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renikud
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MILIM-Bench

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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'phonikud', 'renikud'}) and 3 missing columns ({'Label', 'Category', 'Text'}).

This happened while the csv dataset builder was generating data using

hf://datasets/renikud/MILIM-Bench/data/predictions.tsv (at revision 842d8113d4d31124d7e80fc798d27992208fea93), [/tmp/hf-datasets-cache/medium/datasets/76992296123232-config-parquet-and-info-renikud-MILIM-Bench-7db89a65/hub/datasets--renikud--MILIM-Bench/snapshots/842d8113d4d31124d7e80fc798d27992208fea93/data/gold.tsv (origin=hf://datasets/renikud/MILIM-Bench@842d8113d4d31124d7e80fc798d27992208fea93/data/gold.tsv), /tmp/hf-datasets-cache/medium/datasets/76992296123232-config-parquet-and-info-renikud-MILIM-Bench-7db89a65/hub/datasets--renikud--MILIM-Bench/snapshots/842d8113d4d31124d7e80fc798d27992208fea93/data/predictions.tsv (origin=hf://datasets/renikud/MILIM-Bench@842d8113d4d31124d7e80fc798d27992208fea93/data/predictions.tsv)]

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              renikud: string
              phonikud: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 492
              to
              {'Category': Value('string'), 'Text': Value('string'), 'Label': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'phonikud', 'renikud'}) and 3 missing columns ({'Label', 'Category', 'Text'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/renikud/MILIM-Bench/data/predictions.tsv (at revision 842d8113d4d31124d7e80fc798d27992208fea93), [/tmp/hf-datasets-cache/medium/datasets/76992296123232-config-parquet-and-info-renikud-MILIM-Bench-7db89a65/hub/datasets--renikud--MILIM-Bench/snapshots/842d8113d4d31124d7e80fc798d27992208fea93/data/gold.tsv (origin=hf://datasets/renikud/MILIM-Bench@842d8113d4d31124d7e80fc798d27992208fea93/data/gold.tsv), /tmp/hf-datasets-cache/medium/datasets/76992296123232-config-parquet-and-info-renikud-MILIM-Bench-7db89a65/hub/datasets--renikud--MILIM-Bench/snapshots/842d8113d4d31124d7e80fc798d27992208fea93/data/predictions.tsv (origin=hf://datasets/renikud/MILIM-Bench@842d8113d4d31124d7e80fc798d27992208fea93/data/predictions.tsv)]
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Category
string
Text
string
Label
string
gender
ืชื’ื™ื“ื™, ื”ื›ืœ ื‘ืกื“ืจ ืื™ืชืš?
3=ส”itหˆaฯ‡
gender
ืื ื™ ื ื•ืชืŸ ืœืš ืืช ื”ืกืคืจ, ืชืงืจื ืื•ืชื•.
2=leฯ‡หˆa
gender
ืื ื™ ื ื•ืชืŸ ืœืš ืืช ื”ืกืคืจ, ืชืงืจืื™ ืื•ืชื•.
2=lหˆaฯ‡
gender
ื”ื‘ื™ืช ืฉืœืš ืžืื•ื“ ื™ืคื”, ืืชื” ืงื ื™ืช ืื•ืชื• ืžื–ืžืŸ?
1=สƒelฯ‡หˆa
gender
ืจืื™ืชื™ ืื•ืชืš ื”ื•ืœืš ื‘ืจื—ื•ื‘.
1=ส”otฯ‡หˆa
gender
ืฉืœื—ืชื™ ืืœื™ืš ืžื›ืชื‘, ืืช ืงื™ื‘ืœืช ืื•ืชื•?
1=ส”elหˆajiฯ‡ 4=kibหˆalt
gender
ืชื•ื“ื” ืœืš, ืืชื” ืขื•ื–ืจ ืœื™ ืžืื•ื“.
1=leฯ‡หˆa
gender
ืื ื™ ืžื—ื›ื” ืœืš, ืชื–ื“ืจื–ื™.
2=lหˆaฯ‡
gender
ื”ืžืคืชื— ื ืฉืืจ ืืฆืœืš, ืืชื” ื‘ื˜ื•ื—?
2=ส”etsleฯ‡หˆa
gender
ื”ืžืคืชื— ื ืฉืืจ ืืฆืœืš, ืืช ื‘ื˜ื•ื—ื”?
2=ส”etslหˆeฯ‡
gender
ืื ื™ ื—ื•ื–ืจ ืืœื™ืš, ื—ื›ื” ืœื™ ื“ืงื”.
2=ส”elหˆeฯ‡a
gender
ืžื” ืฉืžืขืชื™ ืžืžืš? ืืชื” ืขื•ื–ื‘?
2=mimฯ‡หˆa
gender
ืžื” ืฉืžืขืชื™ ืžืžืš? ืืช ืขื•ื–ื‘ืช?
2=mimหˆeฯ‡
gender
ื”ื›ื™ืกื ื”ื–ื” ื‘ืฉื‘ื™ืœืš, ืฉื‘ ื‘ื‘ืงืฉื”.
2=biสƒvilฯ‡หˆa
gender
ืžื™ ืขื•ืžื“ ืžื•ืœืš? ืืชื” ืžื›ื™ืจ ืื•ืชื•?
2=mulฯ‡หˆa
gender
ืžื™ ืขื•ืžื“ืช ืžื•ืœืš? ืืช ืžื›ื™ืจื” ืื•ืชื”?
2=mulหˆeฯ‡
gender
ื”ื›ืœ ืงืจื” ื‘ื’ืœืœืš, ืืชื” ืชืžื™ื“ ืžืื—ืจ.
2=biษกlalฯ‡หˆa
gender
