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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
edition: string
panel_id: string
index: double
raw_index: double
scores: struct<balanced_skill: double, balanced_raw: double, breadth_skill: double>
child 0, balanced_skill: double
child 1, balanced_raw: double
child 2, breadth_skill: double
areas: list<item: struct<id: string, label: string, raw: double, skill: double, coverage: double, n: int64, (... 32 chars omitted)
child 0, item: struct<id: string, label: string, raw: double, skill: double, coverage: double, n: int64, benchmarks (... 20 chars omitted)
child 0, id: string
child 1, label: string
child 2, raw: double
child 3, skill: double
child 4, coverage: double
child 5, n: int64
child 6, benchmarks: list<item: int64>
child 0, item: int64
benchmarks: struct<1: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_inde (... 5013 chars omitted)
child 0, 1: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool, t (... 24 chars omitted)
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 6, tracks: list<item: null>
child 0, item: null
child 1, 2: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool, t (... 24 chars omitted)
child 0, raw: double
child 1, skill: double
child 2, covera
...
le
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 40, 24: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 41, 26: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 42, 27: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 43, 34: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
coverage: double
note: string
counts: struct<ok: int64>
child 0, ok: int64
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
engine: string
to
{'engine': Value('string'), 'counts': {'ok': Value('int64')}, 'successful_request_latency_ms': {'median': Value('float64'), 'p95': Value('float64'), 'mean': Value('float64')}, 'benchmarks': List({'catalog_id': Value('int64'), 'dataset': Value('string'), 'requests': Value('int64'), 'answered': Value('int64'), 'unsupported': Value('int64'), 'errors': Value('int64'), 'abstained': Value('int64'), 'pending': Value('int64'), 'scored_requests': Value('int64'), 'metric': Value('string'), 'score': Value('float64'), 'reference_same_cases': Value('null'), 'median_ms': Value('float64'), 'detail': {'field_accuracy': Value('float64'), 'case_exact_accuracy': Value('float64'), 'custom_metrics': {'ndcg_at_10': Value('float64'), 'mrr': Value('float64'), 'recall_at_10': Value('float64'), 'candidate_recall': Value('float64'), 'scorable_candidate_recall': Value('float64'), 'candidates_scored': Value('float64'), 'candidates_retrieved': Value('int64'), 'bm25_ndcg_at_10': Value('float64'), 'quality_quality': Value('float64'), 'quality_cost_usd': Value('float64'), 'quality_utility': Value('float64'), 'quality_oracle_optimal': Value('float64'), 'quality_utility_regret': Value('float64'), 'cost_aware_quality': Value('float64'), 'cost_aware_cost_usd': Value('float64'), 'cost_aware_utility': Value('float64'), 'cost_aware_oracle_optimal': Value('float64'), 'cost_aware_utility_regret': Value('float64'), 'value_regret': Value('float64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': V
...
lue('int64'), 'mean': Value('float64')}}, 'cluster_macro_accuracy': Value('float64'), 'positive_f1_by_field': {'sarcastic': Value('float64'), 'sarcasm': Value('float64'), 'irony': Value('float64'), 'satire': Value('float64'), 'understatement': Value('float64'), 'overstatement': Value('float64'), 'rhetorical_question': Value('float64')}, 'category_macro_f1': Value('float64'), 'scored_fields': Value('int64'), 'chance_on_rows': Value('float64')}, 'tracks': {'RouterBench-0shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'RouterBench-5shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-B-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}}}), 'note': Value('string'), 'edition': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
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
edition: string
panel_id: string
index: double
raw_index: double
scores: struct<balanced_skill: double, balanced_raw: double, breadth_skill: double>
child 0, balanced_skill: double
child 1, balanced_raw: double
child 2, breadth_skill: double
areas: list<item: struct<id: string, label: string, raw: double, skill: double, coverage: double, n: int64, (... 32 chars omitted)
child 0, item: struct<id: string, label: string, raw: double, skill: double, coverage: double, n: int64, benchmarks (... 20 chars omitted)
child 0, id: string
child 1, label: string
child 2, raw: double
child 3, skill: double
child 4, coverage: double
child 5, n: int64
child 6, benchmarks: list<item: int64>
child 0, item: int64
benchmarks: struct<1: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_inde (... 5013 chars omitted)
child 0, 1: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool, t (... 24 chars omitted)
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 6, tracks: list<item: null>
child 0, item: null
child 1, 2: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool, t (... 24 chars omitted)
child 0, raw: double
child 1, skill: double
child 2, covera
...
le
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 40, 24: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 41, 26: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 42, 27: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 43, 34: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
coverage: double
note: string
counts: struct<ok: int64>
child 0, ok: int64
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
engine: string
to
{'engine': Value('string'), 'counts': {'ok': Value('int64')}, 'successful_request_latency_ms': {'median': Value('float64'), 'p95': Value('float64'), 'mean': Value('float64')}, 'benchmarks': List({'catalog_id': Value('int64'), 'dataset': Value('string'), 'requests': Value('int64'), 'answered': Value('int64'), 'unsupported': Value('int64'), 'errors': Value('int64'), 'abstained': Value('int64'), 'pending': Value('int64'), 'scored_requests': Value('int64'), 'metric': Value('string'), 'score': Value('float64'), 'reference_same_cases': Value('null'), 'median_ms': Value('float64'), 'detail': {'field_accuracy': Value('float64'), 'case_exact_accuracy': Value('float64'), 'custom_metrics': {'ndcg_at_10': Value('float64'), 'mrr': Value('float64'), 'recall_at_10': Value('float64'), 'candidate_recall': Value('float64'), 'scorable_candidate_recall': Value('float64'), 'candidates_scored': Value('float64'), 'candidates_retrieved': Value('int64'), 'bm25_ndcg_at_10': Value('float64'), 'quality_quality': Value('float64'), 'quality_cost_usd': Value('float64'), 'quality_utility': Value('float64'), 'quality_oracle_optimal': Value('float64'), 'quality_utility_regret': Value('float64'), 'cost_aware_quality': Value('float64'), 'cost_aware_cost_usd': Value('float64'), 'cost_aware_utility': Value('float64'), 'cost_aware_oracle_optimal': Value('float64'), 'cost_aware_utility_regret': Value('float64'), 'value_regret': Value('float64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': V
...
