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Datasets:
amyrmahdy
/
decima-pii-testpack

Tasks:
Token Classification
Languages:
Arabic
Persian
Urdu
Size:
n<1K
Tags:
pii
pii-detection
test-set
arabic
persian
urdu
License:
Dataset card Data Studio Files
xet
 Community
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
all: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
  child 0, p: double
  child 1, r: double
  child 2, f1: double
  child 3, gold: int64
  child 4, pred: int64
  child 5, tp: int64
  child 6, untyped_f1: double
  child 7, detection: double
digits:arabic_indic: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
  child 0, p: double
  child 1, r: double
  child 2, f1: double
  child 3, gold: int64
  child 4, pred: int64
  child 5, tp: int64
  child 6, untyped_f1: double
  child 7, detection: double
digits:ascii: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
  child 0, p: double
  child 1, r: double
  child 2, f1: double
  child 3, gold: int64
  child 4, pred: int64
  child 5, tp: int64
  child 6, untyped_f1: double
  child 7, detection: double
digits:mixed: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
  child 0, p: double
  child 1, r: double
  child 2, f1: double
  child 3, gold: int64
  child 4, pred: int64
  child 5, tp: int64
  child 6, untyped_f1: double
  child 7, detection: double
digits:none: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
  child 0, p: double
  child 1, r: double
  child 2, 
...
, touched: int64
      child 1, total: int64
      child 2, rate: double
  child 15, org: struct<touched: int64, total: int64, rate: double>
      child 0, touched: int64
      child 1, total: int64
      child 2, rate: double
  child 16, sku: struct<touched: int64, total: int64, rate: double>
      child 0, touched: int64
      child 1, total: int64
      child 2, rate: double
  child 17, ticket: struct<touched: int64, total: int64, rate: double>
      child 0, touched: int64
      child 1, total: int64
      child 2, rate: double
  child 18, tracking: struct<touched: int64, total: int64, rate: double>
      child 0, touched: int64
      child 1, total: int64
      child 2, rate: double
  child 19, ts: struct<touched: int64, total: int64, rate: double>
      child 0, touched: int64
      child 1, total: int64
      child 2, rate: double
  child 20, version: struct<touched: int64, total: int64, rate: double>
      child 0, touched: int64
      child 1, total: int64
      child 2, rate: double
  child 21, wats: struct<touched: int64, total: int64, rate: double>
      child 0, touched: int64
      child 1, total: int64
      child 2, rate: double
pii_free_rows_with_prediction: struct<rows: int64, rate: double>
  child 0, rows: int64
  child 1, rate: double
id: string
spans: list<item: struct<start: int64, end: int64, label: string>>
  child 0, item: struct<start: int64, end: int64, label: string>
      child 0, start: int64
      child 1, end: int64
      child 2, label: string
to
{'id': Value('string'), 'spans': List({'start': Value('int64'), 'end': Value('int64'), 'label': 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 1835, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              all: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
                child 0, p: double
                child 1, r: double
