sentence-transformers/msmarco

MS MARCO Training Dataset

This dataset consists of 4 separate datasets, each using the MS MARCO Queries and passages:

• triplets: This subset contains triplets of query-id, positive-id, negative-id as provided in qidpidtriples.train.full.2.tsv.gz from the MS MARCO Website. The only change is that this dataset has been reshuffled. This dataset can easily be used with an MultipleNegativesRankingLoss a.k.a. InfoNCE loss.
• labeled-list: This subset contains triplets of query-id, doc-ids, labels, i.e. every query is matched with every document from the triplets subset, with the labels column containing a list denoting which doc_ids represent positives and which ones represent negatives.
• bert-ensemble-mse: This subset contains tuples with a score. This score is from the BERT_CAT Ensemble from Hofstätter et al. 2020, and can easily be used with a MLELoss to train an embedding or reranker model via distillation.
• bert-ensemble-margin-mse: This subset contains triplets with a score, such that the score is ensemble_score(query, positive) - ensemble_score(query, negative), also from the BERT_CAT Ensemble from Hofstätter et al. 2020. It can easily be used with a MarginMLELoss to train an embedding or reranker model via distillation.

For all 3 datasets, the id's can be converted using the queries and corpus subsets to real texts.

Dataset Subsets

corpus subset

• Columns: "passage_id", "passage"
• Column types: str, str
• Examples:

  {
    "passage_id": "0",
    "passage": "The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated.",
  }
  

• Collection strategy: Reading collection.tar.gz from MS MARCO.

queries subset

• Columns: "query_id", "query"
• Column types: str, str
• Examples:

  {
    "query_id": "121352",
    "query": "define extreme",
  }
  

• Collection strategy: Reading queries.tar.gz from MS MARCO.

triplets subset

• Columns: "query_id", "positive_id", "negative_id"
• Column types: str, str, str
• Examples:

  {
    "query_id": "395861",
    "positive_id": "1185464",
    "negative_id": "6162229",
  }
  

• Collection strategy: Reading qidpidtriples.train.full.2.tsv.gz from MS MARCO and shuffling the dataset rows.

labeled-list subset

• Columns: "query_id", "doc_ids", "labels"
• Column types: str, List[str], List[int]
• Examples:

