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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<error_spans: list<item: struct<confidence: double, end: int64, label: string, start: int64>>, mt: string, sent_score: double, src: string>
to
{'attn_entropy_max': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'attn_entropy_mean': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_0': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_1': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_10': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_11': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_3': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_4': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_5': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_6': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_7': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_8': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_9': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_0': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_1': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_10': Sequence(feature=Value(dt
...
gprob_layer_2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_3': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_4': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_5': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_6': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_7': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_8': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_9': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_variation': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_rank': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logprob': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logprobs_entropy': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mcd_logprob_mean': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mcd_logprob_var': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mt': Value(dtype='string', id=None), 'mt_tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'src': Value(dtype='string', id=None)}
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in cast_table_to_schema
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2247, in <listcomp>
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<error_spans: list<item: struct<confidence: double, end: int64, label: string, start: int64>>, mt: string, sent_score: double, src: string>
to
{'attn_entropy_max': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'attn_entropy_mean': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_0': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_1': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_10': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_11': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_3': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_4': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_5': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_6': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_7': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_8': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_9': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_0': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_1': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_10': Sequence(feature=Value(dt
...
gprob_layer_2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_3': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_4': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_5': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_6': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_7': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_8': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_9': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_variation': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_rank': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logprob': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logprobs_entropy': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mcd_logprob_mean': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mcd_logprob_var': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mt': Value(dtype='string', id=None), 'mt_tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'src': Value(dtype='string', id=None)}
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 1436, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1053, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1898, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
data
dict |
|---|
{
"attn_entropy_max": [
2.339958190917968,
2.282905340194702,
2.343626737594604,
2.376308917999267,
2.450260162353515,
2.736898899078369,
2.5945565700531,
2.711625576019287,
2.669693946838379,
2.996870756149292,
3.097578763961792,
2.755706787109375,
2.778912305831909,
2.977061510086059,
3.246411561965942,
3.024683475494384,
3.047066926956176,
3.086295127868652,
3.199912071228027,
3.122039318084717,
2.9326891899108882,
3.246726274490356,
3.217886209487915,
3.363406658172607,
3.337556838989258,
3.264714241027832,
3.19309139251709,
3.272591590881347,
3.308735609054565,
3.387471914291382,
3.263206243515014,
3.520528316497802,
3.502490520477295,
3.19600248336792,
3.267713546752929,
3.341743230819702,
3.5184412002563468
],
"attn_entropy_mean": [
1.010926008224487,
1.45946729183197,
1.415699481964111,
1.526630043983459,
1.578728318214416,
1.544429779052734,
1.701514244079589,
1.6680899858474731,
1.386930346488952,
1.68763780593872,
1.679134368896484,
1.5527170896530151,
1.603982806205749,
1.77031135559082,
1.666499257087707,
1.482631683349609,
1.7163165807724,
1.7002893686294551,
1.71753978729248,
1.6435819864273071,
1.492635250091552,
1.33473265171051,
1.6876249313354492,
1.7102898359298702,
1.661299109458923,
1.717187404632568,
1.646026015281677,
1.526857495307922,
1.760711073875427,
1.757240176200866,
1.662917494773864,
1.694769978523254,
1.804497241973877,
1.623793959617614,
1.356486320495605,
1.742087721824646,
1.534439086914062
],
"blood_layer_0": [
2166.40576171875,
684.837158203125,
4858.05126953125,
953.2445068359375,
1258.1431884765625,
3985.82568359375,
3445.90380859375,
914.16552734375,
2586.020263671875,
2231.67529296875,
2707.839599609375,
1616.449462890625,
1310.491943359375,
