splade-NeoBERT-RetroMAE-pretrain trained on LightOn MS MARCO (triplets)

This is a SPLADE Sparse Encoder model finetuned from drexalt/NeoBERT-RetroMAE-pretrain on the ms-marco-en-bge-gemma dataset using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Type: SPLADE Sparse Encoder
Base model: drexalt/NeoBERT-RetroMAE-pretrain
Maximum Sequence Length: 256 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product
Training Dataset:
- ms-marco-en-bge-gemma
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Sparse Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sparse Encoders on Hugging Face

Full Model Architecture

SparseEncoder(
  (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'NeoBERTLMHead'})
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("drexalt/splade-NeoBERT-msmarco-triplets-muon")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoSciFact, NanoMSMARCO, NanoTouche2020, NanoSCIDOCS and NanoNFCorpus
Evaluated with SparseInformationRetrievalEvaluator

Metric	NanoSciFact	NanoMSMARCO	NanoTouche2020	NanoSCIDOCS	NanoNFCorpus
dot_accuracy@1	0.6	0.46	0.6939	0.4	0.46
dot_accuracy@3	0.74	0.66	0.898	0.6	0.58
dot_accuracy@5	0.82	0.74	0.9388	0.7	0.62
dot_accuracy@10	0.86	0.86	0.9796	0.8	0.68
dot_precision@1	0.6	0.46	0.6939	0.4	0.46
dot_precision@3	0.26	0.22	0.6463	0.2933	0.38
dot_precision@5	0.176	0.148	0.6	0.24	0.324
dot_precision@10	0.094	0.086	0.5224	0.172	0.276
dot_recall@1	0.575	0.46	0.048	0.0837	0.0433
dot_recall@3	0.705	0.66	0.1277	0.1817	0.0946
dot_recall@5	0.795	0.74	0.2001	0.2457	0.1134
dot_recall@10	0.84	0.86	0.3355	0.3527	0.1395
dot_ndcg@10	0.7149	0.6432	0.5802	0.3361	0.3488
dot_mrr@10	0.684	0.5754	0.7995	0.5253	0.5304
dot_map@100	0.6735	0.5824	0.4046	0.253	0.1572
query_active_dims	72.84	25.32	19.8367	28.66	20.66
query_sparsity_ratio	0.9976	0.9992	0.9994	0.9991	0.9993
corpus_active_dims	197.2052	169.3692	159.209	212.2401	194.8554
corpus_sparsity_ratio	0.9935	0.9945	0.9948	0.993	0.9936
avg_flops	8.4018	1.709	2.9054	4.0499	2.3608

Sparse Nano BEIR

Dataset: NanoBEIR_mean

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "scifact",
        "msmarco",
        "touche2020",
        "scidocs",
        "nfcorpus"
    ]
}

Metric	Value
dot_accuracy@1	0.5228
dot_accuracy@3	0.6956
dot_accuracy@5	0.7638
dot_accuracy@10	0.8359
dot_precision@1	0.5228
dot_precision@3	0.3599
dot_precision@5	0.2976
dot_precision@10	0.2301
dot_recall@1	0.242
dot_recall@3	0.3538
dot_recall@5	0.4188
dot_recall@10	0.5055
dot_ndcg@10	0.5246
dot_mrr@10	0.6229
dot_map@100	0.4142
query_active_dims	33.5181
query_sparsity_ratio	0.9989
corpus_active_dims	178.9849
corpus_sparsity_ratio	0.9941
avg_flops	2.6045

Training Details

Training Dataset

ms-marco-en-bge-gemma

Dataset: ms-marco-en-bge-gemma at 1a1ffe7
Size: 630,000 training samples
Columns: query_id, document_ids, and scores