lue('int64'), 'mean': Value('float64')}}, 'cluster_macro_accuracy': Value('float64'), 'positive_f1_by_field': {'sarcastic': Value('float64'), 'sarcasm': Value('float64'), 'irony': Value('float64'), 'satire': Value('float64'), 'understatement': Value('float64'), 'overstatement': Value('float64'), 'rhetorical_question': Value('float64')}, 'category_macro_f1': Value('float64'), 'scored_fields': Value('int64'), 'chance_on_rows': Value('float64')}, 'tracks': {'RouterBench-0shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'RouterBench-5shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-B-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}}}), 'note': Value('string'), 'edition': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Unofficial personal project. EXAONE-4.0-1.2B-JEV is an unofficial fine-tune of EXAONE-4.0-1.2B by an individual; it is not released, endorsed or supported by LG AI Research. All numbers here are self-reported.
Decision Index results: EXAONE-4.0-1.2B-JEV (unofficial)
Runs of Decision Index 0.2.1 for carrtesy/EXAONE-4.0-1.2B-JEV. Rows are in the kit's compact format (no payloads or raw outputs), so no suite content is redistributed.
runs/exaone-jev-1.2b-v0.3
| Model | carrtesy/EXAONE-4.0-1.2B-JEV revision v0.3 |
| Server | github.com/carrtesy/EXAONE-JEV tag v0.3, serve/systemone_server.py (vLLM 0.30.0, bf16) |
| Engine | kit http engine, model=exaone-jev, no extra request fields |
| Inference | as v0.2: two option orders averaged, per-type temperatures, both from the model's jev_config.json |
| Hardware | 1 × NVIDIA A40 (48 GB) |
| Suite | same byte-identical rebuild as v0.1 |
| Result | complete: true, 150,317 / 150,317 ok, index 30.29 (raw 47.94); knowledge 13.1, language 27.3, retrieval 41.0, tools 56.6, arts 13.1 |
Same layout, sharding and declared limits as the v0.1 run described below. Latency (one sequential client, 750-row latency-v1-style sample): median 36.1 ms, p95 277.0 ms.
Note: most of the gain over v0.2 is When2Call MCQ (28.6 → 89.0). v0.3 trains on When2Call's own train split; the training rows share no question or tool definition with the suite rows, but it is in-distribution data.
runs/exaone-jev-1.2b-v0.2
| Model | carrtesy/EXAONE-4.0-1.2B-JEV revision v0.2 |
| Server | github.com/carrtesy/EXAONE-JEV tag v0.2, serve/systemone_server.py (vLLM 0.30.0, bf16) |
| Engine | kit http engine, model=exaone-jev, no extra request fields |
| Inference | settings from the model's jev_config.json: each question read with the options in the given and the reversed order, distributions averaged; per-type temperatures |
| Hardware | 1 × NVIDIA A40 (48 GB) |
| Suite | same byte-identical rebuild as v0.1 |
| Result | complete: true, 150,317 / 150,317 ok, index 27.72 (raw 45.88); knowledge 13.2, language 26.7, retrieval 39.0, tools 45.0, arts 13.7 |
Same layout, sharding and declared limits as the v0.1 run described below. Latency measured separately with one sequential client on a 750-row latency-v1-style sample: median 35.5 ms, p95 274.0 ms.
runs/exaone-jev-1.2b-v0.1
| Model | carrtesy/EXAONE-4.0-1.2B-JEV revision v0.1 |
| Server | github.com/carrtesy/EXAONE-JEV tag v0.1, serve/systemone_server.py (vLLM 0.30.0, bf16) |
| Engine | kit http engine, model=exaone-jev, no extra request fields |
| Hardware | 1 × NVIDIA A40 (48 GB) |
| Suite | rebuilt with suite rebuild; rows byte-identical to the published suite (b2b56d6f…, 7429f3c9…) |
| Result | complete: true, 150,317 / 150,317 ok, index 21.64 (raw 40.39) |
The run was split into 8 row shards (position mod 8) driven by 8 sequential runner processes against one server;
results.jsonl.gz is the unmodified concatenation of the shard results (results.jsonl.sha256 is its uncompressed
hash), and shards/ holds each runner's environment.json and status.json. Because the shards were passed with
--rows, frozen_corpus_sha256 is null in those files. Row timings come from concurrent runners and are not latency
measurements.
Declared limits: 2–255 options per choice, 1–10 score levels, 65,536-token prompts; none was hit. Choices with more than 24 options are read in chunks of at most 24 plus a final read over the chunk leaders; nothing is truncated or dropped.
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