Datasets:

KavinduHansaka
/

gherkin-english-100k

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
name: string
schema: struct<input_text: string, gherkin: string, task: string, language: string, tags: string, difficulty: string, domain: string, created_utc: string, source: string>
shards: list<item: struct<path: string, bytes: int64, sha256: string, lines: int64>>
total_examples: int64
vs
input_text: string
gherkin: string
task: string
language: string
tags: list<item: string>
difficulty: string
domain: string
created_utc: string
source: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 543, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              name: string
              schema: struct<input_text: string, gherkin: string, task: string, language: string, tags: string, difficulty: string, domain: string, created_utc: string, source: string>
              shards: list<item: struct<path: string, bytes: int64, sha256: string, lines: int64>>
              total_examples: int64
              vs
              input_text: string
              gherkin: string
              task: string
              language: string
              tags: list<item: string>
              difficulty: string
              domain: string
              created_utc: string
              source: string

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Gherkin EN 100k

Summary:
English-only, 100k-sample synthetic dataset for converting free-form requirements/paragraphs to Gherkin (BDD) feature files. Generated in a GPT‑5 style (no chain-of-thought), with business-facing, testable scenarios.

5 shards × 20k each (JSONL), anonymized (no real personal names; neutral phrase "the user" used instead)
Each line schema:
- input_text (string): free-form requirement/spec paragraph
- gherkin (string): valid Gherkin with Feature, optional Rule/Background, 2–4 Scenarios or a Scenario Outline with Examples
- task (string): "gherkin_conversion"
- language (string): "en"
- tags (list[str]): e.g., @smoke, @regression, @api, @compliance
- difficulty (enum): easy|medium|hard
- domain (enum): ecommerce|banking|healthcare|travel|food_delivery|education|media|hr|analytics|saas
- created_utc (RFC3339 string)
- source (string): "synthetic-gpt5-style"

Statistics

Total examples: 100000
Shards: 5 × 20k
Approx size: 137.3 MB (JSONL raw)

Files

gherkin_en_20k_part1.jsonl
gherkin_en_20k_part2.jsonl
gherkin_en_20k_part3.jsonl
gherkin_en_20k_part4.jsonl
gherkin_en_20k_part5.jsonl
gherkin_en_100k_manifest.json

Intended use

Train/finetune student models via distillation for requirements → Gherkin conversion.
Pretraining/adapter training for BDD-aware assistants.

License

apache-2.0

Citation

If you use this dataset, please cite:

Kavindu Hansaka Jayasinghe (2025). Gherkin EN 100k.

Downloads last month: 13

Total file size:

137 MB