Datasets:

abdelhaqueidali
/

Amazigh-Numbers-To-Words-Dataset

Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Failed to parse string: '1000000000000000000000' as a scalar of type int64
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 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 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 1161, 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
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Failed to parse string: '1000000000000000000000' as a scalar of type int64
              
              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 1880, 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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Number int64	Ten System Abrv string	Ten System Ext string	Twenty System string
0	ⴰⵎⵢⴰ	ⴰⵎⵢⴰ	ⴰⵎⵢⴰ
1	ⵢⴰⵏ	ⵢⴰⵏ	ⵢⴰⵏ
2	ⵙⵉⵏ	ⵙⵉⵏ	ⵙⵉⵏ
3	ⴽⵕⴰⴹ	ⴽⵕⴰⴹ	ⴽⵕⴰⴹ
4	ⴽⴽⵓⵥ	ⴽⴽⵓⵥ	ⴽⴽⵓⵥ
5	ⵙⵎⵎⵓⵙ	ⵙⵎⵎⵓⵙ	ⵙⵎⵎⵓⵙ
6	ⵚⴹⵉⵚ	ⵚⴹⵉⵚ	ⵚⴹⵉⵚ
7	ⵙⴰ	ⵙⴰ	ⵙⴰ
8	ⵜⴰⵎ	ⵜⴰⵎ	ⵜⴰⵎ
9	ⵜⵥⴰ	ⵜⵥⴰ	ⵜⵥⴰ
10	ⵎⵔⴰⵡ	ⵎⵔⴰⵡ	ⵎⵔⴰⵡ
11	ⵢⴰⵏ ⴷ ⵎⵔⴰⵡ	ⵢⴰⵏ ⴷ ⵎⵔⴰⵡ	ⵢⴰⵏ ⴷ ⵎⵔⴰⵡ
12	ⵙⵉⵏ ⴷ ⵎⵔⴰⵡ	ⵙⵉⵏ ⴷ ⵎⵔⴰⵡ	ⵙⵉⵏ ⴷ ⵎⵔⴰⵡ
13	ⴽⵕⴰⴹ ⴷ ⵎⵔⴰⵡ	ⴽⵕⴰⴹ ⴷ ⵎⵔⴰⵡ	ⴽⵕⴰⴹ ⴷ ⵎⵔⴰⵡ
14	ⴽⴽⵓⵥ ⴷ ⵎⵔⴰⵡ	ⴽⴽⵓⵥ ⴷ ⵎⵔⴰⵡ	ⴽⴽⵓⵥ ⴷ ⵎⵔⴰⵡ
15	ⵙⵎⵎⵓⵙ ⴷ ⵎⵔⴰⵡ	ⵙⵎⵎⵓⵙ ⴷ ⵎⵔⴰⵡ	ⵙⵎⵎⵓⵙ ⴷ ⵎⵔⴰⵡ
16	ⵚⴹⵉⵚ ⴷ ⵎⵔⴰⵡ	ⵚⴹⵉⵚ ⴷ ⵎⵔⴰⵡ	ⵚⴹⵉⵚ ⴷ ⵎⵔⴰⵡ
17	ⵙⴰ ⴷ ⵎⵔⴰⵡ	ⵙⴰ ⴷ ⵎⵔⴰⵡ	ⵙⴰ ⴷ ⵎⵔⴰⵡ
18	ⵜⴰⵎ ⴷ ⵎⵔⴰⵡ	ⵜⴰⵎ ⴷ ⵎⵔⴰⵡ	ⵜⴰⵎ ⴷ ⵎⵔⴰⵡ
19	ⵜⵥⴰ ⴷ ⵎⵔⴰⵡ	ⵜⵥⴰ ⴷ ⵎⵔⴰⵡ	ⵜⵥⴰ ⴷ ⵎⵔⴰⵡ
20	ⵙⵉⵎⵔⴰⵡ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ	ⴰⴳⵏⴰⵔ
21	ⵙⵉⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⴰⴳⵏⴰⵔ ⴷ ⵢⴰⵏ
22	ⵙⵉⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⴰⴳⵏⴰⵔ ⴷ ⵙⵉⵏ
23	ⵙⵉⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⴰⴳⵏⴰⵔ ⴷ ⴽⵕⴰⴹ
24	ⵙⵉⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⴰⴳⵏⴰⵔ ⴷ ⴽⴽⵓⵥ
25	ⵙⵉⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⴰⴳⵏⴰⵔ ⴷ ⵙⵎⵎⵓⵙ
26	ⵙⵉⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⴰⴳⵏⴰⵔ ⴷ ⵚⴹⵉⵚ
27	ⵙⵉⵎⵔⴰⵡ ⴷ ⵙⴰ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⴰ	ⴰⴳⵏⴰⵔ ⴷ ⵙⴰ
28	ⵙⵉⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⴰⴳⵏⴰⵔ ⴷ ⵜⴰⵎ
29	ⵙⵉⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⴰⴳⵏⴰⵔ ⴷ ⵜⵥⴰ
30	ⴽⵕⴰⵎⵔⴰⵡ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ	ⴰⴳⵏⴰⵔ ⴷ ⵎⵔⴰⵡ
31	ⴽⵕⴰⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⴰⴳⵏⴰⵔ ⴷ ⵢⴰⵏ ⴷ ⵎⵔⴰⵡ
32	ⴽⵕⴰⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⴰⴳⵏⴰⵔ ⴷ ⵙⵉⵏ ⴷ ⵎⵔⴰⵡ
33	ⴽⵕⴰⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⴰⴳⵏⴰⵔ ⴷ ⴽⵕⴰⴹ ⴷ ⵎⵔⴰⵡ
34	ⴽⵕⴰⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⴰⴳⵏⴰⵔ ⴷ ⴽⴽⵓⵥ ⴷ ⵎⵔⴰⵡ
35	ⴽⵕⴰⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⴰⴳⵏⴰⵔ ⴷ ⵙⵎⵎⵓⵙ ⴷ ⵎⵔⴰⵡ
36	ⴽⵕⴰⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⴰⴳⵏⴰⵔ ⴷ ⵚⴹⵉⵚ ⴷ ⵎⵔⴰⵡ
37	ⴽⵕⴰⵎⵔⴰⵡ ⴷ ⵙⴰ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⴰ	ⴰⴳⵏⴰⵔ ⴷ ⵙⴰ ⴷ ⵎⵔⴰⵡ
38	ⴽⵕⴰⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⴰⴳⵏⴰⵔ ⴷ ⵜⴰⵎ ⴷ ⵎⵔⴰⵡ
39	ⴽⵕⴰⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⴽⵕⴰⴹ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⴰⴳⵏⴰⵔ ⴷ ⵜⵥⴰ ⴷ ⵎⵔⴰⵡ
40	ⴽⴽⵓⵎⵔⴰⵡ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ
41	ⴽⴽⵓⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵢⴰⵏ
42	ⴽⴽⵓⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵉⵏ
