Datasets:

---

Anony84

/

GroundedPersona

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
userId: string
user_msg_count: int64
total_msg_count: int64
active_days: int64
media_count: int64
messages: list<item: struct<id: string, sender: string, timestamp: string, content: string, media: string>>
  child 0, item: struct<id: string, sender: string, timestamp: string, content: string, media: string>
      child 0, id: string
      child 1, sender: string
      child 2, timestamp: string
      child 3, content: string
      child 4, media: string
personality_prose: struct<interests_hobbies_and_domains: string, social_behavior_and_relationship_dynamics: string, hum (... 67 chars omitted)
  child 0, interests_hobbies_and_domains: string
  child 1, social_behavior_and_relationship_dynamics: string
  child 2, humor_and_play_style: string
  child 3, aesthetic_preferences_and_taste: string
skills_and_expertise_list: list<item: null>
  child 0, item: null
career_goals_and_ambitions: null
hobbies_and_interests_list: list<item: string>
  child 0, item: string
city: null
travel_persona: null
uuid: string
country: null
district: null
source_uid: string
_grounding: struct<mode: string, n_claims: int64, coverage: struct<first_day: timestamp[s], last_day: timestamp[ (... 35 chars omitted)
  child 0, mode: string
  child 1, n_claims: int64
  child 2, coverage: struct<first_day: timestamp[s], last_day: timestamp[s], n_days: int64, n_turns: int64>
      child 0, first_day: timestamp[s]
      child 1, last_day: timestamp[s]
      child 2, n_days: int64
      child 3, n_turns: int64
pref
...
ence: int64>
          child 0, dia_ids: list<item: string>
              child 0, item: string
          child 1, confidence: int64
      child 1, anti_stereotypical: struct<dia_ids: list<item: string>, confidence: int64>
          child 0, dia_ids: list<item: string>
              child 0, item: string
          child 1, confidence: int64
      child 2, neutral: struct<dia_ids: list<item: string>, confidence: int64>
          child 0, dia_ids: list<item: string>
              child 0, item: string
          child 1, confidence: int64
  child 8, background_persona: struct<>
  child 9, demographics: struct<>
  child 10, behavior: struct<measured: bool, window: null>
      child 0, measured: bool
      child 1, window: null
  child 11, conversation_analysis: struct<engine: null, coders: null, whow_why: null, whow_how: null, conversation_quadrant: null, self (... 133 chars omitted)
      child 0, engine: null
      child 1, coders: null
      child 2, whow_why: null
      child 3, whow_how: null
      child 4, conversation_quadrant: null
      child 5, self_disclosure_n: null
      child 6, question_asking_n: null
      child 7, phenomena: null
      child 8, harm: struct<verified: null, n_verified: null, n_flags_unioned: null>
          child 0, verified: null
          child 1, n_verified: null
          child 2, n_flags_unioned: null
inferred_traits: list<item: null>
  child 0, item: null
race_ethnicity: null
professional_persona: null
sex: null
communication_persona: string
to
{'uuid': Value('string'), 'source_uid': Value('string'), 'name': Value('string'), 'persona': Value('string'), 'professional_persona': Value('null'), 'sports_persona': Value('null'), 'arts_persona': Value('string'), 'travel_persona': Value('null'), 'culinary_persona': Value('string'), 'family_persona': Value('string'), 'cultural_background': Value('null'), 'skills_and_expertise': Value('null'), 'hobbies_and_interests': Value('string'), 'career_goals_and_ambitions': Value('null'), 'skills_and_expertise_list': List(Value('null')), 'hobbies_and_interests_list': List(Value('string')), 'personality_prose': {'interests_hobbies_and_domains': Value('string'), 'social_behavior_and_relationship_dynamics': Value('string'), 'humor_and_play_style': Value('string'), 'aesthetic_preferences_and_taste': Value('string')}, 'communication_persona': Value('string'), 'preferences_persona': Value('string'), 'background_persona': Value('null'), 'psychotherapy_note': {'presenting_picture': Value('string'), 'themes': List(Value('null')), 'working_hypotheses': List(Value('null')), 'relational_stance': Value('string'), 'to_hold': List(Value('null'))}, 'inferred_traits': List(Value('null')), 'behavior_persona': Value('null'), 'big_five': {'openness': {'level': Value('string'), 'level_agreement': Value('string'), 'confidence': Value('int64'), 'rationale': Value('string'), 'evidence': List(Value('string'))}, 'conscientiousness': {'level': Value('string'), 'level_agreement': Value('string'), 'confidence': Va
...
ynamics': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'humor_and_play_style': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'aesthetic_preferences_and_taste': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}}, 'communication_persona': {'tone': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'formality': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'interaction_pattern': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}}, 'preferences_persona': {'stereotypical': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'anti_stereotypical': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'neutral': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}}, 'background_persona': {}, 'demographics': {}, 'behavior': {'measured': Value('bool'), 'window': Value('null')}, 'conversation_analysis': {'engine': Value('null'), 'coders': Value('null'), 'whow_why': Value('null'), 'whow_how': Value('null'), 'conversation_quadrant': Value('null'), 'self_disclosure_n': Value('null'), 'question_asking_n': Value('null'), 'phenomena': Value('null'), 'harm': {'verified': Value('null'), 'n_verified': Value('null'), 'n_flags_unioned': Value('null')}}}, '_grounding': {'mode': Value('string'), 'n_claims': Value('int64'), 'coverage': {'first_day': Value('timestamp[s]'), 'last_day': Value('timestamp[s]'), 'n_days': Value('int64'), 'n_turns': Value('int64')}}}
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
              userId: string
              user_msg_count: int64
              total_msg_count: int64
              active_days: int64
              media_count: int64
              messages: list<item: struct<id: string, sender: string, timestamp: string, content: string, media: string>>
                child 0, item: struct<id: string, sender: string, timestamp: string, content: string, media: string>
                    child 0, id: string
                    child 1, sender: string