ื”ื›ืœ ืงืจื” ื‘ื’ืœืœืš, ืืช ืชืžื™ื“ ืžืื—ืจืช.
2=biษกlalหˆeฯ‡
gender
ืื ื™ ืœื ื”ื•ืœืš ื‘ืœืขื“ื™ืš, ืืชื” ื‘ื ืื™ืชื™?
3=bilส”adหˆeฯ‡a
gender
ืื ื™ ืœื ื”ื•ืœื›ืช ื‘ืœืขื“ื™ืš, ืืช ื‘ืื” ืื™ืชื™?
3=bilส”adหˆajiฯ‡
gender
ืื ื™ ืจืฅ ืื—ืจื™ืš, ื—ื›ื” ืจื’ืข!
2=ส”aฯ‡aสหˆeฯ‡a
gender
ืื ื™ ืจืฆื” ืื—ืจื™ืš, ื—ื›ื™ ืจื’ืข!
2=ส”aฯ‡aสหˆajiฯ‡
gender
ืื ื™ ืจื•ืฆื” ืœื”ื™ื•ืช ืื™ืชืš, ืืชื” ื—ื‘ืจ ื˜ื•ื‘.
3=ส”itฯ‡หˆa
gender
ืื ื™ ืจื•ืฆื” ืœื”ื™ื•ืช ืื™ืชืš, ืืช ื—ื‘ืจื” ื˜ื•ื‘ื”.
3=ส”itหˆaฯ‡
gender
ืื™ืš ื”ืžืจื’ืฉ ืืฆืœืš? ืืชื” ื ืจืื” ืžืจื•ืฆื”.
2=ส”etsleฯ‡หˆa
gender
ืื ื™ ืกื•ืžืš ืขืœื™ืš, ืืชื” ืชืฆืœื™ื—.
2=ส”alหˆeฯ‡a
gender
ืื ื™ ืกื•ืžื›ืช ืขืœื™ืš, ืืช ืชืฆืœื™ื—ื™.
2=ส”alหˆajiฯ‡
gender
ื™ืฉ ืœื™ ืืžื•ืŸ ื‘ืš, ืืชื” ืชื•ืชื—.
3=beฯ‡หˆa
gender
ื™ืฉ ืœื™ ืืžื•ืŸ ื‘ืš, ืืช ืชื•ืชื—ื™ืช.
3=bหˆaฯ‡
gender
ื›ืœ ื”ืขืชื™ื“ ืœืคื ื™ืš, ืืชื” ืฆืขื™ืจ.
2=lefanหˆeฯ‡a
gender
ื›ืœ ื”ืขืชื™ื“ ืœืคื ื™ืš, ืืช ืฆืขื™ืจื”.
2=lefanหˆajiฯ‡
gender
ืชืฉืžื•ืจ ืขืœ ืขืฆืžืš, ืืชื” ื—ืฉื•ื‘.
2=ส”atsmeฯ‡หˆa
gender
ืชืฉืžืจื™ ืขืœ ืขืฆืžืš, ืืช ื—ืฉื•ื‘ื”.
2=ส”atsmหˆeฯ‡
gender
ื”ืฉืžื™ื™ื ืคืชื•ื—ื™ื ืžืขืœื™ืš, ืืชื” ื—ื•ืคืฉื™.
2=meส”alหˆeฯ‡a
gender
ื”ืฉืžื™ื™ื ืคืชื•ื—ื™ื ืžืขืœื™ืš, ืืช ื—ื•ืคืฉื™ื”.
2=meส”alหˆajiฯ‡
gender
ื”ืื“ืžื” ืจื•ืขื“ืช ืžืชื—ืชื™ืš, ืืชื” ืžืจื’ื™ืฉ?
2=mitaฯ‡tหˆeฯ‡a
gender
ื”ืื“ืžื” ืจื•ืขื“ืช ืžืชื—ืชื™ืš, ืืช ืžืจื’ื™ืฉื”?
2=mitaฯ‡tหˆajiฯ‡
gender
ื›ื•ืœื ื™ื•ื“ืขื™ื ืžื” ืฉืžืš, ืืชื” ืžืคื•ืจืกื.
3=สƒimฯ‡หˆa
gender
ื›ื•ืœื ื™ื•ื“ืขื™ื ืžื” ืฉืžืš, ืืช ืžืคื•ืจืกืžืช.
3=สƒmหˆeฯ‡
gender
ืื ื™ ืฉื•ืžืข ืืช ืงื•ืœืš, ืืชื” ืงืจื•ื‘.
3=kolฯ‡หˆa
gender
ืื ื™ ืฉื•ืžืข ืืช ืงื•ืœืš, ืืช ืงืจื•ื‘ื”.
3=kolหˆeฯ‡
gender
ืื ื™ ืชืžื™ื“ ืคื” ืœื™ื“ืš, ืืชื” ื‘ื˜ื•ื—.
3=lejadeฯ‡หˆa
gender
ืื ื™ ืชืžื™ื“ ืคื” ืœื™ื“ืš, ืืช ื‘ื˜ื•ื—ื”.
3=lejadหˆeฯ‡
gender
