Datasets:
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
built_utc: string
stage: string
results: struct<scalar_health: struct<ok: bool, n_domains: int64, domains: list<item: struct<domain: string, (... 2275 chars omitted)
child 0, scalar_health: struct<ok: bool, n_domains: int64, domains: list<item: struct<domain: string, S: double, ok: bool, q (... 12 chars omitted)
child 0, ok: bool
child 1, n_domains: int64
child 2, domains: list<item: struct<domain: string, S: double, ok: bool, qm: double>>
child 0, item: struct<domain: string, S: double, ok: bool, qm: double>
child 0, domain: string
child 1, S: double
child 2, ok: bool
child 3, qm: double
child 1, lexicon: struct<ok: bool, size: int64>
child 0, ok: bool
child 1, size: int64
child 2, real_gold: struct<ok: bool, n: int64, mean_map: double, mean_semantic: double, cases: list<item: struct<id: str (... 126 chars omitted)
child 0, ok: bool
child 1, n: int64
child 2, mean_map: double
child 3, mean_semantic: double
child 4, cases: list<item: struct<id: string, exact_map_rate: double, semantic_coverage: double, S: double, tokens: (... 50 chars omitted)
child 0, item: struct<id: string, exact_map_rate: double, semantic_coverage: double, S: double, tokens: list<item: (... 38 chars omitted)
child 0, id: string
child 1, exact_map_rate: double
child 2, semantic_coverage: double
child 3, S: do
...
uct<exact: double, partial: double, n: int64>
child 0, exact: double
child 1, partial: double
child 2, n: int64
delta_partial_90_10: double
production_lexicon_keys: int64
waveforms: list<item: struct<text: string, engine: string, path: string>>
child 0, item: struct<text: string, engine: string, path: string>
child 0, text: string
child 1, engine: string
child 2, path: string
prior_partial_90_10: double
content_85_15: struct<exact: double, partial: double, n: int64, by_lang: struct<grc: struct<n: int64, exact: double (... 183 chars omitted)
child 0, exact: double
child 1, partial: double
child 2, n: int64
child 3, by_lang: struct<grc: struct<n: int64, exact: double, partial: double>, la: struct<n: int64, exact: double, pa (... 124 chars omitted)
child 0, grc: struct<n: int64, exact: double, partial: double>
child 0, n: int64
child 1, exact: double
child 2, partial: double
child 1, la: struct<n: int64, exact: double, partial: double>
child 0, n: int64
child 1, exact: double
child 2, partial: double
child 2, ang: struct<n: int64, exact: double, partial: double>
child 0, n: int64
child 1, exact: double
child 2, partial: double
child 3, en: struct<n: int64, exact: double, partial: double>
child 0, n: int64
child 1, exact: double
child 2, partial: double
stack: string
pul_terms: int64
paradigm_terms: int64
to
{'built_utc': Value('string'), 'n_gold': Value('int64'), 'n_content_gold': Value('int64'), 'open_90_10': {'exact': Value('float64'), 'partial': Value('float64'), 'n': Value('int64'), 'by_lang': {'la': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'grc': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'ang': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'en': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}}}, 'open_85_15': {'exact': Value('float64'), 'partial': Value('float64'), 'n': Value('int64')}, 'content_85_15': {'exact': Value('float64'), 'partial': Value('float64'), 'n': Value('int64'), 'by_lang': {'grc': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'la': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'ang': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'en': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}}}, 'train_closed_sample': {'exact': Value('float64'), 'n': Value('int64')}, 'pul_terms': Value('int64'), 'paradigm_terms': Value('int64'), 'production_lexicon_keys': Value('int64'), 'prior_partial_90_10': Value('float64'), 'delta_partial_90_10': Value('float64'), 'waveforms': List({'text': Value('string'), 'engine': Value('string'), 'path': Value('string')}), 'stack': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
built_utc: string
stage: string
results: struct<scalar_health: struct<ok: bool, n_domains: int64, domains: list<item: struct<domain: string, (... 2275 chars omitted)
child 0, scalar_health: struct<ok: bool, n_domains: int64, domains: list<item: struct<domain: string, S: double, ok: bool, q (... 12 chars omitted)
child 0, ok: bool
child 1, n_domains: int64
child 2, domains: list<item: struct<domain: string, S: double, ok: bool, qm: double>>
child 0, item: struct<domain: string, S: double, ok: bool, qm: double>
child 0, domain: string
child 1, S: double
child 2, ok: bool
child 3, qm: double
child 1, lexicon: struct<ok: bool, size: int64>
child 0, ok: bool
child 1, size: int64
child 2, real_gold: struct<ok: bool, n: int64, mean_map: double, mean_semantic: double, cases: list<item: struct<id: str (... 126 chars omitted)
child 0, ok: bool
child 1, n: int64
child 2, mean_map: double
child 3, mean_semantic: double
child 4, cases: list<item: struct<id: string, exact_map_rate: double, semantic_coverage: double, S: double, tokens: (... 50 chars omitted)
child 0, item: struct<id: string, exact_map_rate: double, semantic_coverage: double, S: double, tokens: list<item: (... 38 chars omitted)
child 0, id: string
child 1, exact_map_rate: double
child 2, semantic_coverage: double
child 3, S: do
...
