Datasets:

embedding-benchmark
/

ClosedDataset_2

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
RTEB_GermanLegal1_q_0: struct<RTEB_GermanLegal1_d_0: int64>
  child 0, RTEB_GermanLegal1_d_0: int64
RTEB_GermanLegal1_q_1: struct<RTEB_GermanLegal1_d_1: int64>
  child 0, RTEB_GermanLegal1_d_1: int64
RTEB_GermanLegal1_q_2: struct<RTEB_GermanLegal1_d_2: int64>
  child 0, RTEB_GermanLegal1_d_2: int64
RTEB_GermanLegal1_q_3: struct<RTEB_GermanLegal1_d_3: int64>
  child 0, RTEB_GermanLegal1_d_3: int64
RTEB_GermanLegal1_q_4: struct<RTEB_GermanLegal1_d_4: int64>
  child 0, RTEB_GermanLegal1_d_4: int64
query-id: null
corpus-id: null
score: null
to
{'query-id': Value('string'), 'corpus-id': Value('string'), 'score': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2361, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1914, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              RTEB_GermanLegal1_q_0: struct<RTEB_GermanLegal1_d_0: int64>
                child 0, RTEB_GermanLegal1_d_0: int64
              RTEB_GermanLegal1_q_1: struct<RTEB_GermanLegal1_d_1: int64>
                child 0, RTEB_GermanLegal1_d_1: int64
              RTEB_GermanLegal1_q_2: struct<RTEB_GermanLegal1_d_2: int64>
                child 0, RTEB_GermanLegal1_d_2: int64
              RTEB_GermanLegal1_q_3: struct<RTEB_GermanLegal1_d_3: int64>
                child 0, RTEB_GermanLegal1_d_3: int64
              RTEB_GermanLegal1_q_4: struct<RTEB_GermanLegal1_d_4: int64>
                child 0, RTEB_GermanLegal1_d_4: int64
              query-id: null
              corpus-id: null
              score: null
              to
              {'query-id': Value('string'), 'corpus-id': Value('string'), 'score': Value('float64')}
              because column names don't match

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This dataset is derived from real-world judicial decisions and employs a combination of legal citation matching and BM25 similarity. The BM25 baseline poses a slight risk as it biases the data outside of citation matching. A subset of the dataset was manually verified to ensure correctness and quality.

Usage

import datasets

# Download the dataset
queries = datasets.load_dataset("embedding-benchmark/DS1000", "queries")
documents = datasets.load_dataset("embedding-benchmark/DS1000", "corpus")
pair_labels = datasets.load_dataset("embedding-benchmark/DS1000", "default")

Downloads last month: 11

Size of downloaded dataset files:

3.6 kB

Size of the auto-converted Parquet files:

8.89 kB

Number of rows:

Error code: StreamingRowsError Exception: CastError Message: Couldn't cast RTEB_GermanLegal1_q_0: struct<RTEB_GermanLegal1_d_0: int64> child 0, RTEB_GermanLegal1_d_0: int64 RTEB_GermanLegal1_q_1: struct<RTEB_GermanLegal1_d_1: int64> child 0, RTEB_GermanLegal1_d_1: int64 RTEB_GermanLegal1_q_2: struct<RTEB_GermanLegal1_d_2: int64> child 0, RTEB_GermanLegal1_d_2: int64 RTEB_GermanLegal1_q_3: struct<RTEB_GermanLegal1_d_3: int64> child 0, RTEB_GermanLegal1_d_3: int64 RTEB_GermanLegal1_q_4: struct<RTEB_GermanLegal1_d_4: int64> child 0, RTEB_GermanLegal1_d_4: int64 query-id: null corpus-id: null score: null to {'query-id': Value('string'), 'corpus-id': Value('string'), 'score': Value('float64')} because column names don't match Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise return get_rows( 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 77, in get_rows rows_plus_one = list(itertools.islice(ds, rows_max_number + 1)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2361, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__ for key, pa_table in self._iter_arrow(): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1914, in _iter_arrow pa_table = cast_table_to_features(pa_table, self.features) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2192, in cast_table_to_features raise CastError( datasets.table.CastError: Couldn't cast RTEB_GermanLegal1_q_0: struct<RTEB_GermanLegal1_d_0: int64> child 0, RTEB_GermanLegal1_d_0: int64 RTEB_GermanLegal1_q_1: struct<RTEB_GermanLegal1_d_1: int64> child 0, RTEB_GermanLegal1_d_1: int64 RTEB_GermanLegal1_q_2: struct<RTEB_GermanLegal1_d_2: int64> child 0, RTEB_GermanLegal1_d_2: int64 RTEB_GermanLegal1_q_3: struct<RTEB_GermanLegal1_d_3: int64> child 0, RTEB_GermanLegal1_d_3: int64 RTEB_GermanLegal1_q_4: struct<RTEB_GermanLegal1_d_4: int64> child 0, RTEB_GermanLegal1_d_4: int64 query-id: null corpus-id: null score: null to {'query-id': Value('string'), 'corpus-id': Value('string'), 'score': Value('float64')} because column names don't match

import datasets # Download the dataset queries = datasets.load_dataset("embedding-benchmark/DS1000", "queries") documents = datasets.load_dataset("embedding-benchmark/DS1000", "corpus") pair_labels = datasets.load_dataset("embedding-benchmark/DS1000", "default")