  {
    'query_id': '100',
    'doc_ids': ['3837260', '7854412', '4778006', '7929416', '5833477', '2715823', '903728', '1418399', '2544108', '4592808', '3565885', '260356', '5885724', '2976754', '3530456', '903722', '5136237', '6166367', '5372728', '6166373', '1615726', '5909725', '3278290', '570067', '2628703', '3619930', '3282101', '570061', '1442855', '5293099', '3976606', '3542912', '4358422', '4729309', '3542156', '102825', '2141701', '5885727', '1007725', '5137341', '180070', '2107140', '4942724', '3915139', '7417638', '7426645', '393085', '3129231', '4905980', '3181468', '7218730', '7159323', '5071423', '1609775', '3476284', '2876976', '6064616', '2752167', '5833480', '5451115', '6052155', '6551293', '2710795', '3231730', '1111340', '7885924', '2822828', '3034062', '3515232', '987726', '3129232', '4066994', '3680517', '6560480', '4584385', '5786855', '6117953', '8788361', '1960434', '212333', '7596616', '8433601', '3070543', '3282099', '5559299', '4070401', '5728025', '4584386', '8614523', '7452451', '3059713', '6401629', '6226845', '2710798', '458688', '207737', '5947749', '1615249', '5054795', '6646948', '4222935', '570068', '5860279', '8411096', '2882722', '3660786', '4711085', '4895219', '4929884', '5615159', '6845998', '1460725', '4433443', '5833479', '3542152', '2565332', '6311315', '4021935', '2616000', '7274494', '5241413', '6259470', '1488609', '187116', '8269268', '2399643', '2711733', '987725', '8788355', '7162594', '1164463', '5546714', '180065', '8467768', '4732466', '63493', '2595189', '3314126', '7426649', '856238', '7266280', '7745447', '7900007', '5035510', '373356', '2615996', '987731', '2904166', '4021933', '8149937', '5786854', '3542915', '1922026', '2743264', '4021930', '2821183', '3359767', '2686007', '6241706', '2544107', '2565334', '3070862', '5673424', '1868516', '879518', '2710801', '2878133', '5506342', '7279044', '260357', '1418394', '4198047', '8811927', '6447579', '3187998', '8489919', '2876695', '4641223', '5095750', '1366894', '5343128', '4167730', '4041435', '5676056', '6979590', '8763883', '5915554', '5060317', '8214795', '4932622', '4147294', '6546696', '3909088', '3397816', '4592804', '2268176', '328471', '6695311', '4090950', '2605356', '442753', '2978405', '890707', '3712000', '7227702', '1753582', '3582358', '8091295', '2601271', '3417484', '3450889', '3381536', '8788358', '4869670', '2969334', '8584693', '3026231', '4616200', '4967138', '1668186', '4346365', '4040376', '4655172', '6659144', '3241644', '4337017', '6733817', '8488585', '2701398', '987728', '7021467', '4879063', '5449524', '4043058', '7876390', '3708326', '3202726', '6267835', '7452454', '4111901', '4584380', '2898746', '1770226', '5786858', '2904167', '3767056', '3837262', '8696128', '8714806', '5974586', '4770734', '8614528', '6715004', '5559298', '5522820', '4494346', '4802607', '3505959', '4943876', '5762512', '7900010', '7614375', '641869', '611056', '1620088', '7044504', '5903693', '6470341', '5885731', '2411293', '1729708', '2723955', '4684463', '7632692', '7300912', '570062', '5786857', '1729712', '2859721', '224598', '8049838', '8757368', '2553525', '4276769', '3476280', '5673427', '6196257', '3529315', '1042349', '1008571', '604128', '1274276', '6976077', '558781', '1417835', '8746383', '6534823', '2544102', '4892920', '5326560', '3529311', '8288714', '8410908', '7541381', '8276461', '443963', '1418786', '393082', '2876973', '2041868', '4684460', '2553455', '4294336', '1770227', '1396675', '4821482', '341684', '3317707', '7758155', '1680750', '978378', '4641573', '6447578', '3572351', '7074859', '6560473', '2059066', '7681590', '6241703', '1425182', '941495', '4898655', '2710799', '3694312', '2565339', '5886217', '6997080', '570064', '5697987', '4058317', '3059711', '5540787', '4914280', '413609', '8149940', '2828604', '903721', '4130056', '7126261', '4294342', '4357509', '2041870', '3537437', '1274279', '442759', '3574934', '7007240', '2828792', '4040360', '5504280', '7803953', '6668972', '1698637', '4639591', '782719', 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'5477181', '3841373', '599686', '1822109', '6016976', '7710638', '2969339', '317867', '3889980', '6202558', '1922028', '3068679', '6267107', '4778010', '1164456', '2949959', '3770664', '8830401', '5447370', '2654076', '3837264', '514105', '1623873', '2667055', '1706867', '7394057', '4914279', '6179258', '7241866', '7606221', '863939', '572998', '6307420', '3278288', '7636863', '1833550', '7300908', '6659143', '903724', '7274675', '2041874', '7274491', '6052159', '6064611', '5315118', '7452457', '1733790', '7085674', '5607469', '6715080', '7528693', '6151450', '6936572', '8291592', '6052154', '2628704', '5094428', '2544101', '5209142', '2448798', '4898658', '447958', '3907349', '3837266', '1729705', '2615998' '5777752', '4641228', '4767985', '1964628', '5559295', '8344779', '1481317', '570063', '5422544', '4793008', '2544105', '7288672', '4767982', '8342090', '7160653', '7300914', '1206739', '5425329', '8325658', '6052156', '6668963', '442755', '2240500', '4647422', '6878782', '4040434', '4358312', '8204234', '8512015', '63496', '7229500', '7182554', '6151448', '5974582', '7173877', '8092000', '7212835', '3199491', '393998', '8035146', '1396680', '2735697', '925501', '1922025', '5885730', '1729713', '2755336', '7138459', '570070', '4021934', '903727', '6268913', '4739388', '7241868', '4030464', '2951657', '5974587', '1332617', '3869948', '8705052', '7266276', '7900253', '1614143', '5422548', '1272014', '7861061', '1998585', '3359677', '3985679', '3359768', '519514', '3841374', '3120523', '4021936', '1729707', '1440281', '558784', '5432868', '3205742', '4632606', '5950757', '8419081', '1733791', '4684461', '8049837', '1586152', '4045410', '6447577', '5521525', '2335443', '3311387', '4045403', '1922020', '7488188', '3713391', '1425179', '2859933', '1941737', '3557852', '6361227', '7226652', '5596636', '6165753', '4021931', '8614521', '3542911', '3010907', '3059705', '545587', '5969529', '6560475', '4378414', '570065', '8426345', '7892254', '3059707', '1922024', '1440190', '8149931', '7197977', '2283513', '2978402', '1137959', '3542917', '1615721', '4294340', '3282103', '7976948', '5227466', '2411296', '3492171', '6560474', '977993', '1846450', '8101033', '4356464', '7887336', '36611', '7203435', '5564079', '224596', '7300911', '5886218', '6165751', '4462443', '4684459', '3417478', '2951654', '3186729', '3239802', '3708321', '7664369', '2107143', '6300526', '3289374', '3059710', '4147291', '4641226', '2274293', '5559300', '3770671', '8398154', '488886', '6241704', '2555898', '3488845', '3630018', '1677477', '4639587', '2601269', '1693298', '5424209', '7816672', '8788359', '3428649', '3231726', '2710794', '4329499', '8115452', '6259463', '6285580', '8605584', '3059709', '4270589', '2555856', '2424358', '4211101', '1922021', '4432101'],
    'labels': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
  }
  

• Collection strategy: Reading the triplets subset and grouping all triplets by query_id. The large majority of queries have exactly 1000 doc_ids, out of which often only 1 is labeled positive. Up to 7 documents are labeled positive per query in the entire subset.

bert-ensemble-mse subset

• Columns: "query_id", "passage_id", "score"
• Column types: str, str, float64
• Examples:

  {
    "query_id": "400296",
    "passage_id": "1540783",
    "score": 6.624662,
  }
  

• Collection strategy: Reading the BERT_CAT Ensemble scores from Hofstätter et al. 2020.

bert-ensemble-margin-mse subset

• Columns: "query_id", "positive_id", "negative_id", "score"
• Column types: str, str, str, float64
• Examples:

  {
    "query_id": "400296",
    "positive_id": "1540783",
    "negative_id": "3518497",
    "score": 4.028059,
  }
  

• Collection strategy: Reading the BERT_CAT Ensemble scores from Hofstätter et al. 2020 and computing score = pos_score - neg_score for each triplet.