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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Failed to parse string: 'pump_and_dump' as a scalar of type double
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 784, 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 795, in _write_table
                  pa_table = table_cast(pa_table, self._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 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
                  return array_cast(
                      array,
                  ...<2 lines>...
                      allow_decimal_to_str=allow_decimal_to_str,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2015, in array_cast
                  return array.cast(pa_type)
                         ~~~~~~~~~~^^^^^^^^^
                File "pyarrow/array.pxi", line 1186, in pyarrow.lib.Array.cast
                File "/usr/local/lib/python3.14/site-packages/pyarrow/compute.py", line 414, in cast
                  return call_function("cast", [arr], options, memory_pool)
                File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
                File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
                  result = GetResultValue(
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Failed to parse string: 'pump_and_dump' as a scalar of type double
              
              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 1694, 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 1879, 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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tx_hash
string
from
string
to
string
amount
float64
token
string
tx_type
string
is_illicit
int64
typology
null
case_id
null
timestamp
string
block
int64
0xafdd57a32ffc7e7e4486e1a7af6758211f00ba86
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USDX
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USDX
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GOVA
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USDY
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29
0x4b0a38ab5704e4b3e7f352bfec6be547f106a7fd
0xd05048184696943358325d33cd40f47fae5a51a9
0xa6c7856dcb4588e5b4b1a0f85c8bf6cb13fcf4e7
878.943189
USDY
swap_out
0
null
null
2025-03-01 00:05:58
29
0x4356c1b72d91495d6a182edd56f1fdf623451688
0x4f1ef4cfe951273e8d843d5631c0bf79992bf985
0x5a1ff4b68a7afd144fc5df90c1fbc2918e2594e4
478.388033
GOVA
swap_in
0
null
null
2025-03-01 00:06:03
30
0x014cf57f69541dc48dcabc6c068671dadc87ad31
0xa9f5d3796f458b8537b076b1570338330adfbd29
0xecb4978a6400e54cd33989d5e5bd1cd8f0515844
414.967578
USDX
token_transfer
0
null
null
2025-03-01 00:06:15
31
0x446f79600908c9c65a7e363ac4a583957003c1d9
0x5a1ff4b68a7afd144fc5df90c1fbc2918e2594e4
0x4f1ef4cfe951273e8d843d5631c0bf79992bf985
