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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
                  for key, record in generator:
                                     ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
                  for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
                                              ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  for filename, f in tar_iterator:
                                     ^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
                  for x in self.generator(*self.args):
                           ~~~~~~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1400, in _iter_from_urlpath
                  with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
                       ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 977, in xopen
                  file_obj = fs.open(paths[0], mode)
                File "<string>", line 3, in open
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
                  return self._mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
                  return self._execute_mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
                  result = effect(*args, **kwargs)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
                  tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
                                                               ~~~^^^^^^^^
              TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1393, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1571, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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RWDE

RWDE is a webpage information extraction dataset built from English Common Crawl pages. Pages are organized by website and page-structure group, with extraction queries, JSON schemas, reference XPath locators, model-generated supervision, and field-level ground truth.

The dataset contains two parts:

  • SFT: data used for supervised fine-tuning, including node-identification artifacts and generated scraper programs.
  • RFT: data used for reinforcement fine-tuning, including answer-item and support-item annotations.

The current release excludes queries that lack extracted HTML outputs in SFT or item-level ground truth in RFT.

Statistics

Split Sites Groups Queries Raw HTML pages Query-pages Extracted HTML files Scraper samples
SFT 151 367 548 26,469 38,919 30,359 46,585
RFT 33 95 95 3,826 3,826 2,777 β€”
Total 184 462 643 30,295 42,745 33,136 46,585

A query-page is one page evaluated under one query. A raw HTML page appearing in multiple queries is counted once for each query. RFT additionally contains 3,396 item-level ground-truth JSON files.

Download and extraction

The Hugging Face release contains three archives plus this README, a machine-readable manifest, and SHA-256 checksums:

SFT.tar.gz
RFT.tar.gz
groundtruth.tar.gz
manifest.json
SHA256SUMS

Each archive contains its corresponding top-level directory. To reconstruct the complete dataset:

sha256sum -c SHA256SUMS
tar -xzf SFT.tar.gz
tar -xzf RFT.tar.gz
tar -xzf groundtruth.tar.gz

The command sha256sum -c SHA256SUMS must be run from the directory containing the downloaded release files, before moving or extracting the archives.

Directory structure

RWDE/
β”œβ”€β”€ README.md
β”œβ”€β”€ SFT/
β”‚   └── <website>/<group_id>/
β”‚       β”œβ”€β”€ query.jsonl
β”‚       β”œβ”€β”€ raw_html/
β”‚       β”‚   └── XXXX.html
β”‚       β”œβ”€β”€ simplified_html/
β”‚       β”‚   └── XXXX_simplified.html
β”‚       β”œβ”€β”€ mapped_html/
β”‚       β”‚   └── XXXX_mapped.html
β”‚       └── extracted_html/
β”‚           └── <query_id>/
β”‚               β”œβ”€β”€ XXXX_extracted.html
β”‚               β”œβ”€β”€ XXXX_ids.json
β”‚               └── scraper.jsonl
β”œβ”€β”€ RFT/
β”‚   └── <website>/<group_id>/
β”‚       β”œβ”€β”€ query.jsonl
β”‚       β”œβ”€β”€ raw_html/
β”‚       β”œβ”€β”€ simplified_html/
β”‚       β”œβ”€β”€ mapped_html/
β”‚       └── extracted_html/
β”‚           β”œβ”€β”€ <query_id>/
β”‚           β”‚   β”œβ”€β”€ XXXX_extracted.html
β”‚           β”‚   └── XXXX_ids.json
β”‚           └── <query_id>_gt/
β”‚               └── XXXX.json
└── groundtruth/
    β”œβ”€β”€ SFT/<website>/<group_id>/<query_id>/<field_name>.txt
    └── RFT/<website>/<group_id>/<query_id>/<field_name>.txt

scraper.jsonl is present only for queries with generated scraper samples. In SFT, xxxtickling.com/group000/q01 and xxxtickling.com/group000/q02 do not have this file; the other 546 SFT queries do.

Query definitions

Each group has a query.jsonl file with one JSON object per query. Queries are selected from the passing audit results.