                    child 2, timestamp: string
                    child 3, content: string
                    child 4, media: string
              personality_prose: struct<interests_hobbies_and_domains: string, social_behavior_and_relationship_dynamics: string, hum (... 67 chars omitted)
                child 0, interests_hobbies_and_domains: string
                child 1, social_behavior_and_relationship_dynamics: string
                child 2, humor_and_play_style: string
                child 3, aesthetic_preferences_and_taste: string
              skills_and_expertise_list: list<item: null>
                child 0, item: null
              career_goals_and_ambitions: null
              hobbies_and_interests_list: list<item: string>
                child 0, item: string
              city: null
              travel_persona: null
              uuid: string
              country: null
              district: null
              source_uid: string
              _grounding: struct<mode: string, n_claims: int64, coverage: struct<first_day: timestamp[s], last_day: timestamp[ (... 35 chars omitted)
                child 0, mode: string
                child 1, n_claims: int64
                child 2, coverage: struct<first_day: timestamp[s], last_day: timestamp[s], n_days: int64, n_turns: int64>
                    child 0, first_day: timestamp[s]
                    child 1, last_day: timestamp[s]
                    child 2, n_days: int64
                    child 3, n_turns: int64
              pref
              ...
              ence: int64>
                        child 0, dia_ids: list<item: string>
                            child 0, item: string
                        child 1, confidence: int64
                    child 1, anti_stereotypical: struct<dia_ids: list<item: string>, confidence: int64>
                        child 0, dia_ids: list<item: string>
                            child 0, item: string
                        child 1, confidence: int64
                    child 2, neutral: struct<dia_ids: list<item: string>, confidence: int64>
                        child 0, dia_ids: list<item: string>
                            child 0, item: string
                        child 1, confidence: int64
                child 8, background_persona: struct<>
                child 9, demographics: struct<>
                child 10, behavior: struct<measured: bool, window: null>
                    child 0, measured: bool
                    child 1, window: null
                child 11, conversation_analysis: struct<engine: null, coders: null, whow_why: null, whow_how: null, conversation_quadrant: null, self (... 133 chars omitted)
                    child 0, engine: null
                    child 1, coders: null
                    child 2, whow_why: null
                    child 3, whow_how: null
                    child 4, conversation_quadrant: null
                    child 5, self_disclosure_n: null
                    child 6, question_asking_n: null
                    child 7, phenomena: null
                    child 8, harm: struct<verified: null, n_verified: null, n_flags_unioned: null>
                        child 0, verified: null
                        child 1, n_verified: null
                        child 2, n_flags_unioned: null
              inferred_traits: list<item: null>
                child 0, item: null
              race_ethnicity: null
              professional_persona: null
              sex: null
              communication_persona: string
              to
              {'uuid': Value('string'), 'source_uid': Value('string'), 'name': Value('string'), 'persona': Value('string'), 'professional_persona': Value('null'), 'sports_persona': Value('null'), 'arts_persona': Value('string'), 'travel_persona': Value('null'), 'culinary_persona': Value('string'), 'family_persona': Value('string'), 'cultural_background': Value('null'), 'skills_and_expertise': Value('null'), 'hobbies_and_interests': Value('string'), 'career_goals_and_ambitions': Value('null'), 'skills_and_expertise_list': List(Value('null')), 'hobbies_and_interests_list': List(Value('string')), 'personality_prose': {'interests_hobbies_and_domains': Value('string'), 'social_behavior_and_relationship_dynamics': Value('string'), 'humor_and_play_style': Value('string'), 'aesthetic_preferences_and_taste': Value('string')}, 'communication_persona': Value('string'), 'preferences_persona': Value('string'), 'background_persona': Value('null'), 'psychotherapy_note': {'presenting_picture': Value('string'), 'themes': List(Value('null')), 'working_hypotheses': List(Value('null')), 'relational_stance': Value('string'), 'to_hold': List(Value('null'))}, 'inferred_traits': List(Value('null')), 'behavior_persona': Value('null'), 'big_five': {'openness': {'level': Value('string'), 'level_agreement': Value('string'), 'confidence': Value('int64'), 'rationale': Value('string'), 'evidence': List(Value('string'))}, 'conscientiousness': {'level': Value('string'), 'level_agreement': Value('string'), 'confidence': Va
              ...
              ynamics': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'humor_and_play_style': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'aesthetic_preferences_and_taste': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}}, 'communication_persona': {'tone': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'formality': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'interaction_pattern': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}}, 'preferences_persona': {'stereotypical': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'anti_stereotypical': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}, 'neutral': {'dia_ids': List(Value('string')), 'confidence': Value('int64')}}, 'background_persona': {}, 'demographics': {}, 'behavior': {'measured': Value('bool'), 'window': Value('null')}, 'conversation_analysis': {'engine': Value('null'), 'coders': Value('null'), 'whow_why': Value('null'), 'whow_how': Value('null'), 'conversation_quadrant': Value('null'), 'self_disclosure_n': Value('null'), 'question_asking_n': Value('null'), 'phenomena': Value('null'), 'harm': {'verified': Value('null'), 'n_verified': Value('null'), 'n_flags_unioned': Value('null')}}}, '_grounding': {'mode': Value('string'), 'n_claims': Value('int64'), 'coverage': {'first_day': Value('timestamp[s]'), 'last_day': Value('timestamp[s]'), 'n_days': Value('int64'), 'n_turns': Value('int64')}}}
              because column names don't match