471.752586
USDX
swap_out
0
null
null
2025-03-01 00:06:18
31
0x36d392ddfaf02207e61bf21b62c6a9c440535317
0xc57c16378dded9685483ce71e58193f15e0cca47
0xaec3ee473c93e1ee8a9d7395902085cee816dd88
0.165052
NATIVE
exchange_withdrawal
0
null
null
2025-03-01 00:06:26
32
0x3e857e4b20c68751db92dd6a1d16f5e69f8d5421
0x2903d9dcd10c111b8a3bc01a5d33f9eb535b31ec
0xdc2d9e8042dae384428f16f35d4aa7ca0d5aeab8
0.00879
NATIVE
exchange_withdrawal
0
null
null
2025-03-01 00:06:54
34
0xe1351154074dabdc91d9c9cfa582632b64818658
0x5aa5a686d5bb2251b4826ac427c8cbec9b57f359
0x459d7802b653c5b7aa9029ca4cdf65fe3cfaeb13
700.415077
USDX
bridge_out
0
null
null
2025-03-01 00:07:31
37
0x30fdd22839d57cec9ed1a66ce5e378aa5a213052
0x2903d9dcd10c111b8a3bc01a5d33f9eb535b31ec
0x6c5d069e34f7a3168d8956b09f278e48a725392f
0.324318
NATIVE
exchange_withdrawal
0
null
null
2025-03-01 00:07:32
37
0x1cadd8b04bd332fa978e6b70855c69b6d307585f
0x1bb0fdb6c8a3fa9ea370037b9849a999c56d2103
0x5a1ff4b68a7afd144fc5df90c1fbc2918e2594e4
5,129.908515
WBTX
swap_in
0
null
null
2025-03-01 00:07:35
37
0xab77dad4f4f3730dd983a2bb9a4b05e9ef331cd2
0x601dc3eebba955491b0b286c2d85bcd1129a8fd7
0x5a5f5956ba80ed3f41f3030ea151c1e5960db775
0.300176
NATIVE
transfer
0
null
null
2025-03-01 00:07:36
38
0x407d1a178c9c867409954eb24e0e35c6e64c05b4
0xc6d46ce37e817ca9072f275c6d340abacd4207b6
0xc607a8a94087b1243e6de1d67235a6f89677c370
71.561156
USDY
token_transfer
0
null
null
2025-03-01 00:07:46
38
0x8e7c6d3abc459048205b892e7a87fba11371fb11
0xbef64769fa563dcdd667fb03f9477e56cffbb640
0x1246fc33891377263fd4384e33a0c0be3e2ee0ef
0.072892
NATIVE
transfer
0
null
null
2025-03-01 00:07:46
38
0x35bc07c006e7c9d444545b087a8ef3e5c6a02c85
0x5a1ff4b68a7afd144fc5df90c1fbc2918e2594e4
0x1bb0fdb6c8a3fa9ea370037b9849a999c56d2103
5,071.10841
USDX
swap_out
0
null
null
2025-03-01 00:07:50
39
0x77de217e574b6ded259b0b93bf1ebc52a483c36f
0x020944c1f8a2dd7ff7d6b81182aad7d69b5576cc
0x4a1129ca74b612202723faf5bd1f87193b54f96e
2,317.275684
USDY
swap_in
0
null
null
2025-03-01 00:07:57
39
0x4e61e1d3c4225c30f4c036a610e499bc3b20e7ec
0x16d8592d5437f422888c128b6e23913fb3723466
0xa7639375286a8aea108e290259bf19b1564df769
0.484227
NATIVE
exchange_deposit
0
null
null
2025-03-01 00:07:58
39
0x28415b35e966289d47d4728b2955e1a99e8b39a6
0x6cb8aa328eccff0063c6ac000f6572d13e958350
0xf0d9b11a213920add4b6d5d3669d1cc5a3c41590
816.156121
USDY
swap_in
0
null
null
2025-03-01 00:08:00
40
0x728999943de605fe5af78bff6ad1f5cbdd0ec954
0x2bed8355d726245bf97f60a2c7fbf087ab9ed379
0x1935c555488962b0e5d3400527900027bc7e83c6
0.258626
NATIVE
exchange_withdrawal
0
null
null
2025-03-01 00:08:07
40
0x56cbea7cee567ca253c8997addfb54713d357b5a
0x89efbb88420804f5555d5ab141afd2f042125224
0x7268339c3283d97bb23b7034235e6f46e5f3a0b0
18.330669
NATIVE
payment
0
null
null
2025-03-01 00:08:09
40