43	ⴽⴽⵓⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⵕⴰⴹ
44	ⴽⴽⵓⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⴽⵓⵥ
45	ⴽⴽⵓⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵎⵎⵓⵙ
46	ⴽⴽⵓⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵚⴹⵉⵚ
47	ⴽⴽⵓⵎⵔⴰⵡ ⴷ ⵙⴰ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⴰ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⴰ
48	ⴽⴽⵓⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⴰⵎ
49	ⴽⴽⵓⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⴽⴽⵓⵥ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⵥⴰ
50	ⵙⵎⵎⵓⵎⵔⴰⵡ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵎⵔⴰⵡ
51	ⵙⵎⵎⵓⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵢⴰⵏ ⴷ ⵎⵔⴰⵡ
52	ⵙⵎⵎⵓⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵉⵏ ⴷ ⵎⵔⴰⵡ
53	ⵙⵎⵎⵓⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⵕⴰⴹ ⴷ ⵎⵔⴰⵡ
54	ⵙⵎⵎⵓⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⴽⵓⵥ ⴷ ⵎⵔⴰⵡ
55	ⵙⵎⵎⵓⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵎⵎⵓⵙ ⴷ ⵎⵔⴰⵡ
56	ⵙⵎⵎⵓⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵚⴹⵉⵚ ⴷ ⵎⵔⴰⵡ
57	ⵙⵎⵎⵓⵎⵔⴰⵡ ⴷ ⵙⴰ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⴰ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⴰ ⴷ ⵎⵔⴰⵡ
58	ⵙⵎⵎⵓⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⴰⵎ ⴷ ⵎⵔⴰⵡ
59	ⵙⵎⵎⵓⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⵙⵎⵎⵓⵙ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⵙⵉⵏ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⵥⴰ ⴷ ⵎⵔⴰⵡ
60	ⵚⴹⵉⵎⵔⴰⵡ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ
61	ⵚⴹⵉⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵢⴰⵏ
62	ⵚⴹⵉⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵉⵏ
63	ⵚⴹⵉⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⵕⴰⴹ
64	ⵚⴹⵉⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⴽⵓⵥ
65	ⵚⴹⵉⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵎⵎⵓⵙ
66	ⵚⴹⵉⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵚⴹⵉⵚ
67	ⵚⴹⵉⵎⵔⴰⵡ ⴷ ⵙⴰ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⴰ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⴰ
68	ⵚⴹⵉⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⴰⵎ
69	ⵚⴹⵉⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⵚⴹⵉⵚ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⵥⴰ
70	ⵙⴰⵎⵔⴰⵡ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵎⵔⴰⵡ
71	ⵙⴰⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵢⴰⵏ ⴷ ⵎⵔⴰⵡ
72	ⵙⴰⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵉⵏ ⴷ ⵎⵔⴰⵡ
73	ⵙⴰⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⵕⴰⴹ ⴷ ⵎⵔⴰⵡ
74	ⵙⴰⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⴽⵓⵥ ⴷ ⵎⵔⴰⵡ
75	ⵙⴰⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵎⵎⵓⵙ ⴷ ⵎⵔⴰⵡ
76	ⵙⴰⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵚⴹⵉⵚ ⴷ ⵎⵔⴰⵡ
77	ⵙⴰⵎⵔⴰⵡ ⴷ ⵙⴰ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⴰ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⴰ ⴷ ⵎⵔⴰⵡ
78	ⵙⴰⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⴰⵎ ⴷ ⵎⵔⴰⵡ
79	ⵙⴰⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⵙⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⴽⵕⴰⴹ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⵥⴰ ⴷ ⵎⵔⴰⵡ
