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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ClientPayloadError
Message:      400, message='Can not decode content-encoding: gzip'
Traceback:    Traceback (most recent call last):
                File "/src/workers/datasets_based/src/datasets_based/workers/", line 485, in compute_first_rows_response
                  rows = get_rows(
                File "/src/workers/datasets_based/src/datasets_based/workers/", line 120, in decorator
                  return func(*args, **kwargs)
                File "/src/workers/datasets_based/src/datasets_based/workers/", line 176, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/", line 917, in __iter__
                  for key, example in ex_iterable:
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/", line 113, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/e2e_nlg_cleaned/67671f9bb629768e0ae9847287710dc6e0776aef52039fedbd42958ee0957cbd/", line 89, in _generate_examples
                  for example_idx, example in enumerate(reader):
                File "/usr/local/lib/python3.9/", line 111, in __next__
                  row = next(self.reader)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/implementations/", line 594, in read
                  return super().read(length)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/", line 1684, in read
                  out = self.cache._fetch(self.loc, self.loc + length)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/", line 381, in _fetch
                  self.cache = self.fetcher(start, bend)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/", line 114, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/", line 99, in sync
                  raise return_result
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/", line 54, in _runner
                  result[0] = await coro
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/implementations/", line 663, in async_fetch_range
                  out = await
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/aiohttp/", line 1037, in read
                  self._body = await
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/aiohttp/", line 375, in read
                  block = await self.readany()
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/aiohttp/", line 397, in readany
                  await self._wait("readany")
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/aiohttp/", line 304, in _wait
                  await waiter
              aiohttp.client_exceptions.ClientPayloadError: 400, message='Can not decode content-encoding: gzip'

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Dataset Card for the Cleaned Version of the E2E Dataset

Dataset Summary

An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper:

The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances.

E2E is released in the following paper where you can find more details and baseline results:

Supported Tasks and Leaderboards

  • text2text-generation-other-meaning-representtion-to-text: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a high BLEU, NIST, METEOR, Rouge-L, CIDEr.

This task has an inactive leaderboard which can be found here and ranks models based on the metrics above.


The dataset is in english (en).

Dataset Structure

Data Instances

Example of one instance:

{'human_reference': 'The Vaults pub near Café Adriatic has a 5 star rating.  Prices start at £30.',
 'meaning_representation': 'name[The Vaults], eatType[pub], priceRange[more than £30], customer rating[5 out of 5], near[Café Adriatic]'}

Data Fields

  • human_reference: string, the text is natural language that describes the different characteristics in the meaning representation
  • meaning_representation: list of slots and values to generate a description from

Each MR consists of 3–8 attributes (slots), such as name, food or area, and their values.

Data Splits

The dataset is split into training, validation and testing sets (in a 76.5-8.5-15 ratio), keeping a similar distribution of MR and reference text lengths and ensuring that MRs in different sets are distinct.

train validation test
N. Instances 33525 4299 4693

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

The data was collected using the CrowdFlower platform and quality-controlled following Novikova et al. (2016).

Who are the source language producers?

[More Information Needed]


Following Novikova et al. (2016), the E2E data was collected using pictures as stimuli, which was shown to elicit significantly more natural, more informative, and better phrased human references than textual MRs.

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

  title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
  author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
  year = {2020},
  month = jan,
  volume = {59},
  pages = {123--156},
  doi = {10.1016/j.csl.2019.06.009},
  archivePrefix = {arXiv},
  eprint = {1901.11528},
  eprinttype = {arxiv},
  journal = {Computer Speech \& Language}


Thanks to @yjernite for adding this dataset.

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