{
  "query_id": "q01",
  "query": "Extraction query in natural language",
  "schema": {
    "type": "object",
    "properties": {}
  },
  "reference_xpath": {
    "field_name": {
      "selectors": [
        {
          "xpath": "//main//h1",
          "value_mode": "text",
          "attribute": null
        }
      ],
      "combine": "direct",
      "separator": null
    }
  }
}
  • schema: JSON Schema for the expected extraction result.
  • reference_xpath: field name to locator configuration. A field can have multiple selectors and a composition rule.
  • value_mode: text extracts visible node text; attribute extracts the named attribute.

HTML and node-identification artifacts

  • raw_html/XXXX.html: original page HTML.
  • simplified_html/XXXX_simplified.html: simplified HTML with item identifiers used during preprocessing.
  • mapped_html/XXXX_mapped.html: mapped preprocessing representation.
  • extracted_html/<query_id>/XXXX_extracted.html: query-specific HTML retained after node selection.
  • extracted_html/<query_id>/XXXX_ids.json: model output for node identification.

Each XXXX_ids.json contains exactly:

{
  "reasoning": "Model reasoning",
  "output": "Raw model output",
  "useful_item_ids": ["17", "19", "24"]
}

Scraper samples

For SFT, scraper.jsonl contains one generated scraper sample per line with exactly these fields:

{
  "uid": "website/group000/q01/0000-0013-0032",
  "domain": "website",
  "group": "group000",
  "query_id": "q01",
  "seed_page_ids": ["0000", "0013", "0032"],
  "reasoning": "Model reasoning",
  "output": "Generated scraper source code"
}

Rows are preserved from the source artifacts. The same uid can occur multiple times with different reasoning or output, so uid is not guaranteed to be unique. Review generated code before executing it.

RFT item-level ground truth

In RFT, extracted_html/<query_id>_gt/XXXX.json aligns fields with annotated HTML items:

{
  "answer_item_ids": {
    "field_name": [17, 19]
  },
  "support_item_ids": [23, 27]
}
  • answer_item_ids: item IDs containing direct answers for each field.
  • support_item_ids: nearby or page-level items providing labels, structure, or disambiguating context.

Field-level ground truth

Ground truth is split into groundtruth/SFT and groundtruth/RFT, then organized by website, group, query, and field. SFT values are materialized with the reference XPath locators. RFT values come from the extracted standard answers.

Each <field_name>.txt uses an SWDE/LiveWeb-IE-style text format. The first two lines are headers; data rows begin on line 3:

SFT 24karotgoldcasino.com group000 q02 article_title
173 173 173
0000 "http://24karotgoldcasino.com/2018/06/15/washington-casinos-2/" 1 Washington Casinos
0001 "http://24karotgoldcasino.com/2010/04/22/wetten-a-atlantic-city-2/" 1 Wetten a Atlantic City

Each data row has this form:

<page_id> "<page_url>" <value_count> <value_1>\t<value_2>\t...
  • Scalar fields normally have value_count 1.
  • Array fields contain multiple values separated by tab characters.
  • Missing scalar values and empty arrays have value_count 0 and no payload.
  • Values are stored as text; use query.jsonl for their expected JSON types.

The two header lines contain:

  1. <split> <website> <group_id> <query_id> <field_name>
  2. <page_count> <total_value_count> <total_value_count>

License

The dataset compilation, query definitions, ground-truth annotations, and model-generated artifacts are released under the Creative Commons Attribution 4.0 International License.

You may copy, use, modify, and redistribute these materials, including for commercial purposes, provided that you give appropriate credit, provide a link to the license, and indicate whether you made changes. When using RWDE, please cite the associated paper and this Hugging Face repository.

Raw webpages and other third-party content remain subject to the rights and terms of their original owners. The CC BY 4.0 license applies only to materials for which the RWDE authors can grant rights and does not grant additional rights to third-party webpage content.

Page ID mapping

The four-digit page ID links every representation of the same page. For example, page 0000 can map to:

raw_html/0000.html
simplified_html/0000_simplified.html
mapped_html/0000_mapped.html
extracted_html/<query_id>/0000_extracted.html
extracted_html/<query_id>/0000_ids.json
extracted_html/<query_id>_gt/0000.json

Query-specific files exist only when the corresponding page was retained or annotated for that query. In every field-level ground-truth file, the same page ID is followed by the source page URL.

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