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Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 6 new columns ({'jurisdiction', 'ip_country', 'app_type', 'event_id', 'country', 'local_currency'}) and 3 missing columns ({'banking_hours_indicator', 'arrival_duration_minutes', 'payment_code'}).
This happened while the csv dataset builder was generating data using
hf://datasets/navk8690/paymind-reference-data/payment_method copy.csv (at revision f739e7f1818722bbc87681447e6403b88d8ea6a0), ['hf://datasets/navk8690/paymind-reference-data@f739e7f1818722bbc87681447e6403b88d8ea6a0/arrival copy.csv', 'hf://datasets/navk8690/paymind-reference-data@f739e7f1818722bbc87681447e6403b88d8ea6a0/payment_method copy.csv', 'hf://datasets/navk8690/paymind-reference-data@f739e7f1818722bbc87681447e6403b88d8ea6a0/success copy.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._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
event_id: string
timestamp_utc: string
transaction_type: string
country: string
ip_country: string
jurisdiction: string
currency: string
local_currency: string
amount: double
app_type: string
hour: int64
day_of_week: string
is_weekend: int64
is_cross_border: int64
payment_type: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2081
to
{'timestamp_utc': Value('string'), 'transaction_type': Value('string'), 'currency': Value('string'), 'amount': Value('float64'), 'hour': Value('int64'), 'day_of_week': Value('string'), 'is_weekend': Value('int64'), 'is_cross_border': Value('int64'), 'payment_code': Value('string'), 'payment_type': Value('string'), 'banking_hours_indicator': Value('int64'), 'arrival_duration_minutes': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
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 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 6 new columns ({'jurisdiction', 'ip_country', 'app_type', 'event_id', 'country', 'local_currency'}) and 3 missing columns ({'banking_hours_indicator', 'arrival_duration_minutes', 'payment_code'}).
This happened while the csv dataset builder was generating data using
hf://datasets/navk8690/paymind-reference-data/payment_method copy.csv (at revision f739e7f1818722bbc87681447e6403b88d8ea6a0), ['hf://datasets/navk8690/paymind-reference-data@f739e7f1818722bbc87681447e6403b88d8ea6a0/arrival copy.csv', 'hf://datasets/navk8690/paymind-reference-data@f739e7f1818722bbc87681447e6403b88d8ea6a0/payment_method copy.csv', 'hf://datasets/navk8690/paymind-reference-data@f739e7f1818722bbc87681447e6403b88d8ea6a0/success copy.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
timestamp_utc string | transaction_type string | currency string | amount float64 | hour int64 | day_of_week string | is_weekend int64 | is_cross_border int64 | payment_code string | payment_type string | banking_hours_indicator int64 | arrival_duration_minutes float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|
2022-06-15 00:26:25 | deposit | CAD | 87.87 | 0 | Wednesday | 0 | 0 | bank_transfer | bank_transfer | 0 | 9.84 |
2025-01-16 09:55:09 | deposit | SGD | 3,155.03 | 9 | Thursday | 0 | 1 | worldpay | worldpay | 1 | 0.04 |
2022-07-18 08:48:19 | deposit | EUR | 768.82 | 8 | Monday | 0 | 0 | checkout | checkout | 0 | 0.11 |
2022-12-17 07:51:10 | deposit | AUD | 6,300.37 | 7 | Saturday | 1 | 0 | paypal | paypal | 0 | 0.02 |