80	ⵜⴰⵎⵔⴰⵡ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ
81	ⵜⴰⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵢⴰⵏ
82	ⵜⴰⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵉⵏ
83	ⵜⴰⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⵕⴰⴹ
84	ⵜⴰⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⴽⵓⵥ
85	ⵜⴰⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵎⵎⵓⵙ
86	ⵜⴰⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵚⴹⵉⵚ
87	ⵜⴰⵎⵔⴰⵡ ⴷ ⵙⴰ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⴰ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⴰ
88	ⵜⴰⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⴰⵎ
89	ⵜⴰⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⵜⴰⵎ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⵥⴰ
90	ⵜⵥⴰⵎⵔⴰⵡ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵎⵔⴰⵡ
91	ⵜⵥⴰⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵢⴰⵏ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵢⴰⵏ ⴷ ⵎⵔⴰⵡ
92	ⵜⵥⴰⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵉⵏ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵉⵏ ⴷ ⵎⵔⴰⵡ
93	ⵜⵥⴰⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⵕⴰⴹ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⵕⴰⴹ ⴷ ⵎⵔⴰⵡ
94	ⵜⵥⴰⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⴽⴽⵓⵥ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⴽⴽⵓⵥ ⴷ ⵎⵔⴰⵡ
95	ⵜⵥⴰⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⵎⵎⵓⵙ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⵎⵎⵓⵙ ⴷ ⵎⵔⴰⵡ
96	ⵜⵥⴰⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵚⴹⵉⵚ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵚⴹⵉⵚ ⴷ ⵎⵔⴰⵡ
97	ⵜⵥⴰⵎⵔⴰⵡ ⴷ ⵙⴰ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵙⴰ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵙⴰ ⴷ ⵎⵔⴰⵡ
98	ⵜⵥⴰⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⴰⵎ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⴰⵎ ⴷ ⵎⵔⴰⵡ
99	ⵜⵥⴰⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⵜⵥⴰ ⵉⴷ ⵎⵔⴰⵡ ⴷ ⵜⵥⴰ	ⴽⴽⵓⵥ ⵡⴰⴳⵏⴰⵔⵏ ⴷ ⵜⵥⴰ ⴷ ⵎⵔⴰⵡ

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Amazigh Numbers Dataset

Dataset Summary

This dataset maps integers to their Amazigh textual representations across three different numeral counting systems. It is ideal for NLP tasks, localization for the Amazigh language.

Dataset Structure

Data Fields

Number: The integer numerical value.
Ten System Abrv: Base-10 abbreviated representation - The current standard (e.g., 20 is ⵙⵉⵎⵔⴰⵡ).
Ten System Ext: Base-10 extended representation - The legacy standard (e.g., 20 is ⵙⵉⵏ ⵉⴷ ⵎⵔⴰⵡ).
Twenty System: Vigesimal (Base-20) representation (e.g., 20 is ⴰⴳⵏⴰⵔ and 30 is ⴰⴳⵏⴰⵔ ⴷ ⵎⵔⴰⵡ which says 20 and 10).

Splits

numbers_0_999: Numbers from 0 to 999.
numbers_1000_9999: Numbers from 1,000 to 9,999.
numbers_10000_99999: Numbers from 10,000 to 99,999.
numbers_millions_above: Large exact scales (Millions, Billions, Trillions, etc.).

To generate more numbers:

You find the space of the tool at: https://huggingface.co/spaces/abdelhaqueidali/Amazigh-Numbers-To-Words

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Total file size:

37.9 MB