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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
text: string
appropriateness: int64
judge_model: string
intent: int64
machine_generated: bool
system: string
item_id: string
judge: string
to
{'item_id': Value('string'), 'system': Value('string'), 'intent': Value('int64'), 'appropriateness': Value('int64'), 'judge': Value('string'), 'judge_model': Value('string'), 'machine_generated': Value('bool')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table 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/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
text: string
appropriateness: int64
judge_model: string
intent: int64
machine_generated: bool
system: string
item_id: string
judge: string
to
{'item_id': Value('string'), 'system': Value('string'), 'intent': Value('int64'), 'appropriateness': Value('int64'), 'judge': Value('string'), 'judge_model': Value('string'), 'machine_generated': Value('bool')}
because column names don't match
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 1694, 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 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
item_id string | system string | intent int64 | appropriateness int64 | judge string | judge_model string | machine_generated bool |
|---|---|---|---|---|---|---|
syn-12 | nllb | 1 | 1 | fable-agent | claude-fable-5 | true |
syn-14 | small_naive | 2 | 3 | fable-agent | claude-fable-5 | true |
lit-20 | nllb | 2 | 2 | fable-agent | claude-fable-5 | true |
syn-27 | nllb | 3 | 3 | fable-agent | claude-fable-5 | true |
syn-30 | small_naive | 3 | 5 | fable-agent | claude-fable-5 | true |
syn-25 | small_naive | 2 | 4 | fable-agent | claude-fable-5 | true |
lit-13 | small_naive | 3 | 6 | fable-agent | claude-fable-5 | true |
lit-20 | frontier_instructional | 1 | 2 | fable-agent | claude-fable-5 | true |
syn-25 | nllb | 3 | 4 | fable-agent | claude-fable-5 | true |
lit-06 | small_naive | 1 | 3 | fable-agent | claude-fable-5 | true |
lit-13 | nllb | 2 | 2 | fable-agent | claude-fable-5 | true |
lit-07 | frontier_instructional | 2 | 4 | fable-agent | claude-fable-5 | true |
lit-06 | small_audience | 1 | 3 | fable-agent | claude-fable-5 | true |
lit-17 | nllb | 2 | 2 | fable-agent | claude-fable-5 | true |
syn-32 | frontier_naive | 2 | 5 | fable-agent | claude-fable-5 | true |
lit-10 | small_naive | 2 | 4 | fable-agent | claude-fable-5 | true |
syn-31 | nllb | 2 | 3 | fable-agent | claude-fable-5 | true |
lit-16 | frontier_instructional | 3 | 7 | fable-agent | claude-fable-5 | true |
syn-23 | small_naive | 2 | 4 | fable-agent | claude-fable-5 | true |
syn-20 | small_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
syn-12 | frontier_naive | 2 | 3 | fable-agent | claude-fable-5 | true |
syn-01 | small_naive | 1 | 2 | fable-agent | claude-fable-5 | true |
lit-14 | frontier_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
syn-30 | frontier_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
syn-23 | frontier_naive | 2 | 3 | fable-agent | claude-fable-5 | true |
syn-32 | frontier_instructional | 2 | 1 | fable-agent | claude-fable-5 | true |
syn-30 | small_audience | 3 | 5 | fable-agent | claude-fable-5 | true |
seed-08 | small_naive | 2 | 3 | fable-agent | claude-fable-5 | true |
syn-31 | small_naive | 3 | 5 | fable-agent | claude-fable-5 | true |
syn-30 | frontier_instructional | 3 | 6 | fable-agent | claude-fable-5 | true |
syn-24 | small_naive | 2 | 3 | fable-agent | claude-fable-5 | true |
syn-01 | frontier_audience | 2 | 6 | fable-agent | claude-fable-5 | true |