                child 2, f1: double
                child 3, gold: int64
                child 4, pred: int64
                child 5, tp: int64
                child 6, untyped_f1: double
                child 7, detection: double
              digits:arabic_indic: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
                child 0, p: double
                child 1, r: double
                child 2, f1: double
                child 3, gold: int64
                child 4, pred: int64
                child 5, tp: int64
                child 6, untyped_f1: double
                child 7, detection: double
              digits:ascii: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
                child 0, p: double
                child 1, r: double
                child 2, f1: double
                child 3, gold: int64
                child 4, pred: int64
                child 5, tp: int64
                child 6, untyped_f1: double
                child 7, detection: double
              digits:mixed: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
                child 0, p: double
                child 1, r: double
                child 2, f1: double
                child 3, gold: int64
                child 4, pred: int64
                child 5, tp: int64
                child 6, untyped_f1: double
                child 7, detection: double
              digits:none: struct<p: double, r: double, f1: double, gold: int64, pred: int64, tp: int64, untyped_f1: double, de (... 16 chars omitted)
                child 0, p: double
                child 1, r: double
                child 2, 
              ...
              , touched: int64
                    child 1, total: int64
                    child 2, rate: double
                child 15, org: struct<touched: int64, total: int64, rate: double>
                    child 0, touched: int64
                    child 1, total: int64
                    child 2, rate: double
                child 16, sku: struct<touched: int64, total: int64, rate: double>
                    child 0, touched: int64
                    child 1, total: int64
                    child 2, rate: double
                child 17, ticket: struct<touched: int64, total: int64, rate: double>
                    child 0, touched: int64
                    child 1, total: int64
                    child 2, rate: double
                child 18, tracking: struct<touched: int64, total: int64, rate: double>
                    child 0, touched: int64
                    child 1, total: int64
                    child 2, rate: double
                child 19, ts: struct<touched: int64, total: int64, rate: double>
                    child 0, touched: int64
                    child 1, total: int64
                    child 2, rate: double
                child 20, version: struct<touched: int64, total: int64, rate: double>
                    child 0, touched: int64
                    child 1, total: int64
                    child 2, rate: double
                child 21, wats: struct<touched: int64, total: int64, rate: double>
                    child 0, touched: int64
                    child 1, total: int64
                    child 2, rate: double
              pii_free_rows_with_prediction: struct<rows: int64, rate: double>