ืืฃ ืื—ื“ ืœื ื ื’ื“ืš, ืืชื” ืžื’ื–ื™ื.
3=neษกdeฯ‡หˆa
gender
ืืฃ ืื—ืช ืœื ื ื’ื“ืš, ืืช ืžื’ื–ื™ืžื”.
3=neษกdหˆeฯ‡
gender
ื”ืงื”ืœ ื›ื•ืœื• ื›ืืŸ ื‘ืขื“ืš, ืืชื” ืžื ืฆื—.
3=baส”adฯ‡หˆa
gender
ื”ืงื”ืœ ื›ื•ืœื• ื›ืืŸ ื‘ืขื“ืš, ืืช ืžื ืฆื—ืช.
3=baส”adหˆeฯ‡
gender
ื™ืฉ ื”ืจื‘ื” ืจืขืฉ ืกื‘ื™ื‘ืš, ืืชื” ืฉื•ืžืข?
3=svivฯ‡หˆa
gender
ื™ืฉ ื”ืจื‘ื” ืจืขืฉ ืกื‘ื™ื‘ืš, ืืช ืฉื•ืžืขืช?
3=svivหˆeฯ‡
gender
ืขืฉื™ืชื™ ืืช ื–ื” ืœืžืขื ืš, ืืชื” ืžืขืจื™ืš?
3=lemaส”anฯ‡หˆa
gender
ืขืฉื™ืชื™ ืืช ื–ื” ืœืžืขื ืš, ืืช ืžืขืจื™ื›ื”?
3=lemaส”anหˆeฯ‡
gender
ื‘ื•ื ื ืจืื” ืืช ืขื™ื ื™ืš, ืืชื” ืขื™ื™ืฃ?
3=ส”ejnหˆeฯ‡a
gender
ื‘ื•ืื™ ื ืจืื” ืืช ืขื™ื ื™ืš, ืืช ืขื™ื™ืคื”?
3=ส”ejnหˆajiฯ‡
gender
ืื ื™ ืื•ื—ื– ื‘ืฉืชื™ ื™ื“ื™ืš, ืืชื” ื™ืฆื™ื‘.
3=jadหˆeฯ‡a
gender
ืื ื™ ืื•ื—ื– ื‘ืฉืชื™ ื™ื“ื™ืš, ืืช ื™ืฆื™ื‘ื”.
3=jadหˆajiฯ‡
gender
ืชืกืชื›ืœ ืœืืŸ ื”ื•ืœื›ื•ืช ืจื’ืœื™ืš, ืืชื” ืžืžื”ืจ.
3=สaษกlหˆeฯ‡a
gender
ืชืกืชื›ืœื™ ืœืืŸ ื”ื•ืœื›ื•ืช ืจื’ืœื™ืš, ืืช ืžืžื”ืจืช.
3=สaษกlหˆajiฯ‡
gender
ืจื•ืื™ื ืืช ื”ืคื—ื“ ืขืœ ืคื ื™ืš, ืืชื” ื—ื™ื•ื•ืจ.
4=panหˆeฯ‡a
gender
ืจื•ืื™ื ืืช ื”ืคื—ื“ ืขืœ ืคื ื™ืš, ืืช ื—ื™ื•ื•ืจืช.
4=panหˆajiฯ‡
gender
ืื™ื–ื” ื™ื•ืคื™ ื™ืฉ ื‘ืฉืขืจืš, ืืชื” ืžืกืชืคืจ?
3=beseส”aสฯ‡หˆa
gender
ืื™ื–ื” ื™ื•ืคื™ ื™ืฉ ื‘ืฉืขืจืš, ืืช ืžืกืชืคืจืช?
3=beseส”aสหˆeฯ‡
gender
ืžื™ ืžื˜ืคืœ ื›ืขืช ื‘ื›ืœื‘ืš, ืืชื” ื‘ืขื‘ื•ื“ื”?
3=kalbeฯ‡หˆa
gender
ืžื™ ืžื˜ืคืœืช ื›ืขืช ื‘ื›ืœื‘ืš, ืืช ื‘ืขื‘ื•ื“ื”?
3=kalbหˆeฯ‡
gender
ื”ืื›ืœืชื™ ื”ื™ื•ื ืืช ื—ืชื•ืœืš, ืืชื” ืจื’ื•ืข?
3=ฯ‡atulฯ‡หˆa
gender
ื”ืื›ืœืชื™ ื”ื™ื•ื ืืช ื—ืชื•ืœืš, ืืช ืจื’ื•ืขื”?
3=ฯ‡atulหˆeฯ‡
gender
ืฆื‘ืขื• ืœืš ืืช ื‘ื™ืชืš, ืืชื” ืžืจื•ืฆื”?
3=bejtฯ‡หˆa
gender
ืฆื‘ืขื• ืœืš ืืช ื‘ื™ืชืš, ืืช ืžืจื•ืฆื”?
3=bejtหˆeฯ‡
gender
ืกื™ื“ืจืชื™ ืœืš ืืช ื—ื“ืจืš, ืืชื” ื‘ื?
3=ฯ‡adสeฯ‡หˆa
