The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
Metadata_Identifiers: struct<transaction_id: string, timestamp_utc: string, complexity_level: string>
child 0, transaction_id: string
child 1, timestamp_utc: string
child 2, complexity_level: string
Security_Auth: struct<device_ip: string, jwt_validation: string, kyc_aml_status: string>
child 0, device_ip: string
child 1, jwt_validation: string
child 2, kyc_aml_status: string
Transaction_Intent: struct<origin_entity: string, destination_entity: string, base_amount: double, currency: string, sou (... 354 chars omitted)
child 0, origin_entity: string
child 1, destination_entity: string
child 2, base_amount: double
child 3, currency: string
child 4, source_account_id: string
child 5, destination_account_id: string
child 6, amount: int64
child 7, payment_type: string
child 8, memo: string
child 9, source_account: string
child 10, destination_account: string
child 11, payment_rail: string
child 12, requested_amount: int64
child 13, requested_currency: string
child 14, settlement_type: string
child 15, execution_priority: string
child 16, debtor_iban: string
child 17, creditor_iban: string
child 18, transfer_type: string
Ledger_State_Pre_Execution: struct<sender_account_balance: double, database_lock_applied: bool>
child 0, sender_account_balance: double
child 1, database_lock_applied: bool
Agentic_Reasoning_Trace: list<item: string>
child 0, item: string
Network_API_Logs: struct<primary_gateway_used: string, http_status_code:
...
error_encountered: (... 7 chars omitted)
child 0, primary_gateway_used: string
child 1, http_status_code: int64
child 2, latency_ms: int64
child 3, error_encountered: string
Financial_Resolution: struct<gross_amount: double, taxes_applied: struct<type: string, amount: double>, routing_fees: doub (... 492 chars omitted)
child 0, gross_amount: double
child 1, taxes_applied: struct<type: string, amount: double>
child 0, type: string
child 1, amount: double
child 2, routing_fees: double
child 3, net_convertible: double
child 4, fx_rate_applied: double
child 5, final_disbursement: double
child 6, status: string
child 7, clearing_reference: string
child 8, settlement_timestamp: string
child 9, fee_assessed: int64
child 10, applied_fee: double
child 11, settlement_timestamp_utc: string
child 12, execution_status: string
child 13, settlement_fee: double
child 14, settled_at: string
child 15, applied_fx_rate: double
child 16, debited_amount_usd: int64
child 17, credited_amount_eur: double
child 18, transaction_fee_usd: int64
child 19, settlement_reference: string
child 20, settlement_status: string
child 21, interchange_fee: double
Ledger_State_Post_Execution: struct<sender_account_balance_updated: double, settlement_status: string>
child 0, sender_account_balance_updated: double
child 1, settlement_status: string
Metadata: null
Final Output: string
Instruction: string
Context: string
Reasoned Action Summary: string
to
{'Instruction': Value('string'), 'Context': Value('string'), 'Reasoned Action Summary': Value('string'), 'Final Output': Value('string'), 'Metadata': Json(decode=True)}
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
Metadata_Identifiers: struct<transaction_id: string, timestamp_utc: string, complexity_level: string>
child 0, transaction_id: string
child 1, timestamp_utc: string
child 2, complexity_level: string
Security_Auth: struct<device_ip: string, jwt_validation: string, kyc_aml_status: string>
child 0, device_ip: string
child 1, jwt_validation: string
child 2, kyc_aml_status: string
Transaction_Intent: struct<origin_entity: string, destination_entity: string, base_amount: double, currency: string, sou (... 354 chars omitted)
child 0, origin_entity: string
child 1, destination_entity: string
child 2, base_amount: double
child 3, currency: string
child 4, source_account_id: string
child 5, destination_account_id: string
child 6, amount: int64
child 7, payment_type: string
child 8, memo: string
child 9, source_account: string
child 10, destination_account: string
child 11, payment_rail: string
child 12, requested_amount: int64
child 13, requested_currency: string
child 14, settlement_type: string
child 15, execution_priority: string
child 16, debtor_iban: string
child 17, creditor_iban: string
child 18, transfer_type: string
Ledger_State_Pre_Execution: struct<sender_account_balance: double, database_lock_applied: bool>
child 0, sender_account_balance: double
child 1, database_lock_applied: bool
Agentic_Reasoning_Trace: list<item: string>
child 0, item: string
Network_API_Logs: struct<primary_gateway_used: string, http_status_code:
...