                child 0, rows: int64
                child 1, rate: double
              id: string
              spans: list<item: struct<start: int64, end: int64, label: string>>
                child 0, item: struct<start: int64, end: int64, label: string>
                    child 0, start: int64
                    child 1, end: int64
                    child 2, label: string
              to
              {'id': Value('string'), 'spans': List({'start': Value('int64'), 'end': Value('int64'), 'label': 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 1379, 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 956, 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 1702, 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 1887, 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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id
string
spans
list
16270afab43d
[ { "start": 26, "end": 41, "label": "PERSON_NAME" }, { "start": 48, "end": 58, "label": "PERSON_NAME" }, { "start": 67, "end": 77, "label": "DATE_OF_BIRTH" } ]
a2409d8a5a5b
[ { "start": 55, "end": 73, "label": "NATIONAL_ID" }, { "start": 116, "end": 151, "label": "ADDRESS" }, { "start": 163, "end": 179, "label": "PERSON_NAME" } ]
0167b130ee77
[]
cf1a97860077
[ { "start": 52, "end": 69, "label": "CARD" }, { "start": 99, "end": 111, "label": "PERSON_NAME" } ]
08638d5528a2
[]
3afd130cd31b
[ { "start": 65, "end": 71, "label": "NATIONAL_ID" }, { "start": 81, "end": 91, "label": "PERSON_NAME" }, { "start": 139, "end": 157, "label": "EMAIL" }, { "start": 161, "end": 191, "label": "IP_ADDRESS" }, { "start": 257, "end": 264, "label"...
c0600da0c096
[ { "start": 19, "end": 29, "label": "NATIONAL_ID" }, { "start": 35, "end": 51, "label": "PERSON_NAME" } ]
945ea212db9d
[ { "start": 16, "end": 24, "label": "PASSPORT" }, { "start": 34, "end": 53, "label": "CARD" }, { "start": 101, "end": 121, "label": "EMAIL" }, { "start": 138, "end": 170, "label": "ADDRESS" } ]
d32badbb3497
[ { "start": 17, "end": 23, "label": "PERSON_NAME" }, { "start": 38, "end": 74, "label": "IBAN" }, { "start": 125, "end": 129, "label": "PERSON_NAME" }, { "start": 202, "end": 209, "label": "PERSON_NAME" }, { "start": 230, "end": 240, "label"...
7cd1e2972edb
[]
7ac6885ac9d9
[ { "start": 65, "end": 76, "label": "NATIONAL_ID" }, { "start": 86, "end": 103, "label": "PERSON_NAME" }, { "start": 170, "end": 188, "label": "NATIONAL_ID" }, { "start": 198, "end": 210, "label": "PERSON_NAME" }, { "start": 259, "end": 279, ...
4b54099e8d69
[ { "start": 24, "end": 42, "label": "NATIONAL_ID" }, { "start": 72, "end": 81, "label": "PERSON_NAME" }, { "start": 91, "end": 102, "label": "PERSON_NAME" } ]
47478d5cd874
[ { "start": 16, "end": 20, "label": "PERSON_NAME" }, { "start": 26, "end": 39, "label": "PERSON_NAME" }, { "start": 46, "end": 60, "label": "PHONE" }, { "start": 92, "end": 96, "label": "PERSON_NAME" }, { "start": 124, "end": 152, "label": "...
44b0c3b98be1
[ { "start": 44, "end": 64, "label": "EMAIL" }, { "start": 88, "end": 98, "label": "NATIONAL_ID" }, { "start": 125, "end": 133, "label": "PERSON_NAME" } ]
a02370d77b84
[ { "start": 48, "end": 57, "label": "PASSPORT" } ]
9dc22e328d0e
[ { "start": 21, "end": 31, "label": "DATE_OF_BIRTH" } ]
49fd01ee46f6
