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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:    CastError
Message:      Couldn't cast
qid: string
query: string
gt_answer: string
gt_answer_cleaned: string
prediction: string
prediction_cleaned: string
exact_match: double
soft_matches: struct<1.0: bool, 5.0: bool, 10.0: bool, 20.0: bool, 50.0: bool, 90.0: bool>
  child 0, 1.0: bool
  child 1, 5.0: bool
  child 2, 10.0: bool
  child 3, 20.0: bool
  child 4, 50.0: bool
  child 5, 90.0: bool
reasoning_path: list<item: struct<think: string, prediction: string>>
  child 0, item: struct<think: string, prediction: string>
      child 0, think: string
      child 1, prediction: string
n: int64
soft_exact_match: struct<1.0: double, 5.0: double, 10.0: double, 20.0: double, 50.0: double, 90.0: double>
  child 0, 1.0: double
  child 1, 5.0: double
  child 2, 10.0: double
  child 3, 20.0: double
  child 4, 50.0: double
  child 5, 90.0: double
to
{'n': Value('int64'), 'exact_match': Value('float64'), 'soft_exact_match': {'1.0': Value('float64'), '5.0': Value('float64'), '10.0': Value('float64'), '20.0': Value('float64'), '50.0': Value('float64'), '90.0': Value('float64')}}
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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 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
              qid: string
              query: string
              gt_answer: string
              gt_answer_cleaned: string
              prediction: string
              prediction_cleaned: string
              exact_match: double
              soft_matches: struct<1.0: bool, 5.0: bool, 10.0: bool, 20.0: bool, 50.0: bool, 90.0: bool>
                child 0, 1.0: bool
                child 1, 5.0: bool
                child 2, 10.0: bool
                child 3, 20.0: bool
                child 4, 50.0: bool
                child 5, 90.0: bool
              reasoning_path: list<item: struct<think: string, prediction: string>>
                child 0, item: struct<think: string, prediction: string>
                    child 0, think: string
                    child 1, prediction: string
              n: int64
              soft_exact_match: struct<1.0: double, 5.0: double, 10.0: double, 20.0: double, 50.0: double, 90.0: double>
                child 0, 1.0: double
                child 1, 5.0: double
                child 2, 10.0: double
                child 3, 20.0: double
                child 4, 50.0: double
                child 5, 90.0: double
              to
              {'n': Value('int64'), 'exact_match': Value('float64'), 'soft_exact_match': {'1.0': Value('float64'), '5.0': Value('float64'), '10.0': Value('float64'), '20.0': Value('float64'), '50.0': Value('float64'), '90.0': Value('float64')}}
              because column names don't match

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TRQA Experiments

Prebuilt retrieval indices and experiment results accompanying the TRQA dataset.

This repo holds only artifacts. The queries, qrels, and corpora live in mahtaa/trqa.

Repo structure

Path Contents Retriever type
experiment_results/ experiment_results.zip — retrieval + evaluation outputs —
indices/ecommerce/ Indices over the synthetic e-commerce corpus
  bm25/ Lucene index sparse
  spladepp/ Lucene index (SPLADE++) learned sparse
  bge/ bge_Flat.index dense (FAISS)
  e5/ e5_Flat.index dense (FAISS)
  contriever/ contriever_Flat.index dense (FAISS)
indices/wiki/ Indices over the full English Wikipedia corpus
  bm25/ Lucene index sparse
  spladepp/ Lucene index (SPLADE++) learned sparse
  bge/ bge_Flat.index — 5 parts dense (FAISS)
  e5/ e5_Flat.index — 4 parts dense (FAISS)
  contriever/ contriever_Flat.index — 4 parts dense (FAISS)
indices/wiki_partial/ Indices over the partial Wikipedia corpus
  e5_partial/ e5_Flat.index (exact / flat) dense (FAISS)

Reassembling split indices

The three large Wikipedia dense indices are stored as 45 GB chunks. Concatenate the parts in order before use:

cat bge_Flat.index.part_*        > bge_Flat.index
cat e5_Flat.index.part_*         > e5_Flat.index
cat contriever_Flat.index.part_* > contriever_Flat.index

cat with a shell glob sorts the suffixes (part_aa, part_ab, …) correctly. All other indices are ready to use as-is.

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