2024-10-07 02:39:27 | deposit | NZD | 154.82 | 2 | Monday | 0 | 0 | revolut_pay | revolut_pay | 0 | 0.15 |
2022-11-09 22:57:09 | deposit | NZD | 65.24 | 22 | Wednesday | 0 | 1 | paypal | paypal | 0 | 0.04 |
2025-02-08 15:36:18 | deposit | USD | 12,967.55 | 15 | Saturday | 1 | 0 | wise | wise | 0 | 2.97 |
2023-06-14 12:34:45 | deposit | JPY | 2,709.94 | 12 | Wednesday | 0 | 0 | wise | wise | 1 | 0.18 |
2023-12-08 18:33:31 | withdrawal | GBP | 702.84 | 18 | Friday | 0 | 1 | wise | wise | 0 | 7.08 |
2022-07-05 23:40:23 | withdrawal | GBP | 6,274.61 | 23 | Tuesday | 0 | 1 | bank_transfer | bank_transfer | 0 | 260.51 |
2024-07-05 17:57:48 | deposit | USD | 3,826.49 | 17 | Friday | 0 | 0 | adyen | adyen | 0 | 0.1 |
2026-04-10 02:14:33 | deposit | EUR | 2,566.8 | 2 | Friday | 0 | 1 | bank_transfer | bank_transfer | 0 | 504.39 |
2024-03-12 13:34:23 | deposit | JPY | 116.85 | 13 | Tuesday | 0 | 0 | adyen | adyen | 1 | 0.91 |
2023-02-04 21:11:06 | deposit | USD | 144.05 | 21 | Saturday | 1 | 0 | paypal | paypal | 0 | 0.41 |
2025-12-20 05:02:50 | deposit | CAD | 374.59 | 5 | Saturday | 1 | 0 | wise | wise | 0 | 3.7 |
2023-05-25 09:13:26 | withdrawal | SGD | 114.33 | 9 | Thursday | 0 | 0 | bank_transfer | bank_transfer | 1 | 1,022.1 |
2022-04-21 15:44:18 | deposit | SGD | 445.74 | 15 | Thursday | 0 | 0 | checkout | checkout | 1 | 0.56 |
2022-05-23 16:16:46 | deposit | AUD | 472.02 | 16 | Monday | 0 | 0 | worldpay | worldpay | 1 | 0.23 |
2026-02-28 04:47:34 | deposit | JPY | 10,541.95 | 4 | Saturday | 1 | 0 | bank_transfer | bank_transfer | 0 | 754.62 |
2022-09-12 16:55:03 | deposit | EUR | 324.32 | 16 | Monday | 0 | 0 | stripe | stripe | 1 | 0.2 |
2024-12-07 04:31:08 | deposit | JPY | 2,181.28 | 4 | Saturday | 1 | 0 | worldpay | worldpay | 0 | 0.5 |
2022-03-01 08:48:13 | withdrawal | NZD | 24.34 | 8 | Tuesday | 0 | 1 | wise | wise | 0 | 5.55 |
2024-03-23 00:10:51 | deposit | CAD | 130.81 | 0 | Saturday | 1 | 0 | square | square | 0 | 0.1 |
2024-07-12 04:29:52 | deposit | EUR | 9,199.56 | 4 | Friday | 0 | 0 | worldpay | worldpay | 0 | 0.22 |
2024-04-13 11:16:14 | deposit | SGD | 150.35 | 11 | Saturday | 1 | 0 | square | square | 0 | 0.25 |
2025-09-25 20:32:56 | deposit | NZD | 34.99 | 20 | Thursday | 0 | 0 | revolut_pay | revolut_pay | 0 | 0.78 |
2025-01-03 13:01:24 | deposit | USD | 31.71 | 13 | Friday | 0 | 1 | adyen | adyen | 1 | 1.53 |
2025-01-30 16:42:44 | deposit | AUD | 13,908.09 | 16 | Thursday | 0 | 1 | wise | wise | 1 | 21.03 |
2022-07-23 12:04:17 | deposit | AUD | 64.52 | 12 | Saturday | 1 | 1 | stripe | stripe | 0 | 0.01 |
2025-09-20 14:19:58 | deposit | GBP | 981.6 | 14 | Saturday | 1 | 0 | adyen | adyen | 0 | 0.62 |
2022-06-29 02:52:16 | deposit | SGD | 9,253.61 | 2 | Wednesday | 0 | 0 | checkout | checkout | 0 | 0.69 |
2025-04-25 05:49:54 | withdrawal | CAD | 441.05 | 5 | Friday | 0 | 0 | checkout | checkout | 0 | 48.11 |
2023-08-16 00:49:24 | withdrawal | EUR | 36.22 | 0 | Wednesday | 0 | 0 | wise | wise | 0 | 614.64 |
2023-05-22 08:30:30 | deposit | GBP | 218.03 | 8 | Monday | 0 | 0 | adyen | adyen | 0 | 0.34 |
2026-01-06 02:17:14 | deposit | SGD | 106.06 | 2 | Tuesday | 0 | 0 | adyen | adyen | 0 | 0.02 |
2023-12-12 12:47:17 | deposit | SGD | 10,137.34 | 12 | Tuesday | 0 | 0 | stripe | stripe | 1 | 0.08 |
2025-07-20 03:11:43 | deposit | GBP | 12,649.36 | 3 | Sunday | 1 | 0 | stripe | stripe | 0 | 0.09 |
