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MatrAIx-Amazon-Review-Personas-10K

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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
created_at: string
dataset: string
rows: int64
unique_users: int64
format: string
data_file: string
compressed_bytes: int64
sha256: string
selection: string
source_snapshot_users: int64
source_snapshot: string
source_bucket_rows: struct<00: int64, 01: int64, 02: int64, 03: int64, 04: int64, 05: int64, 06: int64, 07: int64, 08: i (... 192 chars omitted)
  child 0, 00: int64
  child 1, 01: int64
  child 2, 02: int64
  child 3, 03: int64
  child 4, 04: int64
  child 5, 05: int64
  child 6, 06: int64
  child 7, 07: int64
  child 8, 08: int64
  child 9, 09: int64
  child 10, 0a: int64
  child 11, 0b: int64
  child 12, 0c: int64
  child 13, 0d: int64
  child 14, 0e: int64
  child 15, 0f: int64
  child 16, 10: int64
  child 17, 11: int64
  child 18, 12: int64
  child 19, 13: int64
  child 20, 14: int64
  child 21, 15: int64
  child 22, 16: int64
  child 23, 17: int64
  child 24, 18: int64
  child 25, 19: int64
source_files: list<item: string>
  child 0, item: string
model: string
prompt_variant: string
schema_fields_per_persona: int64
sanitizer: string
selection_sha256: string
schema_file: string
schema_sha256: string
fields: list<item: struct<field_id: string, value: string, confidence: double, evidence: string, description (... 35 chars omitted)
  child 0, item: struct<field_id: string, value: string, confidence: double, evidence: string, description: string, a (... 23 chars omitted)
      child 0, field_id: string
      child 1, value: string
      child 2, confidence: double
      child 3, evidence: string
      child 4, description: string
      child 5, assignment_type: string
user_bucket: string
user_id: string
review_count: int64
to
{'user_id': Value('string'), 'user_bucket': Value('string'), 'review_count': Value('int64'), 'prompt_variant': Value('string'), 'fields': List({'field_id': Value('string'), 'value': Value('string'), 'confidence': Value('float64'), 'evidence': Value('string'), 'description': Value('string'), 'assignment_type': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              created_at: string
              dataset: string
              rows: int64
              unique_users: int64
              format: string
              data_file: string
              compressed_bytes: int64
              sha256: string
              selection: string
              source_snapshot_users: int64
              source_snapshot: string
              source_bucket_rows: struct<00: int64, 01: int64, 02: int64, 03: int64, 04: int64, 05: int64, 06: int64, 07: int64, 08: i (... 192 chars omitted)
                child 0, 00: int64
                child 1, 01: int64
                child 2, 02: int64
                child 3, 03: int64
                child 4, 04: int64
                child 5, 05: int64
                child 6, 06: int64
                child 7, 07: int64
                child 8, 08: int64
                child 9, 09: int64
                child 10, 0a: int64
                child 11, 0b: int64
                child 12, 0c: int64
                child 13, 0d: int64
                child 14, 0e: int64
                child 15, 0f: int64
                child 16, 10: int64
                child 17, 11: int64
                child 18, 12: int64
                child 19, 13: int64
                child 20, 14: int64
                child 21, 15: int64
                child 22, 16: int64
                child 23, 17: int64
                child 24, 18: int64
                child 25, 19: int64
              source_files: list<item: string>
                child 0, item: string
              model: string
              prompt_variant: string
              schema_fields_per_persona: int64
              sanitizer: string
              selection_sha256: string
              schema_file: string
              schema_sha256: string
              fields: list<item: struct<field_id: string, value: string, confidence: double, evidence: string, description (... 35 chars omitted)
