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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
name: string
source: string
raw_output: string
prediction: struct<name: string, variant_count: int64, total_count: int64, pronunciations: list<item: struct<pho (... 64 chars omitted)
child 0, name: string
child 1, variant_count: int64
child 2, total_count: int64
child 3, pronunciations: list<item: struct<phonetic: string, ipa: string, count: int64, probability: double>>
child 0, item: struct<phonetic: string, ipa: string, count: int64, probability: double>
child 0, phonetic: string
child 1, ipa: string
child 2, count: int64
child 3, probability: double
normalized_name_policy: string
exact_lookup_is_authoritative: bool
source_rows_indexed: int64
unique_variants: int64
source_sha256: string
unique_names: int64
fuzzy_lookup_is_candidate_only: bool
to
{'source': Value('string'), 'source_sha256': Value('string'), 'source_rows_indexed': Value('int64'), 'unique_names': Value('int64'), 'unique_variants': Value('int64'), 'normalized_name_policy': Value('string'), 'exact_lookup_is_authoritative': Value('bool'), 'fuzzy_lookup_is_candidate_only': 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
name: string
source: string
raw_output: string
prediction: struct<name: string, variant_count: int64, total_count: int64, pronunciations: list<item: struct<pho (... 64 chars omitted)
child 0, name: string
child 1, variant_count: int64
child 2, total_count: int64
child 3, pronunciations: list<item: struct<phonetic: string, ipa: string, count: int64, probability: double>>
child 0, item: struct<phonetic: string, ipa: string, count: int64, probability: double>
child 0, phonetic: string
child 1, ipa: string
child 2, count: int64
child 3, probability: double
normalized_name_policy: string
exact_lookup_is_authoritative: bool
source_rows_indexed: int64
unique_variants: int64
source_sha256: string
unique_names: int64
fuzzy_lookup_is_candidate_only: bool
to
{'source': Value('string'), 'source_sha256': Value('string'), 'source_rows_indexed': Value('int64'), 'unique_names': Value('int64'), 'unique_variants': Value('int64'), 'normalized_name_policy': Value('string'), 'exact_lookup_is_authoritative': Value('bool'), 'fuzzy_lookup_is_candidate_only': 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.
source string | source_sha256 string | source_rows_indexed int64 | unique_names int64 | unique_variants int64 | normalized_name_policy string | exact_lookup_is_authoritative bool | fuzzy_lookup_is_candidate_only bool |
|---|---|---|---|---|---|---|---|
data/train.jsonl | 92e8914062fec481914ae23734e0e020dd4cd33dd97c75f1ed6e738d6403c7ef | 395,386 | 105,418 | 117,088 | Unicode NFKC + casefold + retain alphanumeric characters | true | true |
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NetJets G2P continuation bundle
This repository is the reproducibility bundle for the NameCoach NetJets pronunciation-distribution experiments.
Model
The matching adapter is in Reza2kn/t5gemma-2-4b-netjets-balanced-0p1, based on google/t5gemma-2-4b-4b. The uploaded checkpoint-2472 contains the final LoRA adapter, optimizer state, scheduler state, RNG state, tokenizer, and trainer state so training can resume.
This run used QLoRA (4-bit NF4 base weights), 4 GPUs, per-GPU batch size 8, gradient accumulation 1, learning rate 1e-5, and 0.1 epoch from the grouped-mixture adapter.
Data and evaluation
source/: original NetJets, sim-effective, and sim-final XLSX inputs.data/: occurrence-level processed training data and source mixture metadata.data_balanced_mixture_50_50/: occurrence rows plus grouped-distribution rows used in the balanced signal run.eval/: sealed 1K evaluation inputs, direct-distribution outputs, sampling outputs, and score receipts.rag/: SQLite pronunciation index and its manifest.scripts/: builders, trainers, evaluators, and the RAG inference router.
The sealed 1K has no exact train-name overlap. Exact RAG lookup is authoritative only for known names; fuzzy retrieval is candidate-only for unseen names.
Important current results
The final adapter's 16-draw sealed sampling result is: parse 100%, top-1 target mode 50.1%, top-1 observed 58.1%, exact phonetic+IPA set 18.8%, exact variant count 34.2%, variant-set recall 55.6%, TV 0.5300.
The direct complete-distribution result is: parse 99.2%, exact full set 11.1%, exact variant count 36.3%, variant-set recall 50.1%, IPA mapping consistency 97.7%, TV 0.5653.
Resume outline
- Download the model repo and this bundle.
- Use
scripts/build_balanced_distribution_mixture.pyor the checked-in mixture files. - Continue with
scripts/train_t5gemma_distribution_multigpu.pyand the uploaded adapter/checkpoint. - Run both evaluators on the sealed inputs before comparing another run.
- Use
scripts/infer_t5gemma_rag.pyfor exact-known-name routing and fuzzy candidate hints.
Do not use exact RAG on the sealed unseen-name score; report known-name and unseen-name routes separately.
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