config_name: auto features: - name: id dtype: string - name: context dtype: string - name: fields dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 11238415 num_examples: 2000 - name: validation num_bytes: 1413271 num_examples: 250 - name: test num_bytes: 967686 num_examples: 250 download_size: 3083883 dataset_size: 13619372 - config_name: book features: - name: id dtype: string - name: context dtype: string - name: fields dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 10607037 num_examples: 2000 - name: validation num_bytes: 1154204 num_examples: 250 - name: test num_bytes: 889597 num_examples: 250 download_size: 4421087 dataset_size: 12650838 - config_name: camera features: - name: id dtype: string - name: context dtype: string - name: fields dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 12385911 num_examples: 1967 - name: validation num_bytes: 1460617 num_examples: 250 - name: test num_bytes: 1358161 num_examples: 234 download_size: 3991272 dataset_size: 15204689 - config_name: job features: - name: id dtype: string - name: context dtype: string - name: fields dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 9442196 num_examples: 2000 - name: validation num_bytes: 1496159 num_examples: 250 - name: test num_bytes: 837263 num_examples: 250 download_size: 4275553 dataset_size: 11775618 - config_name: movie features: - name: id dtype: string - name: context dtype: string - name: fields dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 8255353 num_examples: 2000 - name: validation num_bytes: 772403 num_examples: 250 - name: test num_bytes: 1212301 num_examples: 250 download_size: 4180518 dataset_size: 10240057 - config_name: nbaplayer features: - name: id dtype: string - name: context dtype: string - name: fields dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 10604602 num_examples: 2000 - name: validation num_bytes: 1240665 num_examples: 250 - name: test num_bytes: 1575637 num_examples: 250 download_size: 4043722 dataset_size: 13420904 - config_name: restaurant features: - name: id dtype: string - name: context dtype: string - name: fields dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 9732954 num_examples: 2000 - name: validation num_bytes: 1368139 num_examples: 250 - name: test num_bytes: 968450 num_examples: 250 download_size: 3831925 dataset_size: 12069543 - config_name: university features: - name: id dtype: string - name: context dtype: string - name: fields dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 8480872 num_examples: 2000 - name: validation num_bytes: 1615878 num_examples: 250 - name: test num_bytes: 922002 num_examples: 250 download_size: 2706175 dataset_size: 11018752 configs: - config_name: auto data_files: - split: train path: auto/train-* - split: validation path: auto/validation-* - split: test path: auto/test-* - config_name: book data_files: - split: train path: book/train-* - split: validation path: book/validation-* - split: test path: book/test-* - config_name: camera data_files: - split: train path: camera/train-* - split: validation path: camera/validation-* - split: test path: camera/test-* - config_name: job data_files: - split: train path: job/train-* - split: validation path: job/validation-* - split: test path: job/test-* - config_name: movie data_files: - split: train path: movie/train-* - split: validation path: movie/validation-* - split: test path: movie/test-* - config_name: nbaplayer data_files: - split: train path: nbaplayer/train-* - split: validation path: nbaplayer/validation-* - split: test path: nbaplayer/test-* - config_name: restaurant data_files: - split: train path: restaurant/train-* - split: validation path: restaurant/validation-* - split: test path: restaurant/test-* - config_name: university data_files: - split: train path: university/train-* - split: validation path: university/validation-* - split: test path: university/test-* license: other source_datasets: [] tags: - decision-model --- # swde SWDE (Hao et al., SIGIR 2011) attribute records over the visible text of real web pages. SWDE has 124,291 product, book, job, movie, player, restaurant, car and university pages from 80 websites (8 verticals, 10 sites each), each page one entity, with page-level values of 3 to 5 attributes per vertical labelled by per-site handcrafted regular expressions over DOM text nodes (procedural labels; ```` when the page has no value). Each page's HTML is converted to visible text (``VisibleText``) and every value is located verbatim, word-bounded, after decoding character references and collapsing whitespace; attributes with an unlocated value are left out, never turned into nulls. Pages longer than ``window`` characters are cut to one window holding every located value. One config per vertical; one row per page with ``fields`` = ``{attribute: [[(start, value), ...] per distinct value]}`` (JSON, empty for a labelled null). SWDE has no official split: each vertical's ten sites are split by hash (8 train, 1 validation, 1 test; ``swde_split``), so evaluation websites are unseen. Pages are template-generated, so each site contributes its ``pages_per_site`` lowest-hash eligible pages. Original data: [original distribution](http://web.archive.org/web/20210630013015id_/https://codeplexarchive.blob.core.windows.net/archive/projects/swde/swde.zip). Repackaged as parquet for [tasksource](https://github.com/sileod/tasksource) by [scripts/repackage_dataset/web_extraction.py](https://github.com/sileod/tasksource/blob/main/scripts/repackage_dataset/web_extraction.py#L440). [Conversion provenance](provenance.json): source revisions, hashes, configuration and exclusions.