[ { "start": 19, "end": 29, "label": "DATE_OF_BIRTH" }, { "start": 45, "end": 59, "label": "IP_ADDRESS" }, { "start": 74, "end": 93, "label": "CARD" }, { "start": 115, "end": 123, "label": "PASSPORT" } ]
a9b85ca07fde
[]
ad187fee1ae2
[ { "start": 26, "end": 39, "label": "PERSON_NAME" }, { "start": 69, "end": 99, "label": "IBAN" }, { "start": 106, "end": 120, "label": "PERSON_NAME" } ]
5985d2cbabd2
[ { "start": 46, "end": 70, "label": "EMAIL" }, { "start": 74, "end": 88, "label": "IP_ADDRESS" }, { "start": 135, "end": 159, "label": "EMAIL" }, { "start": 163, "end": 177, "label": "IP_ADDRESS" }, { "start": 243, "end": 257, "label": "NATI...
698ec3a97afb
[ { "start": 24, "end": 42, "label": "NATIONAL_ID" } ]
d4a9426fbeb9
[ { "start": 38, "end": 74, "label": "IBAN" }, { "start": 104, "end": 108, "label": "PERSON_NAME" } ]
0d8ba0005ee9
[]
6749115a6c23
[ { "start": 24, "end": 37, "label": "PERSON_NAME" }, { "start": 48, "end": 61, "label": "PHONE" } ]
b04f82063999
[ { "start": 17, "end": 51, "label": "ADDRESS" }, { "start": 65, "end": 80, "label": "PHONE" }, { "start": 96, "end": 111, "label": "PERSON_NAME" } ]
5758d0b68217
[ { "start": 20, "end": 38, "label": "NATIONAL_ID" } ]
c3e254ce8a31
[ { "start": 13, "end": 29, "label": "CARD" } ]
e19ab64e5dc3
[ { "start": 46, "end": 66, "label": "EMAIL" }, { "start": 70, "end": 84, "label": "IP_ADDRESS" }, { "start": 150, "end": 168, "label": "NATIONAL_ID" }, { "start": 178, "end": 192, "label": "PERSON_NAME" }, { "start": 240, "end": 259, "label"...
336110592980
[]
1e1df678443a
[ { "start": 38, "end": 50, "label": "IP_ADDRESS" }, { "start": 71, "end": 80, "label": "PERSON_NAME" } ]
2d2e82b9ef8b
[ { "start": 6, "end": 22, "label": "CARD" } ]
a44e71f4566a
[ { "start": 17, "end": 49, "label": "ADDRESS" }, { "start": 68, "end": 76, "label": "PASSPORT" } ]
8cb807bf05b4
[ { "start": 46, "end": 84, "label": "ADDRESS" } ]
f1ae23f73327
[ { "start": 17, "end": 46, "label": "IBAN" } ]
0c00a9c464f5
[ { "start": 17, "end": 21, "label": "PERSON_NAME" }, { "start": 50, "end": 69, "label": "CARD" }, { "start": 128, "end": 158, "label": "IBAN" }, { "start": 189, "end": 193, "label": "PERSON_NAME" }, { "start": 222, "end": 232, "label": "PHON...
ebe1fd45a40d
[ { "start": 45, "end": 64, "label": "EMAIL" }, { "start": 68, "end": 82, "label": "IP_ADDRESS" }, { "start": 129, "end": 154, "label": "EMAIL" }, { "start": 158, "end": 172, "label": "IP_ADDRESS" } ]
d5c221b61c0a
[]
4403b4f7adcb
[ { "start": 65, "end": 77, "label": "NATIONAL_ID" }, { "start": 87, "end": 98, "label": "PERSON_NAME" }, { "start": 165, "end": 177, "label": "NATIONAL_ID" }, { "start": 187, "end": 199, "label": "PERSON_NAME" }, { "start": 266, "end": 276, ...
ce23436d1a4a
[]
a365370abe58
[ { "start": 60, "end": 95, "label": "ADDRESS" } ]
4d8414003582
[ { "start": 17, "end": 20, "label": "PERSON_NAME" }, { "start": 32, "end": 48, "label": "PHONE" }, { "start": 75, "end": 78, "label": "PERSON_NAME" }, { "start": 103, "end": 117, "label": "NATIONAL_ID" }, { "start": 123, "end": 139, "label":...
77025d040f00
[]
dfd6d0a2648b
[ { "start": 18, "end": 23, "label": "PERSON_NAME" }, { "start": 40, "end": 88, "label": "ADDRESS" }, { "start": 129, "end": 138, "label": "PASSPORT" }, { "start": 168, "end": 173, "label": "PERSON_NAME" }, { "start": 202, "end": 212, "label"...
9a173181a9e2
[ { "start": 29, "end": 46, "label": "PERSON_NAME" }, { "start": 71, "end": 90, "label": "CARD" } ]