2026-06-12 09:34:07 | deposit | NZD | 8,061.88 | 9 | Friday | 0 | 1 | paypal | paypal | 1 | 0.06 |
2022-01-15 02:38:16 | deposit | USD | 3,070.81 | 2 | Saturday | 1 | 1 | checkout | checkout | 0 | 0.33 |
2024-05-22 09:12:33 | withdrawal | NZD | 3,267.38 | 9 | Wednesday | 0 | 0 | revolut_pay | revolut_pay | 1 | 454.8 |
2023-05-26 22:12:18 | deposit | EUR | 35.64 | 22 | Friday | 0 | 0 | paypal | paypal | 0 | 0.09 |
2025-11-05 04:36:31 | deposit | SGD | 10.24 | 4 | Wednesday | 0 | 0 | paypal | paypal | 0 | 2.98 |
2025-02-23 09:44:38 | deposit | EUR | 5,393.08 | 9 | Sunday | 1 | 0 | worldpay | worldpay | 0 | 0.17 |
2023-08-31 09:56:59 | deposit | USD | 12.14 | 9 | Thursday | 0 | 0 | revolut_pay | revolut_pay | 1 | 0.84 |
2025-01-23 12:01:05 | deposit | CAD | 149.99 | 12 | Thursday | 0 | 0 | paypal | paypal | 1 | 0.08 |
2023-02-01 11:43:17 | deposit | JPY | 1,481.72 | 11 | Wednesday | 0 | 0 | worldpay | worldpay | 1 | 2.6 |
2025-03-22 05:04:44 | deposit | EUR | 3,236.84 | 5 | Saturday | 1 | 0 | adyen | adyen | 0 | 0.04 |
2024-04-12 13:38:37 | deposit | AUD | 60.11 | 13 | Friday | 0 | 0 | worldpay | worldpay | 1 | 0.68 |
2026-01-11 01:40:42 | withdrawal | GBP | 2,158.54 | 1 | Sunday | 1 | 0 | revolut_pay | revolut_pay | 0 | 1.35 |
2025-10-12 20:52:54 | deposit | USD | 912 | 20 | Sunday | 1 | 0 | square | square | 0 | 0.13 |
2022-06-25 18:03:03 | deposit | NZD | 1,637.49 | 18 | Saturday | 1 | 0 | wise | wise | 0 | 3.94 |
2022-10-16 18:26:57 | withdrawal | NZD | 10.34 | 18 | Sunday | 1 | 1 | worldpay | worldpay | 0 | 17.29 |
2022-06-10 13:29:04 | deposit | NZD | 5,332.56 | 13 | Friday | 0 | 0 | worldpay | worldpay | 1 | 1.26 |
2025-07-17 09:16:30 | deposit | NZD | 7,793.54 | 9 | Thursday | 0 | 0 | bank_transfer | bank_transfer | 1 | 26.25 |
2023-03-01 11:18:17 | deposit | GBP | 45.55 | 11 | Wednesday | 0 | 0 | checkout | checkout | 1 | 0.06 |
2022-11-16 01:34:04 | withdrawal | GBP | 5,004.82 | 1 | Wednesday | 0 | 0 | checkout | checkout | 0 | 60.44 |
2026-06-01 17:56:52 | withdrawal | GBP | 4,966.39 | 17 | Monday | 0 | 0 | adyen | adyen | 0 | 25.93 |
2026-03-01 00:52:45 | deposit | AUD | 1,442.43 | 0 | Sunday | 1 | 1 | worldpay | worldpay | 0 | 0.21 |
2023-12-01 04:42:39 | deposit | CAD | 8,659.03 | 4 | Friday | 0 | 0 | paypal | paypal | 0 | 1.07 |
2026-03-10 09:22:41 | deposit | CAD | 1,905.99 | 9 | Tuesday | 0 | 0 | revolut_pay | revolut_pay | 1 | 0.5 |
2025-09-10 14:58:58 | deposit | JPY | 22.39 | 14 | Wednesday | 0 | 0 | paypal | paypal | 1 | 0.03 |
2025-12-27 07:54:22 | deposit | USD | 11.11 | 7 | Saturday | 1 | 0 | checkout | checkout | 0 | 0.34 |
2024-07-16 08:42:52 | withdrawal | NZD | 14.48 | 8 | Tuesday | 0 | 0 | bank_transfer | bank_transfer | 0 | 640.81 |
2023-04-14 19:39:25 | deposit | GBP | 7,829.96 | 19 | Friday | 0 | 0 | checkout | checkout | 0 | 0.36 |
2024-06-05 17:22:53 | deposit | USD | 12.99 | 17 | Wednesday | 0 | 0 | worldpay | worldpay | 0 | 0.47 |
2024-09-22 05:28:43 | withdrawal | USD | 946.57 | 5 | Sunday | 1 | 0 | paypal | paypal | 0 | 13.78 |
2026-02-25 19:34:07 | withdrawal | JPY | 5,974.64 | 19 | Wednesday | 0 | 0 | revolut_pay | revolut_pay | 0 | 2.78 |
2024-09-10 08:26:16 | withdrawal | GBP | 29.07 | 8 | Tuesday | 0 | 0 | checkout | checkout | 0 | 4.34 |
2022-02-25 14:09:35 | withdrawal | NZD | 871.17 | 14 | Friday | 0 | 0 | revolut_pay | revolut_pay | 1 | 30.42 |