syn-25 | frontier_naive | 3 | 4 | fable-agent | claude-fable-5 | true |
syn-27 | frontier_instructional | 3 | 7 | fable-agent | claude-fable-5 | true |
syn-31 | frontier_naive | 3 | 4 | fable-agent | claude-fable-5 | true |
lit-17 | frontier_audience | 1 | 1 | fable-agent | claude-fable-5 | true |
lit-06 | frontier_naive | 1 | 3 | fable-agent | claude-fable-5 | true |
seed-02 | small_naive | 2 | 4 | fable-agent | claude-fable-5 | true |
lit-06 | frontier_instructional | 1 | 2 | fable-agent | claude-fable-5 | true |
lit-10 | frontier_naive | 3 | 5 | fable-agent | claude-fable-5 | true |
syn-19 | frontier_instructional | 1 | 1 | fable-agent | claude-fable-5 | true |
lit-10 | nllb | 2 | 2 | fable-agent | claude-fable-5 | true |
lit-05 | frontier_naive | 2 | 4 | fable-agent | claude-fable-5 | true |
syn-22 | small_naive | 3 | 5 | fable-agent | claude-fable-5 | true |
syn-07 | small_naive | 2 | 4 | fable-agent | claude-fable-5 | true |
syn-26 | small_audience | 3 | 5 | fable-agent | claude-fable-5 | true |
lit-20 | frontier_naive | 1 | 1 | fable-agent | claude-fable-5 | true |
syn-01 | nllb | 1 | 1 | fable-agent | claude-fable-5 | true |
syn-23 | frontier_instructional | 3 | 6 | fable-agent | claude-fable-5 | true |
syn-07 | frontier_naive | 3 | 5 | fable-agent | claude-fable-5 | true |
syn-32 | small_naive | 3 | 4 | fable-agent | claude-fable-5 | true |
syn-19 | small_naive | 2 | 3 | fable-agent | claude-fable-5 | true |
syn-31 | frontier_audience | 2 | 4 | fable-agent | claude-fable-5 | true |
syn-20 | frontier_naive | 3 | 4 | fable-agent | claude-fable-5 | true |
seed-08 | small_audience | 2 | 4 | fable-agent | claude-fable-5 | true |
lit-18 | small_naive | 1 | 1 | fable-agent | claude-fable-5 | true |
syn-22 | frontier_instructional | 1 | 1 | fable-agent | claude-fable-5 | true |
lit-10 | small_audience | 2 | 3 | fable-agent | claude-fable-5 | true |
lit-08 | small_naive | 2 | 2 | fable-agent | claude-fable-5 | true |
seed-08 | frontier_instructional | 2 | 3 | fable-agent | claude-fable-5 | true |
lit-06 | frontier_audience | 3 | 5 | fable-agent | claude-fable-5 | true |
lit-16 | small_audience | 3 | 4 | fable-agent | claude-fable-5 | true |
syn-24 | nllb | 1 | 2 | fable-agent | claude-fable-5 | true |
syn-07 | nllb | 2 | 2 | fable-agent | claude-fable-5 | true |
lit-14 | small_audience | 3 | 7 | fable-agent | claude-fable-5 | true |
seed-02 | nllb | 3 | 6 | fable-agent | claude-fable-5 | true |
lit-08 | nllb | 1 | 1 | fable-agent | claude-fable-5 | true |
syn-26 | frontier_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
syn-07 | small_audience | 3 | 7 | fable-agent | claude-fable-5 | true |
syn-20 | small_naive | 3 | 6 | fable-agent | claude-fable-5 | true |
syn-22 | nllb | 3 | 3 | fable-agent | claude-fable-5 | true |
syn-19 | small_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
lit-13 | frontier_naive | 3 | 4 | fable-agent | claude-fable-5 | true |
syn-19 | nllb | 1 | 2 | fable-agent | claude-fable-5 | true |
lit-05 | frontier_audience | 3 | 7 | fable-agent | claude-fable-5 | true |
syn-24 | small_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
lit-18 | frontier_instructional | 3 | 7 | fable-agent | claude-fable-5 | true |
seed-02 | small_audience | 3 | 7 | fable-agent | claude-fable-5 | true |
syn-14 | small_audience | 2 | 2 | fable-agent | claude-fable-5 | true |
seed-08 | nllb | 1 | 1 | fable-agent | claude-fable-5 | true |
syn-27 | frontier_naive | 2 | 3 | fable-agent | claude-fable-5 | true |
lit-06 | nllb | 2 | 3 | fable-agent | claude-fable-5 | true |
lit-07 | small_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
lit-17 | small_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