c09c34f95152
[ { "start": 25, "end": 34, "label": "PERSON_NAME" }, { "start": 62, "end": 76, "label": "NATIONAL_ID" }, { "start": 88, "end": 106, "label": "EMAIL" } ]
a578b83fdcbe
[ { "start": 64, "end": 72, "label": "NATIONAL_ID" }, { "start": 82, "end": 95, "label": "PERSON_NAME" }, { "start": 162, "end": 169, "label": "NATIONAL_ID" }, { "start": 179, "end": 196, "label": "PERSON_NAME" }, { "start": 263, "end": 281, ...
cf6933158432
[ { "start": 20, "end": 34, "label": "NATIONAL_ID" } ]
1e98759432d8
[ { "start": 17, "end": 24, "label": "PERSON_NAME" }, { "start": 45, "end": 55, "label": "NATIONAL_ID" }, { "start": 61, "end": 76, "label": "PERSON_NAME" } ]
3d43627e1d8d
[]
51959ed8fc1d
[]
dfb625051b90
[ { "start": 31, "end": 39, "label": "PASSPORT" }, { "start": 106, "end": 109, "label": "PERSON_NAME" } ]
a208db30cd9a
[ { "start": 23, "end": 33, "label": "DATE_OF_BIRTH" }, { "start": 67, "end": 81, "label": "PERSON_NAME" } ]
44848d603a90
[ { "start": 13, "end": 37, "label": "IBAN" } ]
ab7d1cb7236f
[ { "start": 45, "end": 55, "label": "DATE_OF_BIRTH" }, { "start": 67, "end": 71, "label": "PERSON_NAME" } ]
eb946c90135b
[ { "start": 19, "end": 22, "label": "PERSON_NAME" }, { "start": 47, "end": 61, "label": "NATIONAL_ID" }, { "start": 67, "end": 77, "label": "PERSON_NAME" } ]
a80938daf8b0
[ { "start": 45, "end": 62, "label": "EMAIL" }, { "start": 66, "end": 78, "label": "IP_ADDRESS" }, { "start": 143, "end": 161, "label": "NATIONAL_ID" }, { "start": 171, "end": 182, "label": "PERSON_NAME" }, { "start": 231, "end": 253, "label"...
0727fe3e6316
[ { "start": 32, "end": 42, "label": "PERSON_NAME" }, { "start": 73, "end": 91, "label": "NATIONAL_ID" } ]
9bd5add362d5
[]
35abf65e3da8
[ { "start": 65, "end": 75, "label": "NATIONAL_ID" }, { "start": 85, "end": 96, "label": "PERSON_NAME" }, { "start": 163, "end": 178, "label": "NATIONAL_ID" }, { "start": 188, "end": 203, "label": "PERSON_NAME" }, { "start": 270, "end": 285, ...
99c1f860ff5d
[ { "start": 26, "end": 63, "label": "IBAN" } ]
bac57129f2cb
[ { "start": 0, "end": 4, "label": "PERSON_NAME" }, { "start": 60, "end": 78, "label": "NATIONAL_ID" } ]
5b7ef835ccee
[ { "start": 6, "end": 14, "label": "PASSPORT" } ]
1361c92cac2c
[ { "start": 24, "end": 37, "label": "PHONE" } ]
7ce516c1f1c7
[]
79668d885ddd
[ { "start": 50, "end": 60, "label": "PHONE" }, { "start": 115, "end": 133, "label": "NATIONAL_ID" } ]
a092eeaa84fa
[ { "start": 24, "end": 37, "label": "PHONE" } ]
4484391bdaf4
[ { "start": 17, "end": 21, "label": "PERSON_NAME" }, { "start": 29, "end": 40, "label": "PERSON_NAME" }, { "start": 49, "end": 59, "label": "DATE_OF_BIRTH" } ]
824453d9072d
[ { "start": 15, "end": 25, "label": "DATE_OF_BIRTH" }, { "start": 40, "end": 50, "label": "PERSON_NAME" }, { "start": 60, "end": 82, "label": "EMAIL" } ]
fd28d0edde62
[ { "start": 34, "end": 46, "label": "NATIONAL_ID" } ]
9924692c0480
[]
487e8f148a8e
[ { "start": 17, "end": 20, "label": "PERSON_NAME" }, { "start": 61, "end": 90, "label": "IBAN" }, { "start": 108, "end": 117, "label": "PERSON_NAME" }, { "start": 158, "end": 194, "label": "IBAN" }, { "start": 212, "end": 216, "label": "PERS...
a103e16a7414
[]
4d6a3de1b273
[ { "start": 46, "end": 64, "label": "EMAIL" }, { "start": 68, "end": 83, "label": "IP_ADDRESS" } ]
359df0e8bd52
[]
86a942b11578