2026-06-03 14:14:49 | deposit | EUR | 4,757.34 | 14 | Wednesday | 0 | 0 | adyen | adyen | 1 | 0.06 |
2023-05-01 13:25:08 | deposit | EUR | 85.54 | 13 | Monday | 0 | 0 | square | square | 1 | 0.03 |
2022-05-10 03:50:29 | withdrawal | AUD | 1,774.29 | 3 | Tuesday | 0 | 0 | adyen | adyen | 0 | 10.45 |
2024-09-21 16:17:54 | deposit | SGD | 63.37 | 16 | Saturday | 1 | 0 | adyen | adyen | 0 | 0.2 |
2024-10-24 01:00:34 | deposit | USD | 7,102.78 | 1 | Thursday | 0 | 0 | stripe | stripe | 0 | 0.85 |
2023-07-19 11:40:58 | deposit | JPY | 836.43 | 11 | Wednesday | 0 | 1 | checkout | checkout | 1 | 0.21 |
2025-02-13 05:16:34 | deposit | SGD | 498.75 | 5 | Thursday | 0 | 0 | wise | wise | 0 | 1.52 |
2022-10-05 16:45:37 | deposit | EUR | 296.86 | 16 | Wednesday | 0 | 0 | revolut_pay | revolut_pay | 1 | 0.06 |
2023-04-06 20:29:34 | withdrawal | NZD | 401.03 | 20 | Thursday | 0 | 0 | wise | wise | 0 | 10.02 |
2024-10-25 17:30:17 | deposit | JPY | 5,863.46 | 17 | Friday | 0 | 0 | revolut_pay | revolut_pay | 0 | 0.56 |
2022-06-07 10:40:10 | deposit | JPY | 16.54 | 10 | Tuesday | 0 | 0 | adyen | adyen | 1 | 0.05 |
2024-04-30 23:56:24 | deposit | USD | 7,185.95 | 23 | Tuesday | 0 | 0 | paypal | paypal | 0 | 0.01 |
2024-05-01 15:37:37 | deposit | SGD | 8,805.93 | 15 | Wednesday | 0 | 0 | checkout | checkout | 1 | 0.06 |
2025-04-26 15:04:36 | deposit | AUD | 5,588.8 | 15 | Saturday | 1 | 0 | worldpay | worldpay | 0 | 0.28 |
2025-05-28 10:22:43 | deposit | GBP | 113.55 | 10 | Wednesday | 0 | 0 | checkout | checkout | 1 | 0.54 |
2025-12-05 09:10:38 | deposit | AUD | 24.2 | 9 | Friday | 0 | 0 | revolut_pay | revolut_pay | 1 | 0.13 |
2025-09-08 16:51:06 | deposit | CAD | 195.08 | 16 | Monday | 0 | 0 | worldpay | worldpay | 1 | 4.98 |
2023-01-18 20:15:31 | deposit | JPY | 154.31 | 20 | Wednesday | 0 | 0 | square | square | 0 | 0.13 |
2024-06-29 22:16:02 | deposit | SGD | 412.01 | 22 | Saturday | 1 | 1 | paypal | paypal | 0 | 0.09 |
2023-12-17 12:59:05 | withdrawal | JPY | 83.04 | 12 | Sunday | 1 | 0 | revolut_pay | revolut_pay | 0 | 23.18 |
2022-10-18 19:21:32 | deposit | JPY | 4,211.8 | 19 | Tuesday | 0 | 0 | bank_transfer | bank_transfer | 0 | 227.31 |
2025-07-08 13:49:54 | deposit | AUD | 2,440.87 | 13 | Tuesday | 0 | 0 | bank_transfer | bank_transfer | 1 | 316.22 |
2025-07-31 00:30:17 | withdrawal | NZD | 53.91 | 0 | Thursday | 0 | 0 | paypal | paypal | 0 | 7.4 |
2026-03-31 13:32:11 | withdrawal | SGD | 74.46 | 13 | Tuesday | 0 | 0 | wise | wise | 1 | 9.85 |
2025-12-22 11:36:14 | deposit | GBP | 282.19 | 11 | Monday | 0 | 0 | worldpay | worldpay | 1 | 1.96 |
2026-01-12 00:25:45 | deposit | EUR | 6,138.35 | 0 | Monday | 0 | 0 | worldpay | worldpay | 0 | 0.06 |
2024-03-08 22:15:43 | deposit | AUD | 121.74 | 22 | Friday | 0 | 0 | stripe | stripe | 0 | 0.01 |
2024-10-20 16:16:58 | deposit | JPY | 304.06 | 16 | Sunday | 1 | 1 | stripe | stripe | 0 | 0.03 |
2022-09-08 23:49:13 | deposit | CAD | 50.72 | 23 | Thursday | 0 | 0 | worldpay | worldpay | 0 | 0.05 |
2022-06-28 23:48:41 | withdrawal | AUD | 6,008.97 | 23 | Tuesday | 0 | 0 | adyen | adyen | 0 | 25.52 |
2023-08-16 06:57:53 | deposit | GBP | 6,640.32 | 6 | Wednesday | 0 | 1 | bank_transfer | bank_transfer | 0 | 140.87 |
PayMind Reference Dataset
Synthetic/reference payment-routing data for PayMind, an open-source payment route intelligence engine.