lit-14 | nllb | 2 | 3 | fable-agent | claude-fable-5 | true |
syn-27 | small_audience | 2 | 5 | fable-agent | claude-fable-5 | true |
lit-18 | nllb | 2 | 3 | fable-agent | claude-fable-5 | true |
lit-05 | nllb | 2 | 4 | fable-agent | claude-fable-5 | true |
syn-31 | small_audience | 3 | 5 | fable-agent | claude-fable-5 | true |
syn-32 | small_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
lit-11 | nllb | 1 | 2 | fable-agent | claude-fable-5 | true |
syn-19 | frontier_naive | 2 | 4 | fable-agent | claude-fable-5 | true |
syn-02 | small_audience | 2 | 3 | fable-agent | claude-fable-5 | true |
lit-10 | frontier_instructional | 2 | 4 | fable-agent | claude-fable-5 | true |
lit-16 | frontier_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
syn-24 | frontier_audience | 2 | 4 | fable-agent | claude-fable-5 | true |
lit-07 | small_naive | 2 | 4 | fable-agent | claude-fable-5 | true |
lit-16 | small_naive | 3 | 4 | fable-agent | claude-fable-5 | true |
lit-08 | frontier_audience | 3 | 6 | fable-agent | claude-fable-5 | true |
syn-02 | small_naive | 2 | 3 | fable-agent | claude-fable-5 | true |
End of preview.
Sawda
A pilot benchmark for intent-preserving translation of culturally loaded trade communication between Afghan Dari and English. 60 native-validated fictional items; 324 translations from six open-weights systems; 180 blind human judgments by a native Dari speaker; two LLM-judge calibration sets.
Paper and code: https://github.com/MurtazaKafka/sawda-bench
Files
| file | contents |
|---|---|
data/items.jsonl |
60 validated items: id, direction (fa2en/en2fa), utterance_src, context, literal_render, intent_render, pragmatic_note, failure_class, tranche (seed/lit/synth), lang_code |
data/translations.jsonl |
324 system outputs (54 eval items × 6 systems), verbatim, with measured token usage; 4 empty outputs flagged |
data/human_judgments.jsonl |
180 blind native-speaker judgments: intent (3/2/1), appropriateness (1–7), notes, quick-flags |
data/llm_judgments.jsonl, data/llm_judgments_rubric_v1.jsonl |
DeepSeek v4-pro judge calibration (rubric v2 and v1) |
data/fable_judgments.jsonl |
Claude Fable 5 panel calibration |
data/heldout_ids.json |
6 items held out as few-shot exemplars (excluded from evaluation) |
data/judging_plan.json |
The randomized blind judging order |
drafts/*.jsonl |
Pre-validation drafts, released for diffing against validated items |
Data statement (summary)
- Language variety: the Persian side is Afghan Dari (all items carry
lang_code: prs_Arab); Iranian Farsi appears only as an unwanted output register in system translations. Direction field namesfa2en/en2faare legacy labels denoting Dari. - Provenance: 8 seed items written by a native Dari speaker from lived trade/family-business communication; 20 items grounded in the taarof and business-pragmatics literatures; 32 validated synthetic variants. All items are fictional: no real names, businesses, or identifying details.
- Annotation: a single native Dari-speaking validator/judge (the benchmark author). Judgments are blind to system identity but not item provenance. No inter-annotator agreement is available yet.
- Known limitations: pilot scale (30 judged items); machine judge labels failed calibration (κ ≤ 0.17) and are released as calibration data only, not evaluation labels.
Ethics
Dari materials involve communities under active persecution. All items are fictional. See the paper's Ethical Considerations section.
Citation
Paper draft in the GitHub repo (paper/). Formal citation forthcoming.
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