Approximate statistics based on the first 1000 samples:

	query_id	document_ids	scores
type	int	list	list
details	1717: ~0.10% 2559: ~0.10% 4516: ~0.10% 5453: ~0.10% 6970: ~0.10% 7460: ~0.10% 7565: ~0.10% 7830: ~0.10% 8194: ~0.10% 10323: ~0.10% 12091: ~0.10% 12574: ~0.10% 12734: ~0.10% 14980: ~0.10% 17038: ~0.10% 18983: ~0.10% 20235: ~0.10% 22465: ~0.10% 22835: ~0.10% 23733: ~0.10% 23954: ~0.10% 24203: ~0.10% 24387: ~0.10% 24665: ~0.10% 25634: ~0.10% 26002: ~0.10% 26540: ~0.10% 26646: ~0.10% 26667: ~0.10% 26927: ~0.10% 28082: ~0.10% 29042: ~0.10% 29506: ~0.10% 31861: ~0.10% 34696: ~0.10% 34813: ~0.10% 34828: ~0.10% 36140: ~0.10% 40587: ~0.10% 41259: ~0.10% 43292: ~0.10% 44019: ~0.10% 44392: ~0.10% 45059: ~0.10% 45996: ~0.10% 46414: ~0.10% 48113: ~0.10% 48196: ~0.10% 49460: ~0.10% 49676: ~0.10% 50523: ~0.10% 51035: ~0.10% 51238: ~0.10% 52406: ~0.10% 58277: ~0.10% 58610: ~0.10% 63482: ~0.10% 63910: ~0.10% 64628: ~0.10% 65352: ~0.10% 65783: ~0.10% 65823: ~0.10% 66069: ~0.10% 66296: ~0.10% 67034: ~0.10% 67328: ~0.10% 67714: ~0.10% 70988: ~0.10% 71460: ~0.10% 74099: ~0.10% 74797: ~0.10% 75434: ~0.10% 75911: ~0.10% 76521: ~0.10% 81392: ~0.10% 83268: ~0.10% 83753: ~0.10% 86826: ~0.10% 87095: ~0.10% 87171: ~0.10% 87759: ~0.10% 88920: ~0.10% 89772: ~0.10% 90055: ~0.10% 90292: ~0.10% 96038: ~0.10% 96983: ~0.10% 100204: ~0.10% 101660: ~0.10% 104318: ~0.10% 107059: ~0.10% 107372: ~0.10% 107470: ~0.10% 108418: ~0.10% 108464: ~0.10% 109583: ~0.10% 111387: ~0.10% 112160: ~0.10% 114114: ~0.10% 114216: ~0.10% 115480: ~0.10% 115976: ~0.10% 120637: ~0.10% 122741: ~0.10% 123024: ~0.10% 123071: ~0.10% 125810: ~0.10% 126369: ~0.10% 127793: ~0.10% 128407: ~0.10% 128545: ~0.10% 129917: ~0.10% 130181: ~0.10% 130771: ~0.10% 130891: ~0.10% 131810: ~0.10% 131975: ~0.10% 133482: ~0.10% 134498: ~0.10% 134685: ~0.10% 135628: ~0.10% 138675: ~0.10% 140491: ~0.10% 141451: ~0.10% 141486: ~0.10% 141942: ~0.10% 143523: ~0.10% 148169: ~0.10% 150490: ~0.10% 150986: ~0.10% 151253: ~0.10% 151870: ~0.10% 152951: ~0.10% 157457: ~0.10% 158103: ~0.10% 158725: ~0.10% 158952: ~0.10% 159201: ~0.10% 159462: ~0.10% 161112: ~0.10% 161996: ~0.10% 162669: ~0.10% 162898: ~0.10% 163201: ~0.10% 165614: ~0.10% 167167: ~0.10% 169027: ~0.10% 169037: ~0.10% 169417: ~0.10% 170856: ~0.10% 170960: ~0.10% 171395: ~0.10% 172158: ~0.10% 176786: ~0.10% 177931: ~0.10% 178010: ~0.10% 178681: ~0.10% 178829: ~0.10% 181564: ~0.10% 183694: ~0.10% 183897: ~0.10% 184132: ~0.10% 186247: ~0.10% 186372: ~0.10% 188336: ~0.10% 