[ { "start": 91, "end": 122, "label": "EMAIL" }, { "start": 139, "end": 143, "label": "PERSON_NAME" } ]
0dadc80122f0
[ { "start": 51, "end": 60, "label": "PASSPORT" }, { "start": 85, "end": 88, "label": "PERSON_NAME" } ]
252af2d0c845
[ { "start": 73, "end": 83, "label": "NATIONAL_ID" }, { "start": 95, "end": 99, "label": "PERSON_NAME" } ]
f7696d75074d
[]
471a6425418d
[ { "start": 46, "end": 69, "label": "EMAIL" }, { "start": 73, "end": 85, "label": "IP_ADDRESS" }, { "start": 151, "end": 169, "label": "NATIONAL_ID" }, { "start": 179, "end": 189, "label": "PERSON_NAME" }, { "start": 237, "end": 263, "label"...
2bc648c9c219
[ { "start": 48, "end": 62, "label": "NATIONAL_ID" }, { "start": 68, "end": 79, "label": "PERSON_NAME" }, { "start": 92, "end": 96, "label": "PERSON_NAME" } ]
3c0630d94872
[ { "start": 43, "end": 64, "label": "EMAIL" }, { "start": 81, "end": 85, "label": "PERSON_NAME" } ]
eab2356ca609
[ { "start": 29, "end": 41, "label": "PERSON_NAME" }, { "start": 100, "end": 116, "label": "CARD" } ]
981cdfa5c8f2
[ { "start": 4, "end": 12, "label": "PERSON_NAME" }, { "start": 18, "end": 31, "label": "PHONE" }, { "start": 64, "end": 74, "label": "NATIONAL_ID" } ]
fa5772401d91
[ { "start": 16, "end": 25, "label": "PASSPORT" } ]
ab7c972800aa
[ { "start": 64, "end": 74, "label": "NATIONAL_ID" }, { "start": 84, "end": 99, "label": "PERSON_NAME" } ]
ecc875f89f54
[ { "start": 72, "end": 120, "label": "ADDRESS" } ]
c602ddf71907
[ { "start": 64, "end": 75, "label": "NATIONAL_ID" }, { "start": 85, "end": 93, "label": "PERSON_NAME" }, { "start": 142, "end": 159, "label": "EMAIL" }, { "start": 163, "end": 177, "label": "IP_ADDRESS" }, { "start": 223, "end": 241, "label"...
16aa92f5ed10
[ { "start": 17, "end": 20, "label": "PERSON_NAME" }, { "start": 39, "end": 75, "label": "IBAN" }, { "start": 126, "end": 155, "label": "IBAN" } ]
8dfa99d1db4c
[ { "start": 29, "end": 44, "label": "PERSON_NAME" }, { "start": 54, "end": 64, "label": "PHONE" } ]
b55e7f66791a
[ { "start": 51, "end": 63, "label": "NATIONAL_ID" }, { "start": 69, "end": 84, "label": "PERSON_NAME" } ]
ece9ba6e21f1
[ { "start": 23, "end": 37, "label": "NATIONAL_ID" }, { "start": 43, "end": 60, "label": "PERSON_NAME" }, { "start": 95, "end": 120, "label": "EMAIL" } ]
7458f600845e
[]
f90a26fb8d49
[ { "start": 6, "end": 35, "label": "IBAN" } ]
0b2cbd63c79e
[ { "start": 4, "end": 15, "label": "PERSON_NAME" }, { "start": 21, "end": 33, "label": "PHONE" } ]
e063def36946
[ { "start": 64, "end": 79, "label": "NATIONAL_ID" }, { "start": 89, "end": 102, "label": "PERSON_NAME" }, { "start": 151, "end": 180, "label": "EMAIL" }, { "start": 184, "end": 195, "label": "IP_ADDRESS" } ]
617dd84cd521
[ { "start": 17, "end": 21, "label": "PERSON_NAME" }, { "start": 30, "end": 39, "label": "PERSON_NAME" }, { "start": 60, "end": 81, "label": "EMAIL" }, { "start": 109, "end": 113, "label": "PERSON_NAME" }, { "start": 121, "end": 135, "label":...
842bf7e92fe9
[ { "start": 45, "end": 62, "label": "EMAIL" }, { "start": 66, "end": 81, "label": "IP_ADDRESS" } ]
3474b4046861
[ { "start": 17, "end": 47, "label": "IBAN" } ]
7e3f9d1c2830
[ { "start": 15, "end": 29, "label": "DATE_OF_BIRTH" }, { "start": 35, "end": 46, "label": "IP_ADDRESS" }, { "start": 64, "end": 73, "label": "PASSPORT" } ]
264320637cc4
[ { "start": 27, "end": 46, "label": "CARD" } ]
End of preview.