gender
ืกื™ื“ืจืชื™ ืœืš ืืช ื—ื“ืจืš, ืืช ื‘ืื”?
3=ฯ‡adสหˆeฯ‡
gender
ืงืจืืชื™ ื‘ืฉืงื™ืงื” ืืช ืกืคืจืš, ืืชื” ืกื•ืคืจ.
3=sifสeฯ‡หˆa
gender
ืงืจืืชื™ ื‘ืฉืงื™ืงื” ืืช ืกืคืจืš, ืืช ืกื•ืคืจืช.
3=sifสหˆeฯ‡
gender
ืืชื” ืฆืจื™ืš ืœืฉืžื•ืจ ืขืœ ื›ืกืคืš, ืืชื” ืคื–ืจืŸ.
4=kaspeฯ‡หˆa
gender
ืืช ืฆืจื™ื›ื” ืœืฉืžื•ืจ ืขืœ ื›ืกืคืš, ืืช ืคื–ืจื ื™ืช.
4=kaspหˆeฯ‡
gender
ืื ื™ ืœื ืืงื— ืžื–ืžื ืš, ืืชื” ื•ื“ืื™ ืขืžื•ืก.
3=mizmanฯ‡หˆa
gender
ืื ื™ ืœื ืืงื— ืžื–ืžื ืš, ืืช ื•ื“ืื™ ืขืžื•ืกื”.
3=mizmanหˆeฯ‡
gender
ืื™ืคื” ื—ื ื™ืช ืืช ืจื›ื‘ืš, ืืชื” ื–ื•ื›ืจ?
3=สiฯ‡veฯ‡หˆa
gender
ืื™ืคื” ื—ื ื™ืช ืืช ืจื›ื‘ืš, ืืช ื–ื•ื›ืจืช?
3=สiฯ‡vหˆeฯ‡
gender
ืฉื›ื—ืช ืคื” ืืช ืชื™ืงืš, ืืชื” ืฆืจื™ืš?
3=tikฯ‡หˆa
gender
ืฉื›ื—ืช ืคื” ืืช ืชื™ืงืš, ืืช ืฆืจื™ื›ื”?
3=tikหˆeฯ‡
gender
ื™ืฉ ื›ืชื ืงื˜ืŸ ืขืœ ื‘ื’ื“ืš, ืืชื” ืจื•ืื”?
4=biษกdeฯ‡หˆa
gender
ื™ืฉ ื›ืชื ืงื˜ืŸ ืขืœ ื‘ื’ื“ืš, ืืช ืจื•ืื”?
4=biษกdหˆeฯ‡
gender
ื”ืื ืืชื” ืื•ื”ื‘ ืืช ืืจืฆืš, ืืชื” ื ืฉืืจ?
4=ส”aสtseฯ‡หˆa
gender
ื”ืื ืืช ืื•ื”ื‘ืช ืืช ืืจืฆืš, ืืช ื ืฉืืจืช?
4=ส”aสtsหˆeฯ‡
gender
ืžืชื™ ืชืขื–ื•ื‘ ืืช ืขื™ืจืš, ืืชื” ืขื•ื‘ืจ?
3=ส”iสฯ‡หˆa
gender
ืžืชื™ ืชืขื–ื‘ื™ ืืช ืขื™ืจืš, ืืช ืขื•ื‘ืจืช?
3=ส”iสหˆeฯ‡
gender
ื”ืงืฉื‘ืชื™ ื”ื™ื˜ื‘ ืœื›ืœ ื“ื‘ืจื™ืš, ืืชื” ืฆื•ื“ืง.
3=dvaสหˆeฯ‡a
gender
ื”ืงืฉื‘ืชื™ ื”ื™ื˜ื‘ ืœื›ืœ ื“ื‘ืจื™ืš, ืืช ืฆื•ื“ืงืช.
3=dvaสหˆajiฯ‡
gender
ืื ื™ ืžืขืจื™ืš ืืช ื›ืœ ืžืขืฉื™ืš, ืืชื” ืฆื“ื™ืง.
4=maส”asหˆeฯ‡a
gender
ืื ื™ ืžืขืจื™ื›ื” ืืช ื›ืœ ืžืขืฉื™ืš, ืืช ืฆื“ื™ืงื”.
4=maส”asหˆajiฯ‡
gender
ื”ืื ื”ื–ืžื ืช ืืช ื›ืœ ื—ื‘ืจื™ืš, ืืชื” ื—ื•ื’ื’?
4=ฯ‡aveสหˆeฯ‡a
gender
ื”ืื ื”ื–ืžื ืช ืืช ื›ืœ ื—ื‘ืจื™ืš, ืืช ื—ื•ื’ื’ืช?
4=ฯ‡aveสหˆajiฯ‡
gender
ืžื™ ืื•ืกืฃ ืืช ื™ืœื“ื™ืš, ืืชื” ืคื ื•ื™?
3=jaladหˆeฯ‡a
gender
ืžื™ ืื•ืกืคืช ืืช ื™ืœื“ื™ืš, ืืช ืคื ื•ื™ื”?
3=jaladหˆajiฯ‡