error_encountered: (... 7 chars omitted)
child 0, primary_gateway_used: string
child 1, http_status_code: int64
child 2, latency_ms: int64
child 3, error_encountered: string
Financial_Resolution: struct<gross_amount: double, taxes_applied: struct<type: string, amount: double>, routing_fees: doub (... 492 chars omitted)
child 0, gross_amount: double
child 1, taxes_applied: struct<type: string, amount: double>
child 0, type: string
child 1, amount: double
child 2, routing_fees: double
child 3, net_convertible: double
child 4, fx_rate_applied: double
child 5, final_disbursement: double
child 6, status: string
child 7, clearing_reference: string
child 8, settlement_timestamp: string
child 9, fee_assessed: int64
child 10, applied_fee: double
child 11, settlement_timestamp_utc: string
child 12, execution_status: string
child 13, settlement_fee: double
child 14, settled_at: string
child 15, applied_fx_rate: double
child 16, debited_amount_usd: int64
child 17, credited_amount_eur: double
child 18, transaction_fee_usd: int64
child 19, settlement_reference: string
child 20, settlement_status: string
child 21, interchange_fee: double
Ledger_State_Post_Execution: struct<sender_account_balance_updated: double, settlement_status: string>
child 0, sender_account_balance_updated: double
child 1, settlement_status: string
Metadata: null
Final Output: string
Instruction: string
Context: string
Reasoned Action Summary: string
to
{'Instruction': Value('string'), 'Context': Value('string'), 'Reasoned Action Summary': Value('string'), 'Final Output': Value('string'), 'Metadata': Json(decode=True)}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
π Financial Agentic AI Data - Enterprise Evaluation Suites
Welcome to M&S Data Labs. We do not scrape data; we engineer pristine, highly structured enterprise AI infrastructure.
This repository hosts our Free Trust Samples, providing data engineers and founders with hands-on access to our tiered cognitive architectures. These samples allow you to rigorously stress-test our logic, formatting, and schema compliance in your own pipelines before upgrading to our massive production suites.
ποΈ Inside This Repository (The Free Samples)
We have uploaded three distinct evaluation samples so you can experience our architectural evolution:
- 1. The 5-Pillar Schema (100-Record Sample): High-impact, foundational logic (Instruction, Context, Action Summary, Output, Metadata). Built for agile teams and rapid baseline model tuning.
- 2. The 8-Element Framework (100-Record Sample): Engineered for growing AI architectures requiring deeper contextual reasoning, cross-field logic, and edge-case handling.
- 3. The 13-Element Cognitive Architecture (120-Record Sample): Our absolute powerhouse. Designed for Tier-1 financial institutions handling complex, multi-step Agentic execution, stringent policy controls, and extreme payment gateway failovers.
(Download the respective files from the "Files and versions" tab to test them).
π‘οΈ Absolute Provenance & Compliance
We build for enterprise safety.
- Zero-PII Guarantee: Zero real-world customer, account, or production banking records were intentionally used.
- 100% Synthetic: Every record is mathematically generated and deterministically mapped.
- Audit-Ready Infrastructure: Our full enterprise releases are delivered via a robust 21-file structure, complete with cryptographic SHA-256 checksums and machine-generated QA validation reports.
π Ready for Production? Get the Enterprise Master Suites
To train your models fully and deploy them commercially against extreme financial edge cases, you need the complete cognitive package.
Available Premium Enterprise Suites (In Our Store):
- The 10K Foundation Suites (5-Pillar & 8-Element): Ideal for rigorous stress-testing and workflow integration.
- The 12.5K Enterprise Lite Suite (13-Element): Expanded logic for dynamic tax handling and cross-border routing.
- The 25K Enterprise Master Suite (13-Element): The ultimate data engine. Maximum coverage for AML, Fraud Detection, and multi-step failover scenarios.
π Click Here to Visit Our Official Store and Upgrade to the Full Master Suites
π¬ Need Custom Generation? We offer On-Demand Enterprise Generation to inject your proprietary routing logic directly into our pipeline. Connect with us on LinkedIn to discuss custom solutions!
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