decima-pii test pack

500 labelled messages (100 each in Arabic, Persian, Urdu, Turkish and English) for checking that an integration of decima-pii reproduces the reference output of each released version. All text and values are synthetic: the names, phone numbers, IDs, IBANs and card numbers are generated in their real formats and belong to no one. The rows are a fixed random sample (seed 2026) of decima-pii's own held-out test set.

This is a reproduction check, not an accuracy claim: the model was tuned with this test set's error categories in mind, so scores here are near-perfect. For accuracy on text the model never saw, see the outside results on the model card.

Files

file what sha256
testpack.jsonl {id, lang, variety, digits_script, text, spans: [{start, end, label}], negatives} (character offsets; negatives are look-alike values that are not personal data) c31cd4e32538315c15b5ed8e9f18c9a4a1a20e4f2fc2244a8719320961f0afea
expected-decima-pii-v0.2-nbits8.jsonl the reference output {id, spans} of decima-pii v0.2, onnx/model_nbits8.onnx, run through decima_pii.py with decoder="v2" (bridging, post-filter, windowing) a82508eb10084446a49f804ddb7befdd16633cd460a0cea4df268ca434d2bc1f
metrics-decima-pii-v0.2-nbits8.json v0.2's scores, per language and per label ab95f6231540e54c172995a73f4adc8918f9a8b90ad6d1978136feb414a1701d
expected-decima-pii-v0.1-nbits8.jsonl the reference output of decima-pii v0.1, same file and script with decoder="v1" 9fc789c42e1c31b264a76380a38c1b90b4d1bf0538a8d7fc266ba74f9d521b4a
metrics-decima-pii-v0.1-nbits8.json v0.1's scores, per language and per label 23ca52082da0999c3bf6e18a0537c10cda0f750b38006bf0f8c486b9d0ef8ac2

Pinned models

Pin the model by tag and match the decoder to it: v0.2 uses decoder="v2" (the default in decima_pii.py from v0.2 on), v0.1 uses decoder="v1". DecimaPII.from_pretrained("amyrmahdy/decima-pii", revision="v0.1") picks v1 for you. The two decoders differ in two small ways: v2 keeps the period of a final initial in a name ("Anna S."), and counts number words toward the digit minimum of a phone or ID. In decima-ai 2.5.0 and newer, from decima import PII loads v0.2 with decoder v2.

amyrmahdy/decima-pii at tag v0.2 (tag 217922ef8870cc2be7cce9d784e922626e8d4289, commit 27a7a46bb6649faf931c23919aeb9c3259404aa4):

file sha256
onnx/model_nbits8.onnx (default) 0b09f47226c8a10c785013d6a7f897493b8bb2e9654ba1c8e938b4fccf559ea0
onnx/model_quantized.onnx (browser) 75d1ddc8cbb2470225aaeef92977ffabb0e6c7aecab5546c986ab36439257e6d
onnx/model.onnx (fp32) 7057b4ad69518131a075efdf4f83b122e6dc3f5fe0046ada685cdac53699f8ab
model.safetensors (PyTorch) 511c3cbdd3176794872664caed8eae2d52e83212b07fbdb72b409c4fe682f4f1
tokenizer.json 4379fe7f4a7af10b1ac15ffc8ae90f446f92082393699e9dd54b8c3b5ec0abad

amyrmahdy/decima-pii at tag v0.1 (tag a90c86f8f426d61895866024370fd96e30d37f4b, commit 9bce91b8030a0326fa84b9bc06033df3b6313fdd):

file sha256
onnx/model_nbits8.onnx (default) 473e6150c2f6bd9fe47a3164cf64c3dedccc1ec5697828c335c028f72ce98bf3
onnx/model_quantized.onnx (browser) 297109796d3880daa553f107eb727804ae4235ccc9d68b305c1471ceb15d4c24
onnx/model.onnx (fp32) 6b49793bd411c6937de9b5a6efc8c9d60eda08c22b96e66245870fac36eee8cd
model.safetensors (PyTorch) 87b786892e84b29276cf84ea9073b4c30c8c05b380cc0ce62ae367bd2dc7b4b7
tokenizer.json 4379fe7f4a7af10b1ac15ffc8ae90f446f92082393699e9dd54b8c3b5ec0abad (same in both)

Reference scores (exact span and label)

v0.2 precision v0.2 recall v0.2 F1 v0.1 F1
all (1,124 spans) 0.995 0.996 0.996 0.996
Arabic 0.996 1.000 0.998 0.996
Persian 0.982 0.982 0.982 0.984
Urdu 1.000 1.000 1.000 1.000
Turkish 0.995 1.000 0.998 1.000
English 1.000 1.000 1.000 1.000

An integration reproduces a version when its spans equal that version's expected-...jsonl row for row (same offsets and labels). v0.2's output differs from v0.1's on 11 of the 500 rows. Note that the tokenizer file carries a 256-token truncation setting: disable it and tag long texts in overlapping windows, as decima_pii.py does.
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