This dataset is designed to demonstrate PayMind's training, evaluation, and routing workflow across route selection, transaction reliability, and expected settlement time.
Important: This dataset contains synthetic/reference data only. It does not contain real customers, real transactions, payment credentials, personally identifiable information, or production payment-provider performance data.
Dataset Files
The dataset contains three primary CSV files:
payment_method.csv
Reference data for training the payment-method candidate model.
The model learns which payment routes are most relevant for a given transaction context.
success.csv
Reference data for training the transaction-success model.
The model estimates the probability of a transaction succeeding for an eligible payment route.
arrival.csv
Reference data for training the settlement model.
The model estimates expected transaction arrival/settlement times, including typical and more conservative settlement estimates.
Tasks
PayMind uses the dataset across multiple tabular machine-learning tasks:
- Payment route selection — identify relevant payment methods for a transaction.
- Transaction reliability — estimate the probability of successful processing.
- Settlement estimation — estimate expected transaction arrival time.
- Route intelligence — combine model outputs with eligibility rules and estimated fees to rank available payment routes.
Intended Use
This dataset is intended for:
- Demonstrating the PayMind architecture
- Training the PayMind reference models
- Testing the PayMind training pipeline
- Software development and integration testing
- Machine-learning experimentation
- Educational and research use
Users deploying PayMind in a real payment environment should train models using appropriately governed data from their own environment.
Data Source
The data provided in this repository is synthetic/reference data created for the PayMind open-source project.
It should not be interpreted as observed behaviour of any real payment provider, financial institution, customer, or payment network.
Privacy
The reference dataset is designed to contain no real:
- Customer identities
- Names or email addresses
- Account or card details
- Payment credentials
- Authentication tokens or API keys
- Production transaction records
- Personally identifiable information (PII)
- Proprietary payment-provider performance data
Reference Models
The PayMind reference models are trained using synthetic/reference data and are intended to demonstrate the architecture rather than provide production payment-routing benchmarks.
Models:
https://huggingface.co/navk8690/paymind-reference-models
Live Demo
Try PayMind through the interactive Hugging Face Space:
https://huggingface.co/spaces/navk8690/paymind
The demo evaluates transaction context, eligible payment routes, predicted reliability, settlement expectations, and estimated cost to produce a ranked route recommendation.
Source Code
PayMind is open source.
GitHub:
https://github.com/navjotk8690/paymind
The repository includes the SDK, API, model implementations, training pipeline, data contracts, reference configuration, tests, and Gradio demo.
Training
The PayMind project provides a training pipeline for rebuilding the reference models or training models using your own compatible datasets.
The expected datasets are:
payment_method.csv
success.csv
arrival.csv
Refer to the PayMind repository documentation for the exact schemas and training instructions.
Limitations
This dataset is provided as a reference implementation dataset.
Results obtained from models trained on this data:
- Do not represent actual payment-provider performance
- Should not be treated as production routing benchmarks
- Do not guarantee transaction success or settlement time
- Should not be used as a substitute for production-specific model validation
- May not reflect the distributions, constraints, or behaviour of a real payment environment
Production users should validate their own data, models, eligibility rules, fee configuration, and ranking strategy before deployment.
License
This dataset is released under the GNU General Public License v3.0 (GPL-3.0) as part of the PayMind open-source project.
See the repository license for full terms.
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