1906.5972900390625,
1330.772216796875,
18150.734375,
1583.2042236328125,
4568.31494140625,
2884.68505859375,
2759.0078125,
1713.492919921875,
2109.4130859375,
1168.834228515625,
2451.841796875,
2121.25146484375,
4934.2802734375,
776.5548095703125,
4237.1181640625,
1137.897705078125,
3220.269287109375,
2494.23291015625,
25400.80078125,
839.02587890625,
2593.580322265625,
1271.7860107421875,
1224.887451171875,
1492.9158935546875
],
"blood_layer_1": [
560.3055419921875,
1229.2685546875,
860.63330078125,
486.18634033203125,
364.61260986328125,
727.0596923828125,
1228.388427734375,
1113.466552734375,
1776.0152587890625,
1478.500732421875,
683.8162841796875,
1339.148193359375,
947.7755126953125,
868.0977783203125,
1361.387451171875,
2029.986572265625,
609.6535034179688,
926.7008666992188,
674.7320556640625,
1800.294677734375,
1340.9610595703125,
339.9802551269531,
1973.7691650390625,
1294.15185546875,
662.141357421875,
1456.459228515625,
489.49591064453125,
1572.112548828125,
1625.7802734375,
1634.6300048828125,
646.4552612304688,
1164.1654052734375,
789.2089233398438,
268.08782958984375,
431.2235412597656,
822.7039184570312,
374.8334045410156
],
"blood_layer_10": [
618.1671752929688,
1245.9168701171875,
1063.1927490234375,
1076.531982421875,
778.947509765625,
1472.0732421875,
1716.998046875,
989.9464721679688,
1816.6630859375,
634.337890625,
1070.796630859375,
931.65673828125,
1358.7344970703125,
969.1505126953125,
823.4451904296875,
901.4571533203125,
877.8388671875,
1777.024658203125,
1145.152099609375,
1074.7449951171875,
945.9498291015625,
1032.595947265625,
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1195.1539306640625,
758.25537109375,
1124.4566650390625,
1165.985107421875,
542.4092407226562,
587.1814575195312,
981.7813720703125,
542.8219604492188,
996.1939697265625,
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1412.00244140625,
1302.4931640625
],
"blood_layer_11": [
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1.323329210281372,
2.894321918487549,
1.931014418601989,
2.702407836914062,
1.639834761619567,
3.9061660766601562,
2.148986339569092,
3.189066410064697,
0.930147051811218,
1.69289231300354,
2.055850028991699,
3.008103370666504,
3.218468427658081,
1.470008373260498,
2.724023580551147,
2.241512298583984,
1.8686010837554932,
3.567328214645385,
1.779799818992614,
2.614797353744507,
1.409435033798217,
2.055803775787353,
1.387682318687439,
3.02400255203247,
2.185380220413208,
1.645991086959838,
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2.039041042327881,
0.658346652984619,
1.616549491882324,
1.681051850318908,
2.153428077697754,
2.6488938331604
],
"blood_layer_2": [
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799.0650634765625,
1016.2744140625,
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1305.597412109375,
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436.63507080078125,
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669.2523193359375,
1638.106201171875,
1469.113037109375,
1861.7896728515625,
1866.591552734375,
995.6638793945312,
1029.8843994140625,
2138.822509765625
],
"blood_layer_3": [
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744.6094970703125,
680.7784423828125,
551.3319702148438,
492.59674072265625,
976.7203369140625,
459.56951904296875,
1763.730224609375,
644.6911010742188,
1479.075927734375,
719.143798828125,
779.0015869140625,
1748.3043212890625,
1610.1597900390625,
744.27734375,
1879.3671875,
1303.887939453125,
1015.6173706054688,
1100.3294677734375,
1584.59765625,
1715.6402587890625,
922.9893798828125,
1927.4150390625,
998.4228515625,
818.2647705078125,
457.11151123046875,
693.14892578125,
1323.8065185546875,
1510.1336669921875,
863.5205078125,
1351.4390869140625,
1304.162841796875,
781.5809326171875,
888.5745849609375,
978.5234375,
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265.5069885253906
],
"blood_layer_4": [
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789.6336669921875,
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617.5609130859375
],
"blood_layer_5": [
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753.3904418945312,
1607.5205078125,
1027.77783203125,
1252.6031494140625,
712.8707275390625,
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330.63031005859375,
1324.000244140625,
566.1597900390625,
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517.0081787109375,
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530.0752563476562,
1600.7608642578125,
1563.035400390625,
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571.3765258789062,
1194.826904296875,
1407.3934326171875,
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461.2076721191406,
840.3199462890625,
938.8132934570312,
527.4364013671875,
796.4165649414062,
471.7684631347656,
938.9859619140625
],
"blood_layer_6": [
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1891.850830078125,
715.8377075195312,
1342.7249755859375,
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1629.8221435546875,
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1293.452880859375,
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1434.815673828125,
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770.439453125,
591.564697265625,
1204.984130859375,
476.58123779296875,
846.2962036132812,
1081.9442138671875,
1012.9072265625,
1539.784912109375,
1097.9947509765625
],
"blood_layer_7": [
1222.1165771484375,
520.0565185546875,
1477.317626953125,
1403.39453125,
796.6602783203125,
327.1413879394531,
1976.69921875,
757.7989501953125,
635.2598266601562,
1432.718017578125,
829.6044921875,
1429.374755859375,
971.17626953125,
826.5090942382812,
1110.156005859375,
640.6336059570312,
1334.7525634765625,
729.8160400390625,
993.796875,
1001.02197265625,
1654.678955078125,
1283.964111328125,
649.0888061523438,
1059.7872314453125,
982.9771728515625,
268.3844299316406,
1451.67236328125,
1111.369140625,
497.6077575683594,
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ุฑ ููุชุฑุฉ ุฃุทูู, ููู
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ุณุงูุฑูู ุงูุฐูู ูุงู ูุฏููู
ุตุนูุจุฉ ูู ุงูุชููู ู
ุน ุงูุซูุงูุฉ ุงูุฌุฏูุฏุฉ ุงุญูุงูุง ูุฏููู
ุตุนูุจุฉ ุฎุงุตุฉ ูู ุงูุชููู ู
ุน ุซูุงูุชูู
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"src": "Travellers who had an easy time adjusting to the new culture sometimes have a particularly hard time readjusting to their native culture."
