Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<corpus_id: struct<occurrences: int64, unique_sha256: int64>, corpus_text: struct<occurrences: int64, unique_sha256: int64>, qrel_relation: struct<occurrences: int64, unique_sha256: int64>, query_id: struct<occurrences: int64, unique_sha256: int64>, query_text: struct<occurrences: int64, unique_sha256: int64>>
to
{'evaluation_text': {'occurrences': Value('int64'), 'unique_sha256': Value('int64')}, 'example_id': {'occurrences': Value('int64'), 'unique_sha256': Value('int64')}, 'example_relation': {'occurrences': Value('int64'), 'unique_sha256': Value('int64')}}
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 478, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<corpus_id: struct<occurrences: int64, unique_sha256: int64>, corpus_text: struct<occurrences: int64, unique_sha256: int64>, qrel_relation: struct<occurrences: int64, unique_sha256: int64>, query_id: struct<occurrences: int64, unique_sha256: int64>, query_text: struct<occurrences: int64, unique_sha256: int64>>
to
{'evaluation_text': {'occurrences': Value('int64'), 'unique_sha256': Value('int64')}, 'example_id': {'occurrences': Value('int64'), 'unique_sha256': Value('int64')}, 'example_relation': {'occurrences': Value('int64'), 'unique_sha256': Value('int64')}}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.
Korean Embedding Benchmark Blocklist v1
Sionic Korean retrieval 9종과 공식 MTEB(kor, v1) 6종의 평가 입력을 학습 데이터에서
제외하기 위한 평가 전용 SHA-256 blocklist다. 원문, 원본 query, qrel label, raw
source ID를 포함하지 않는다. 각 gzip 파일에는 canonicalized evaluation field의
SHA-256 digest만 한 줄에 하나씩 정렬해 저장한다.
범위
- protocol 1:
sionic9-fixed-prompt-v1, 9 tasks - protocol 2:
mteb-korean-v1-mteb-2.18.0, 6 tasks - complete task artifacts: 15/15
- field families: query/document/evaluation text, example/query/corpus IDs, relevance 또는 candidate relation
- dataset revision, split, subset, field별 occurrence/unique count는 task manifest에 고정
MIRACL과 Ko-StrategyQA처럼 두 protocol에서 공유되는 dataset도 protocol별 manifest를 별도로 보존한다. 이 dataset 자체를 학습, synthetic query 생성, hard-negative mining, distillation, checkpoint selection에 사용하면 안 된다.
사용
학습 후보 text를 builder와 같은 canonicalization 규칙으로 normalize한 뒤 SHA-256을
계산하고, 대응하는 *.sha256.gz의 digest set과 exact 비교한다. ID/relation hash는
평가 source lineage 차단과 audit에 사용한다. near-duplicate 검사는 이 release의 exact
hash와 별도로 수행해야 한다.
PYTHONPATH=third_party/mteb .venv-mteb/bin/python \
scripts/build_benchmark_blocklist.py \
--output-dir outputs/decontamination/benchmark_blocklist \
--minhash off
재현 코드와 고정 protocol은 LLM-OS-Models/Embedding에 있다.
제한과 권리
digest는 원문을 대신하는 학습 데이터가 아니며 평가 dataset의 license를 재허가하지 않는다. 각 task manifest에 upstream dataset/license metadata를 기록한다. hash exact match가 없다는 사실은 의미상 오염이나 번역·요약·부분문자열 overlap 부재를 보장하지 않는다.
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