antoinelouis/colbert-xm
Sentence Similarity • 0.9B • Updated • 26k • 70
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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<msmarco_neg: list<item: int64>>
to
{'bm25': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, 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 1802, 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 2122, 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<msmarco_neg: list<item: int64>>
to
{'bm25': Sequence(feature=Value(dtype='int64', id=None), length=-1, 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 1524, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, 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 2038, 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.
qid int64 | pos sequence | neg dict |
|---|---|---|
121,352 | [
2912794
] | {
"bm25": [
1702145,
792622,
7282917,
8807233,
1625767,
2504055,
3064358,
8093240,
3589604,
674182,
7423473,
3301799,
8709757,
1565364,
2547759,
4646379,
4668215,
3196873,
5669242,
4744967,
24481,
8703242,
7876505,
634741,... |
634,306 | [
1668221
] | {
"bm25": [
830424,
1985345,
1153265,
1638798,
2866545,
6587610,
1767082,
8079692,
8144573,
609473,
4771183,
2110665,
4587253,
7138532,
7051965,
1316681,
6958226,
7136857,
2489101,
1316680,
6949099,
379309,
445075,
8834207... |
920,825 | [
3285660
] | {
"bm25": [
3285654,
1096041,
3560295,
1096044,
3560296,
5235906,
1096048,
3560289,
3560298,
6865432,
4706745,
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2866117,
2866111,
1096040,
3560293,
3285661,
2866108,
1096043,
3560291,
2866114,
1096047,
639... |
510,633 | [
1879754
] | {
"bm25": [
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7299554,
8270101,
870946,
4221836,
5593642,
4221837,
5339298,
2746531,
387749,
6288512,
5537214,
1879757,
5799547,
4221838,
7526050,
7299555,
6621359,
7526044,
6051601,
5339301,
4620294,
152414,
848715... |
737,889 | [
189333
] | {
"bm25": [
189335,
8495994,
4695289,
8495990,
6063870,
189328,
7226696,
5721526,
8495991,
6063873,
2008980,
189331,
6348655,
6063874,
189334,
189332,
189329,
2607736,
6348657,
6348652,
2607735,
6348658,
4695281,
6348654,
... |
674,172 | [
1170310
] | {
"bm25": [
7012271,
8794120,
5761364,
53146,
7166083,
6866565,
5577350,
1419334,
7955290,
1865201,
1715616,
186953,
1277680,
7650436,
1662645,
8442564,
254238,
2999699,
7939950,
6951006,
2473326,
429854,
1662409,
1608001,... |
303,205 | [
6487240
] | {
"bm25": [
5387771,
5065084,
7367230,
223024,
6370775,
5988146,
5919748,
294189,
5065082,
7367232,
463644,
7367229,
6791323,
360464,
8500559,
294188,
5648138,
977095,
923506,
4286850,
3959438,
2259857,
3278765,
6487245,
... |
570,009 | [
299069
] | {
"bm25": [
4882996,
507607,
5454271,
5488970,
930834,
3730109,
8076617,
6199171,
1838792,
2887444,
718402,
2137823,
4710481,
5308115,
3051140,
650289,
5282615,
5047102,
8076624,
7468730,
6729826,
777761,
4311120,
5120643,... |
492,875 | [
1147449
] | {
"bm25": [
1147445,
4859158,
7980570,
3409840,
6889336,
1147,
8729356,
7498842,
4862122,
1844845,
2379735,
6545680,
8172204,
381302,
2379740,
6626503,
2512154,
5925882,
1447383,
5925884,
5401759,
1963776,
6626504,
3409847... |
54,528 | [
2984158
] | {
"bm25": [
2984165,
477342,
2878030,
2984159,
4984880,
518461,
3286587,
8463663,
402925,
7416534,
335116,
2842879,
313434,
3915909,
7416531,
1531101,
7361290,
4598936,
7857947,
7361296,
3286585,
869205,
1919932,
373602,
... |
738,368 | [
6967300
] | {
"bm25": [
6967308,
6967299,
8514152,
7136143,
6967302,
1558063,
3829373,
8498471,
5276177,
6164902,
7670427,
6164903,
7827702,
334213,
1051427,
152276,
8625563,
1558067,
989941,
6707801,
5000167,
4376865,
7008206,
713614... |
507,001 | [
556790
] | {
"bm25": [
556788,
1446974,
508221,
1446981,
7057926,
1446976,
5209983,
1689437,
5755444,
1622424,
1622423,
8165400,
5184093,
8151028,
556781,
753716,
8522402,
7635830,
362459,
5184087,
508225,
508223,
5753299,
6367113,
... |
466,926 | [
4681169
] | {
"bm25": [
6895033,
1015707,
3931616,
3784153,
3931615,
2496379,
8314393,
5314025,
3111782,
1015712,
1183363,
7364251,
4332202,
7774785,
7526456,
3349687,
615289,
3784150,
4681170,
5314021,
2948246,
1278120,
3931618,
4056... |
1,181,095 | [
2918389
] | {
"bm25": [
8030704,
2623534,
7948576,
3767350,
104462,
2918395,
2075477,
2918396,
432625,
3999273,
2623533,
1228399,
2055346,
4962852,
453050,
2055347,
8190839,
366162,
2589475,
5138480,
8120970,
2918394,
3971256,
2623535... |
224,811 | [
1734362
] | {
"bm25": [
3695126,
6084438,
1734366,
1269759,
6760363,
8539374,
4273243,
4267033,
4295764,