0x8caf6766e11912cc19a302b7b0eb3e44c34766cd
0x4a1129ca74b612202723faf5bd1f87193b54f96e
0x020944c1f8a2dd7ff7d6b81182aad7d69b5576cc
2,300.311039
USDY
swap_out
0
null
null
2025-03-01 00:08:12
41
0x72639f4fd27b9268ba9534738893132c322d8027
0xf0d9b11a213920add4b6d5d3669d1cc5a3c41590
0x6cb8aa328eccff0063c6ac000f6572d13e958350
809.939513
USDY
swap_out
0
null
null
2025-03-01 00:08:15
41
0x8d58173ec4330d0019adbbf7c116e8397af75ec9
0x43682fbf0364d0a54a4ddd835119af9e671c3c99
0xefecaae9b04bfa901abe62945d4e01e2dad1ba5c
0.100411
NATIVE
nft_purchase
0
null
null
2025-03-01 00:08:25
42
0xf4ed4a49945edb4e0965f407e15afef25af2c7fc
0x4f564d6a693f7ad922ccb24a4ad96178fadc9206
0xd05048184696943358325d33cd40f47fae5a51a9
82.27576
WBTX
swap_in
0
null
null
2025-03-01 00:08:33
42
0xb9d4da0b12533571602da2b378e92c3eb3173a08
0x3b5f2e5319ffc0446531944e30a533df211c40a4
0xa7639375286a8aea108e290259bf19b1564df769
0.064169
NATIVE
exchange_deposit
0
null
null
2025-03-01 00:08:48
44
0x23f4fbae765763b640fa825f96fdf3a1ca657951
0xd05048184696943358325d33cd40f47fae5a51a9
0x4f564d6a693f7ad922ccb24a4ad96178fadc9206
80.629559
WBTX
swap_out
0
null
null
2025-03-01 00:08:48
44
0x24a2f641b79a6672b34e1f25e97c51e9a2c8ef34
0xa5413c23d6c7f08af197da1a23e6c689607f16b9
0x2dcf7b6e9621b0327a4e5a112844ad20ca6e55d7
7,454.867785
USDX
token_transfer
0
null
null
2025-03-01 00:08:49
44
0x43251f25621fe72807065d0fd7f8e6b06ac99138
0x73013742bd66c5930fd213d382ea1bf3e5f35574
0x79205e28b4f352befb94fbe1adad3cea92c1fe09
52.960788
USDY
token_transfer
0
null
null
2025-03-01 00:08:54
44
0xabc64e0898b005a9d9b00e5c94d1166c97166095
0xe18d76704c6c1b41e2c03ad251e396393ffa47b5
0x10a946f4542b40202fb6599920389e962e41ed1e
45.362809
WBTX
swap_in
0
null
null
2025-03-01 00:09:08
45
0x2bb174b45eb5e4bb6e4bc39a4b41ceaedc53d633
0xfc3da186852c397e78f1598b3c77cecf4f5685a5
0xf0d9b11a213920add4b6d5d3669d1cc5a3c41590
921.803884
USDX
swap_in
0
null
null
2025-03-01 00:09:12
46
0xcafc42c1acb35d54541519daff67064246648169
0x72f4b0a102211cc6955712dd2cbca07884184a2e
0xc57c16378dded9685483ce71e58193f15e0cca47
0.704786
NATIVE
exchange_deposit
0
null
null
2025-03-01 00:09:16
46
0x17b6da1e393dfa1f5d1c8095ba8d20f82c53fb99
0xfa7e23b591cdd730d70c02ebef7093d535f293d7
0xe40f59cf50f2fbcc1d236d4912eda17c280a46dc
21.252482
NATIVE
payment
0
null
null
2025-03-01 00:09:19
46
0xda302967191f5401da0c3fb12df34f2fb7a5c7e7
0x10a946f4542b40202fb6599920389e962e41ed1e
0xe18d76704c6c1b41e2c03ad251e396393ffa47b5
44.697063
NATIVE
swap_out
0
null
null
2025-03-01 00:09:23
46
0x068b6092c4bf0e4f71116d63823fb3a1d3978d20
0xf0d9b11a213920add4b6d5d3669d1cc5a3c41590
0xfc3da186852c397e78f1598b3c77cecf4f5685a5
916.289411
USDX
swap_out
0
null
null
2025-03-01 00:09:27
47
0xd7f61fbbb173762ebea8cc0381e06eb23eabf49a
0xa2f10c591a7203ef512ebd5c6f978c18c7ce2b07
0x820974abf9da570f76c651200d9410ae526a153e