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ุง ุชุนูุฏ ุฅูู ุงููุทู ุจุนุฏ ุงูุนูุด ูู ุงูุฎุงุฑุฌุ ุชููู ู
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ู ุซูุงูุฉ ู
ูุฒูู.",
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"src": "When returning home after living abroad, you've adapted to the new culture and lost some of your habits from your home culture."
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ุง ุฐูุจุช ุฅูู ุงูุฎุงุฑุฌ ูู ุงูุจุฏุงูุฉุ ูุงู ุงููุงุณ ุนูู ุงูุฃุฑุฌุญ ุตุจูุฑูู ูููู
ููุ ูุนุฑููู ุฃู ุงูู
ุณุงูุฑูู ูู ุจูุฏ ุฌุฏูุฏ ูุฌุจ ุฃู ูุชููููุง.",
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"src": "When you went abroad at first, people were probably patient and understanding, knowing that travellers in a new country need to adapt."
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],
"mcd_logprob_var": [
0.102747350931167,
0.023079358041286,
0.0014600907452400002,
0.014703709632158002,
0.000897007994353,
0.0028715960215770003,
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0.024370569735765003,
0.017033975571393002,
0.102450221776962,
0.10565410554409001,
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0.000618724909145,
0.00002924697218986693,
0.030574014410376004,
0.00041007864638200003,
0.014452585019171,
0.038635730743408,
0.0006561725749630001,
0.00001203113606607075
],
"mt": "ูุฏ ูุง ูุชููุน ุงููุงุณ ุฃู ุงูุตุจุฑ ูุงูููู
ูู
ุฃูุถุง ุถุฑูุฑููู ููู
ุณุงูุฑูู ุนุงุฆุฏูู ุฅูู ุงููุทู.",
"mt_tokens": [
"ar_AR",
"โูุฏ",
"โูุง",
"โู",
"ุชููุน",
"โุงููุงุณ",
"โุฃู",
"โุงูุต",
"ุจุฑ",
"โูุงู",
"ููู
",
"โูู
",
"โุฃูุถุง",
"โุถุฑูุฑู",
"ูู",
"โููู
",
"ุณุงูุฑ",
"ูู",
"โุนุงุฆุฏ",
"ูู",
"โุฅูู",
"โุงููุทู",
".",
"</s>"
],
"src": "People may not anticipate that patience and understanding are also necessary for travellers returning home."
}
|
{"attn_entropy_max":[2.382016897201538,2.302021980285644,2.330931186676025,2.494935512542724,2.21885(...TRUNCATED)
|
{"attn_entropy_max":[2.302583694458008,2.19846773147583,2.354688167572021,2.50736665725708,2.3666181(...TRUNCATED)
|
{"attn_entropy_max":[2.388372182846069,2.358000755310058,2.361966133117676,2.461437225341797,2.66208(...TRUNCATED)
|
{"attn_entropy_max":[2.703143119812011,2.6171882152557373,2.477118492126465,2.731252193450927,2.6596(...TRUNCATED)
|
{"attn_entropy_max":[2.365798473358154,2.435630321502685,2.420302152633667,2.598120212554931,2.42446(...TRUNCATED)
|
End of preview.
This repository contains the data for the paper Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement.
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