4095504,
4477182,
8816071,
2606928,
5351408,
1734360,
8539371,
4095502,
5945388,
6203112,
5648218,
3551302,
6203106,
4095505,
447... |
918,533 | [
38114,
5439788
] | {
"bm25": [
4111623,
547550,
4188295,
1071975,
3737930,
6715854,
3737929,
7954952,
2166744,
4566754,
8522013,
6907307,
8420304,
5948634,
8335610,
7364773,
8610572,
659442,
4145481,
4996132,
1873490,
5163710,
7863111,
28707... |
80,926 | [
5496866
] | {
"bm25": [
365218,
1176298,
1176304,
3910750,
6431053,
365215,
1176297,
6335570,
5159480,
1513326,
1299528,
2158112,
5011859,
820841,
5388857,
307264,
5554021,
1299527,
6150664,
7299282,
3045228,
3319643,
5973644,
2010045... |
906,071 | [
3463469
] | {
"bm25": [
1250939,
1130544,
237664,
7533880,
3357192,
6017951,
7189857,
3736111,
939617,
5903454,
6362876,
5161377,
2860110,
7400172,
2003472,
4717367,
1317341,
547949,
4996376,
2167258,
1958539,
1066757,
1354780,
605371... |
428,191 | [
2060878
] | {
"bm25": [
1014431,
2060875,
1014429,
1849458,
6320968,
714539,
2060880,
2060873,
5957540,
6928674,
2060877,
5957531,
1413371,
5957539,
7357437,
5957536,
714537,
5957534,
714540,
5482069,
7176667,
1014438,
5957535,
101443... |
790,457 | [
2268614
] | {
"bm25": [
8551395,
3940761,
2682544,
8532448,
7506296,
7184040,
3622834,
3391332,
5626984,
2243649,
6909684,
3346723,
5501265,
7331761,
7436202,
3025684,
2453594,
4752047,
8549100,
5626986,
2453591,
3785629,
7633952,
468... |
242,081 | [
2410544
] | {
"bm25": [
5572961,
6139254,
5687301,
5687304,
7118883,
385846,
5691107,
5704026,
5704027,
5680698,
4281217,
160835,
5786522,
8267176,
3651850,
1038159,
1669029,
4093600,
1991647,
529369,
5240803,
5655965,
6574420,
557296... |
1,006,418 | [
2575598
] | {
"bm25": [
7449270,
5977209,
7449263,
2575599,
3512461,
3512467,
4688151,
3175503,
73653,
7603511,
2641145,
480272,
5130039,
1513055,
6774788,
7603506,
2154746,
7233674,
6214410,
2575606,
3171986,
3175507,
588177,
588179,... |
843,270 | [
4721621
] | {
"bm25": [
5520385,
2299949,
6926657,
2299953,
1964069,
4112287,
7166758,
3024750,
8744861,
1148222,
5178720,
8649138,
2383205,
8129935,
5336959,
5453138,
4879642,
3293712,
8663794,
724600,
978380,
8579758,
1503879,
25744... |
551,231 | [
539300
] | {
"bm25": [
162839,
6415929,
8100951,
7372865,
182228,
1240424,
6099655,
6415933,
1695993,
4271985,
2193123,
2379969,
7016930,
8118833,
6073977,
8190772,
2005422,
8080883,
2946809,
671278,
5862824,
2193121,
842623,
1729675... |
166,621 | [
4922264
] | {
"bm25": [
4922265,
1490575,
4942944,
4418239,
4922266,
4358668,
4358671,
7770454,
7336628,
8732459,
2210725,
6919360,
4708327,
4708330,
8260926,
5731007,
4917514,
3394290,
4546937,
1733935,
7185286,
4546936,
1931343,
435... |
End of preview.
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Check out the documentation for more information.
MS MARCO Passages Hard Negatives
This repository contains raw datasets, all of which have also been formatted for easy training in the MS MARCO Mined Triplets collection. We recommend looking there first.
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using Bing search engine.
This dataset repository contains files that are helpful to train bi-encoder models e.g. using sentence-transformers.
Training Code
You can find here an example how these files can be used to train bi-encoders: SBERT.net - MS MARCO - MarginMSE
cross-encoder-ms-marco-MiniLM-L-6-v2-scores.pkl.gz
This is a pickled dictionary in the format: scores[qid][pid] -> cross_encoder_score
It contains 160 million cross-encoder scores for (query, paragraph) pairs using the cross-encoder/ms-marco-MiniLM-L-6-v2 model.
msmarco-hard-negatives.jsonl.gz
This is a jsonl file: Each line is a JSON object. It has the following format:
{"qid": 867436, "pos": [5238393], "neg": {"bm25": [...], ...}}
qid is the query-ID from MS MARCO, pos is a list with paragraph IDs for positive passages. neg is a dictionary where we mined hard negatives using different (mainly dense retrieval) systems.
It contains hard negatives mined from BM25 (using ElasticSearch) and the following dense models:
msmarco-distilbert-base-tas-b
msmarco-distilbert-base-v3
msmarco-MiniLM-L-6-v3
distilbert-margin_mse-cls-dot-v2
distilbert-margin_mse-cls-dot-v1
distilbert-margin_mse-mean-dot-v1
mpnet-margin_mse-mean-v1
co-condenser-margin_mse-cls-v1
distilbert-margin_mse-mnrl-mean-v1
distilbert-margin_mse-sym_mnrl-mean-v1
distilbert-margin_mse-sym_mnrl-mean-v2
co-condenser-margin_mse-sym_mnrl-mean-v1
From each system, 50 most similar paragraphs were mined for a given query.
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