gender
ื”ืื ื“ื™ื‘ืจืช ื”ื™ื•ื ืขื ื”ื•ืจื™ืš, ืืชื” ืžืชื’ืขื’ืข?
4=hoสหˆeฯ‡a
gender
ื”ืื ื“ื™ื‘ืจืช ื”ื™ื•ื ืขื ื”ื•ืจื™ืš, ืืช ืžืชื’ืขื’ืขืช?
4=hoสหˆajiฯ‡
gender
ืจืื™ืชื™ ื‘ืžืงืจื” ืืช ืื—ื™ืš, ืืชื” ื“ื•ืžื”.
3=ส”aฯ‡หˆiฯ‡a
gender
ืจืื™ืชื™ ื‘ืžืงืจื” ืืช ืื—ื™ืš, ืืช ื“ื•ืžื”.
3=ส”aฯ‡หˆiฯ‡
gender
ืคื’ืฉืชื™ ืืชืžื•ืœ ืืช ืื—ื™ื•ืชื™ืš, ืืชื” ืฉืžื—?
3=ส”aฯ‡jotหˆeฯ‡a
gender
ืคื’ืฉืชื™ ืืชืžื•ืœ ืืช ืื—ื™ื•ืชื™ืš, ืืช ืฉืžื—ื”?
3=ส”aฯ‡jotหˆajiฯ‡
gender
ืื ื™ ืžื—ืคืฉ ืืช ืืฉืชืš, ืืชื” ืฉื?
3=ส”iสƒteฯ‡หˆa
End of preview.

MILIM-Bench

MILIM-Bench is a small Hebrew benchmark for evaluating grapheme-to-phoneme (G2P) systems. It contains Hebrew text examples grouped by linguistic category, with target pronunciations for the words that should be checked.

The benchmark is distributed as MILIM-Bench-v5.tsv and is hosted on Hugging Face at renikud/MILIM-Bench.

Format

Each row contains:

  • Category: the phenomenon being tested.
  • Text: the Hebrew sentence or phrase.
  • Label: expected pronunciation targets, written as token_index=IPA. Token indexes start from 0, and a row may contain one or more indexed words.

Example label:

2=leฯ‡หˆa

This means token 2 in the text should be pronounced leฯ‡หˆa.

Categories

The current file has 1,653 examples across 12 categories:

  • acronyms
  • colloquial
  • foreign
  • gender
  • homographs
  • ilspeech-v2-test
  • minimal stress pairs
  • names
  • penultimate stress
  • rare phonemes
  • slang
  • Regular Homographed

Intended Use

Use this dataset to compare Hebrew G2P outputs against category-specific expected pronunciations, especially for ambiguity, stress placement, names, loanwords, slang, acronyms, and gendered forms.
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