uct<exact: double, partial: double, n: int64>
child 0, exact: double
child 1, partial: double
child 2, n: int64
delta_partial_90_10: double
production_lexicon_keys: int64
waveforms: list<item: struct<text: string, engine: string, path: string>>
child 0, item: struct<text: string, engine: string, path: string>
child 0, text: string
child 1, engine: string
child 2, path: string
prior_partial_90_10: double
content_85_15: struct<exact: double, partial: double, n: int64, by_lang: struct<grc: struct<n: int64, exact: double (... 183 chars omitted)
child 0, exact: double
child 1, partial: double
child 2, n: int64
child 3, by_lang: struct<grc: struct<n: int64, exact: double, partial: double>, la: struct<n: int64, exact: double, pa (... 124 chars omitted)
child 0, grc: struct<n: int64, exact: double, partial: double>
child 0, n: int64
child 1, exact: double
child 2, partial: double
child 1, la: struct<n: int64, exact: double, partial: double>
child 0, n: int64
child 1, exact: double
child 2, partial: double
child 2, ang: struct<n: int64, exact: double, partial: double>
child 0, n: int64
child 1, exact: double
child 2, partial: double
child 3, en: struct<n: int64, exact: double, partial: double>
child 0, n: int64
child 1, exact: double
child 2, partial: double
stack: string
pul_terms: int64
paradigm_terms: int64
to
{'built_utc': Value('string'), 'n_gold': Value('int64'), 'n_content_gold': Value('int64'), 'open_90_10': {'exact': Value('float64'), 'partial': Value('float64'), 'n': Value('int64'), 'by_lang': {'la': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'grc': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'ang': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'en': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}}}, 'open_85_15': {'exact': Value('float64'), 'partial': Value('float64'), 'n': Value('int64')}, 'content_85_15': {'exact': Value('float64'), 'partial': Value('float64'), 'n': Value('int64'), 'by_lang': {'grc': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'la': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'ang': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}, 'en': {'n': Value('int64'), 'exact': Value('float64'), 'partial': Value('float64')}}}, 'train_closed_sample': {'exact': Value('float64'), 'n': Value('int64')}, 'pul_terms': Value('int64'), 'paradigm_terms': Value('int64'), 'production_lexicon_keys': Value('int64'), 'prior_partial_90_10': Value('float64'), 'delta_partial_90_10': Value('float64'), 'waveforms': List({'text': Value('string'), 'engine': Value('string'), 'path': Value('string')}), 'stack': Value('string')}
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 1683, 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 1869, 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.
built_utc string | n_gold int64 | n_content_gold int64 | open_90_10 dict | open_85_15 dict | content_85_15 dict | train_closed_sample dict | pul_terms int64 | paradigm_terms int64 | production_lexicon_keys int64 | prior_partial_90_10 float64 | delta_partial_90_10 float64 | waveforms list | stack string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2026-07-18T05:16:22.625794+00:00 | 51,821 | 36,041 | {
"exact": 0.024296919839295963,
"partial": 0.16606083795676296,
"n": 5227,
"by_lang": {
"la": {
"n": 2432,
"exact": 0.016858552631578948,
"partial": 0.18914473684210525
},
"grc": {
"n": 1481,
"exact": 0.03308575286968265,
"partial": 0.18771100607697502
},
... | {
"exact": 0.02281708094327597,
"partial": 0.16137667304015296,
"n": 7845
} | {
"exact": 0.03432700993676603,
"partial": 0.13785004516711835,
"n": 5535,
"by_lang": {
"grc": {
"n": 1988,
"exact": 0.04024144869215292,
"partial": 0.1750503018108652
},
"la": {
"n": 1826,
"exact": 0.030120481927710843,
"partial": 0.1276013143483023
},
"a... | {
"exact": 0.9766666666666667,
"n": 1200
} | 73,736 | 94,914 | 66,318 | 0.1875 | -0.021439 | [
{
"text": "aqua",
"engine": "sapi_wav",
"path": "D:\\training data\\pflt_linguistics\\05_phonology_vocal\\waveforms\\la_aqua.wav"
},
{
"text": "water",
"engine": "sapi_wav",
"path": "D:\\training data\\pflt_linguistics\\05_phonology_vocal\\waveforms\\en_water.wav"
},
{
"text": "λ... | latin-safe rev_morph + names/places mine + denser paradigms + ethnonym soft + gapfill + waveforms |
PFLT FSOT — sample densify + benchmark snapshot
Version: 0.2.0 (2026-07-21)
Companion model card: dappalumbo91/pflt-fsot
Code: protofluid-language-translator
Contents
| File | Role |
|---|---|
sample_densify.tsv |
Small form→gloss densify sample for demos |
metrics_snapshot.json |
Public catalog + MT benchmarks (sacreBLEU / chrF) |
DATASET_README.md |
This card |
What this is not
- Not the full multi-GB densify/gold lexicon (local product only)
- Not FLORES scores (Hub parquet still gated)
- Not a claim of commercial DeepL news SOTA
Headline numbers (v0.2.0)
| Track | Metric | Value |
|---|---|---|
| Catalog | langs / form→gloss product | 113 / ~99.99% |
| Chat neural open-set | mean best sacreBLEU (16 langs) | 50.19 |
| Hybrid oracle | mean sacreBLEU | 53.58 |
| WMT14 de→en | opus-mt-de-en sacreBLEU | 33.88 |
| WMT14 de→en | NLLB-600M sacreBLEU | 33.37 |
Full tables and honesty notes: model card + metrics_snapshot.json.
License
Apache-2.0 for packaging. Sample rows may derive from Wiktionary-class data — respect upstream licenses.
- Downloads last month
- 92