0.060333
NATIVE
exchange_deposit
0
null
null
2025-03-01 00:09:31
47
0x1ac39df325fa2b43c854e11503902c02b38a12ba
0x70f999dd0d824a627151788c6f4f731a685bb20d
0xa05dba0815efafd474ad584698751d89326ea0d0
236.234033
WBTX
swap_in
0
null
null
2025-03-01 00:09:34
47
0x0dd75577c241f9bcc20e9525413114f5c46b2c79
0x7302af331d599f7385495d121fafb655092479c4
0x5083e8cd792fb6282428920ccab1714021ab0553
0.082298
NATIVE
swap_in
0
null
null
2025-03-01 00:09:42
48
0x4b416fff07aeb5222e90cd5e8e72d315b13ccf99
0x00cbd4d290d0cf5be8814642908573d1ec5f0db5
0x67d509457e55ea7608d51f7331863da79f1dedad
279.182445
WBTX
token_transfer
0
null
null
2025-03-01 00:09:44
48
0x7b9bb691f3b6619a5edbf11e631a27bbec57e776
0xa05dba0815efafd474ad584698751d89326ea0d0
0x70f999dd0d824a627151788c6f4f731a685bb20d
230.573244
USDX
swap_out
0
null
null
2025-03-01 00:09:49
49
0xb21c1844eeb01fb6107557cf08700222d1592219
0x3fceae8f6c9e6a00e47d72097f7bed817d4587bc
0x61c2404facee42e5aaa8bbd88c2f849f93cd7b35
0.136821
NATIVE
exchange_withdrawal
0
null
null
2025-03-01 00:09:53
49
0x651fdbc581d167fd364483c93679399689460091
0x5083e8cd792fb6282428920ccab1714021ab0553
0x7302af331d599f7385495d121fafb655092479c4
0.080414
WBTX
swap_out
0
null
null
2025-03-01 00:09:57
49
0x1d342b690b6fd8a04a9da5d01ef16a4ec612add4
0xd5b4b8755d0f6307a21d086af31a7b924a89d4b9
0xff0934ae21c5a77a129500edcd60b89918ecf23d
0.180165
NATIVE
exchange_deposit
0
null
null
2025-03-01 00:10:22
51
0x895e97b9f9bd2582cfac11ac14962d4561550df3
0xeb5feb5b044bb388b0db7e594b99dabb444ba314
0xa7639375286a8aea108e290259bf19b1564df769
0.029801
NATIVE
exchange_deposit
0
null
null
2025-03-01 00:10:36
53
0xb2decb98b565a90cc5e3cc2017e6faba487ff29d
0x79923b884666457a11cf601c0454c72798b09234
0xf9d32d9cd0959c0421daebe3b727a77c3fa49a8f
149.391085
WBTX
swap_in
0
null
null
2025-03-01 00:10:40
53
0x21862a1e097e7afe40ebc07a2db8d122c320ce04
0x571dba41e2f59d56e91ea29e87be526af4df1c76
0xf89b0f92b3acccd043f04c2f8b052756a2c8d482
258.145267
USDX
payment
0
null
null
2025-03-01 00:10:47
53
0xd905b031c3e2842d260c1ac22b82d48f7015d2a0
0xf9d32d9cd0959c0421daebe3b727a77c3fa49a8f
0x79923b884666457a11cf601c0454c72798b09234
147.078404
USDX
swap_out
0
null
null
2025-03-01 00:10:55
54
0x32e8df07de3f9334e8e696c853b724bfdf1cf054
0xf310ccd9b5f5ecd7f64b88f45eeb01f2e7b0326c
0x1caf0c6742177d3a7d292c3200fe467b7f58ee70
9,489.820405
USDY
swap_in
0
null
null
2025-03-01 00:10:59
54
0x246e438c563ecba8e735186cedc445b3c57776c0
0x1ce423d5af22e7560a8aef1f7bcefe20020c5e74
0xc57c16378dded9685483ce71e58193f15e0cca47
0.114592
NATIVE
exchange_deposit
0
null
null
2025-03-01 00:11:02
55
0x0ce68c3372bc660b600075ab73befd7a56c26055
0x34b10faf6596f2a3ec75e8b530c10c9cf4f1b487
0x2903d9dcd10c111b8a3bc01a5d33f9eb535b31ec
0.032603
NATIVE
exchange_deposit
0
null
null
2025-03-01 00:11:11
55
End of preview.