                child 0, item: struct<field_id: string, value: string, confidence: double, evidence: string, description: string, a (... 23 chars omitted)
                    child 0, field_id: string
                    child 1, value: string
                    child 2, confidence: double
                    child 3, evidence: string
                    child 4, description: string
                    child 5, assignment_type: string
              user_bucket: string
              user_id: string
              review_count: int64
              to
              {'user_id': Value('string'), 'user_bucket': Value('string'), 'review_count': Value('int64'), 'prompt_variant': Value('string'), 'fields': List({'field_id': Value('string'), 'value': Value('string'), 'confidence': Value('float64'), 'evidence': Value('string'), 'description': Value('string'), 'assignment_type': Value('string')})}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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user_id
string
user_bucket
string
review_count
int64
prompt_variant
string
fields
list
AE2PVGJP2A7PXXOGR6ZOTDSMX4CA
00
71
medium_b
[ { "field_id": "age_bracket", "value": null, "confidence": 0, "evidence": "", "description": "", "assignment_type": "unsupported" }, { "field_id": "region", "value": null, "confidence": 0, "evidence": "", "description": "", "assignment_type": "unsupported" }, {...
AE4MSVM3PMIOAHLDAZQBZEBV42QQ
00
58
medium_b
[{"field_id":"age_bracket","value":null,"confidence":0.0,"evidence":"","description":"","assignment_(...TRUNCATED)
AE4ZJ52HPRP4KZHEP57ZI5TRZZBA
00
33
medium_b
[{"field_id":"age_bracket","value":"45-54","confidence":0.7,"evidence":"I'm a widow who would instea(...TRUNCATED)
AE563AU3SUO2DOP4KMAKUOQWY5NQ
00
89
medium_b
[{"field_id":"age_bracket","value":null,"confidence":0.0,"evidence":"","description":"","assignment_(...TRUNCATED)
AE5C4ZXD7GZYMKDIGCRQA4HAJIWQ
00
37
medium_b
[{"field_id":"age_bracket","value":null,"confidence":0.0,"evidence":"","description":"","assignment_(...TRUNCATED)
AE5MEGX7ZPBQKLDIOBT3OW2SRUNA
00
40
medium_b
[{"field_id":"age_bracket","value":null,"confidence":0.0,"evidence":"","description":"","assignment_(...TRUNCATED)
AE6Y3FIJINDEN6FEJNJRZHXEWN7Q
00
32
medium_b
[{"field_id":"age_bracket","value":null,"confidence":0.0,"evidence":"","description":"","assignment_(...TRUNCATED)
AE75B2NNAC2ESASFLVDWGOSCYYAQ
00
37
medium_b
[{"field_id":"age_bracket","value":null,"confidence":0.0,"evidence":"","description":"","assignment_(...TRUNCATED)
AE76MZDXVM66WO47CZ3TB7BTGJMQ
00
94
medium_b
[{"field_id":"age_bracket","value":null,"confidence":0.0,"evidence":"","description":"","assignment_(...TRUNCATED)
AE7AEWDMNTWCIK6LVYEGOJH55YKA
00
37
medium_b
[{"field_id":"age_bracket","value":null,"confidence":0.0,"evidence":"","description":"","assignment_(...TRUNCATED)
End of preview.

MatrAIx Amazon Review Personas 10K

A deterministic 10,000-user subset of persona attributes extracted from Amazon reviewer histories using Qwen/Qwen3.6-35B-A3B, the medium_b prompt, and schema sanitization.

  • Rows / unique users: 10,000 / 10,000
  • Format: gzip JSONL, one persona per line
  • Schema coverage: 1,290 unique persona fields per row
  • Data: data/amazon_review_personas_10k.jsonl.gz
  • Schema: schema/dimensions.json
  • SHA-256: c6649191622e9dec827fdda8cff4010e994285ec91b7af17d4b4a6fe8f855b34

Each row contains user_id, user_bucket, review_count, prompt_variant, and fields. Each field includes field_id, value, confidence, evidence, description, and assignment_type.

This subset is selected deterministically from complete bucket shards in ascending bucket/file order, preserving source row order. See manifest.json for provenance, source bucket counts, and checksums.

Values and descriptions are model-generated inferences grounded where possible by review evidence. They may contain errors and must not be treated as verified personal facts.

Source extraction: MatrAIx2026/MatrAIx2026, Amazon medium_b Qwen3.6 extraction (HF dataset PR #53 plus H200 continuation).
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