QM Synthetic Crypto AML: On-chain Illicit Flows - Free Sample

Buy the full commercial edition: $30 USD - launch price until 24 Oct 2026 (regular price $49 USD) -> Polar checkout, instant download Also on Gumroad.
This free sample is non-commercial (CC BY-NC-SA 4.0). The paid full edition has a commercial licence.

Summary

Synthetic on-chain (EVM-style) crypto transfers with labelled illicit flows, for crypto AML, wallet-risk and graph-ML models; the paid full edition can be used commercially.

  • Best for: crypto AML / wallet-risk prototyping, GNN and path-feature research, compliance tooling demos
  • Not for: sanctions screening or attributing real wallets

Quick start

from datasets import load_dataset
ds = load_dataset("quailrobot/crypto-aml-v1-sample", split="train")  # main table: transfers_sample.csv
# pandas alternative: pd.read_csv("hf://datasets/quailrobot/crypto-aml-v1-sample/transfers_sample.csv")

Facts

  • Task: tabular / graph binary classification (illicit transfer detection) + typology evaluation
  • Labels: is_illicit (0/1); typology = phishing_drainer, peel_chain, mixer_layering, rug_pull, pump_and_dump; case_id; wallet role
  • Full edition size: 1,243,565 transfers, 78,922 wallets, 90 days, 150 illicit cases (zip 112 MB)
  • Free sample size: 211,648 transfers = first 15 days (transfers_sample.csv); roles and cases only for illicit activity seen in that window
  • Format: full: Parquet (transfers) + CSV (wallets, cases); sample: CSV
  • What's included (full edition): transfers.parquet, wallets.csv, cases.csv, stats.json, benchmark.json, bench_crypto_aml.py, README.md, LICENSE.txt
  • Price: $30 USD, launch price until 24 Oct 2026 (regular price $49 USD); checkout may display the equivalent in your local currency
  • Buy URL (primary): https://buy.polar.sh/polar_cl_Tp2K2g2Qk1WYAxMRXoMbGvNX23DxKj9mcjvMM24q3li
  • Buy URL (secondary, also on Gumroad): https://quailcraft1.gumroad.com/l/synthetic-crypto-aml-onchain
  • Licence (full edition): LicenseRef-QuailModel-Commercial (commercial use allowed, no resale of the data)
  • Licence (free sample): CC-BY-NC-SA-4.0
  • Validation: Our own test on the full edition (time split, train first 70 % of days): class-balanced gradient boosting on 8 flat per-transfer features ROC-AUC 0.967, PR-AUC 0.169 (benchmark.json + script included). Graph methods should lift PR-AUC.
  • Data source: 100% synthetic, generated by QuailModel with AI assistance (generator code written with an AI model)
  • Catalog (all QuailModel datasets, catalog.json, llms.txt): https://quailrobot-quailmodel.static.hf.space
  • Last updated: 2026-10-10

Validation

Our own test on the full edition (time split, train first 70 % of days): class-balanced gradient boosting on 8 flat per-transfer features ROC-AUC 0.967, PR-AUC 0.169 (benchmark.json + script included). Graph methods should lift PR-AUC.

Price & licence

Limitations

  • 100% synthetic: no real addresses or chains; not validated against real chain data.
  • typology, case_id and wallet role are labels - exclude them from model inputs.

Custom dataset of YOUR object ($249)

Need data of YOUR object? Custom synthetic dataset, $249 USD -> order on Polar

  • Note: this is an image / object-detection service (not tabular data).
  • What you get: 2,000 labelled photoreal synthetic images (640x640 JPEG) of your own object or scenario (product, part, tool, drone, package, defect...), up to 3 classes, YOLO bounding boxes + data.yaml, train/val/test split, quality report
  • Licence: commercial use allowed
  • Price: $249 USD one-time; one round of adjustments included
  • Delivery: typically 3-5 business days after we receive your reference photos + rough dimensions
  • Optional sim-to-real test: send ~200 of your own labelled real images and we report how much the synthetic data improves a detector on them
  • Refund: full refund if we cannot deliver your request (14-day refund policy)
  • Limits: only objects you own or are allowed to use; no weapons or anything meant to harm people; no copied third-party 3D assets
  • Order URL: https://buy.polar.sh/polar_cl_AQu6LzRtqiQmKPlgPt0zePBtvWjJI4vULeFMQ4NLHH9
  • Details + contact: https://quailrobot-quailmodel.static.hf.space

This free sample: 211,648 transfers = first 15 days (transfers_sample.csv); roles and cases only for illicit activity seen in that window. Full commercial edition: 1,243,565 transfers, 78,922 wallets, 90 days, 150 illicit cases (zip 112 MB). Fully synthetic data; summary, facts, validation and limitations are in the block above.

Files

transfers (tx_hash, from, to, amount, token, tx_type, timestamp, block, is_illicit, typology, case_id), wallets.csv (address, entity_type, role), cases.csv (case_id, typology, n_transfers). typology, case_id and wallet role are LABELS - exclude them from model inputs.

How it was made

Produced by QuailModel's own simulator, including legitimate activity alongside the labelled patterns. Labels are exact, because they are known by construction.

Credits

No third-party data was used.

Licence

Sample edition: CC BY-NC-SA 4.0 (non-commercial). The full commercial edition is sold by QuailModel (see the buy link).

Disclosure

Generated by QuailModel with AI assistance (generator code written with an AI model); all data is synthetic / computer-generated. Validate on your own real data before production use.

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