188833: ~0.10% 188840: ~0.10% 189084: ~0.10% 189504: ~0.10% 189998: ~0.10% 191093: ~0.10% 191178: ~0.10% 195381: ~0.10% 196379: ~0.10% 197524: ~0.10% 199027: ~0.10% 200003: ~0.10% 200074: ~0.10% 200640: ~0.10% 200715: ~0.10% 202019: ~0.10% 202917: ~0.10% 204733: ~0.10% 207182: ~0.10% 207263: ~0.10% 207390: ~0.10% 208531: ~0.10% 209119: ~0.10% 209498: ~0.10% 210396: ~0.10% 210646: ~0.10% 212391: ~0.10% 212913: ~0.10% 212983: ~0.10% 216011: ~0.10% 219184: ~0.10% 221171: ~0.10% 222287: ~0.10% 222473: ~0.10% 222614: ~0.10% 224756: ~0.10% 224950: ~0.10% 226310: ~0.10% 229816: ~0.10% 231247: ~0.10% 233896: ~0.10% 234813: ~0.10% 235383: ~0.10% 235475: ~0.10% 236436: ~0.10% 237398: ~0.10% 237486: ~0.10% 238352: ~0.10% 238432: ~0.10% 239059: ~0.10% 240439: ~0.10% 242982: ~0.10% 243774: ~0.10% 245317: ~0.10% 245838: ~0.10% 247749: ~0.10% 250171: ~0.10% 251682: ~0.10% 251974: ~0.10% 253372: ~0.10% 255994: ~0.10% 256208: ~0.10% 256348: ~0.10% 256873: ~0.10% 256903: ~0.10% 257494: ~0.10% 259130: ~0.10% 260993: ~0.10% 261546: ~0.10% 263423: ~0.10% 264018: ~0.10% 265660: ~0.10% 266417: ~0.10% 269054: ~0.10% 272011: ~0.10% 272089: ~0.10% 272289: ~0.10% 272701: ~0.10% 274193: ~0.10% 281847: ~0.10% 282378: ~0.10% 282515: ~0.10% 284372: ~0.10% 284853: ~0.10% 284993: ~0.10% 287371: ~0.10% 288845: ~0.10% 290277: ~0.10% 293252: ~0.10% 294511: ~0.10% 295331: ~0.10% 298947: ~0.10% 299022: ~0.10% 301039: ~0.10% 301950: ~0.10% 306879: ~0.10% 307687: ~0.10% 307994: ~0.10% 308323: ~0.10% 308437: ~0.10% 309562: ~0.10% 310117: ~0.10% 311615: ~0.10% 312776: ~0.10% 313906: ~0.10% 315788: ~0.10% 315930: ~0.10% 318325: ~0.10% 319124: ~0.10% 321169: ~0.10% 322402: ~0.10% 323823: ~0.10% 323864: ~0.10% 325436: ~0.10% 325917: ~0.10% 326282: ~0.10% 326893: ~0.10% 328432: ~0.10% 328739: ~0.10% 328861: ~0.10% 328866: ~0.10% 330169: ~0.10% 330483: ~0.10% 331641: ~0.10% 333342: ~0.10% 335022: ~0.10% 336943: ~0.10% 338933: ~0.10% 340030: ~0.10% 340229: ~0.10% 340940: ~0.10% 342046: ~0.10% 343714: ~0.10% 346971: ~0.10% 348036: ~0.10% 348584: ~0.10% 348655: ~0.10% 354043: ~0.10% 356609: ~0.10% 358466: ~0.10% 358674: ~0.10% 359147: ~0.10% 359175: ~0.10% 359699: ~0.10% 360330: ~0.10% 360573: ~0.10% 363180: ~0.10% 363360: ~0.10% 364722: ~0.10% 364728: ~0.10% 365337: ~0.10% 365622: ~0.10% 365969: ~0.10% 366447: ~0.10% 368185: ~0.10% 368380: ~0.10% 368600: ~0.10% 370233: ~0.10% 372263: ~0.10% 372363: ~0.10% 376365: ~0.10% 376950: ~0.10% 378730: ~0.10% 379033: ~0.10% 381384: ~0.10% 381390: ~0.10% 382274: ~0.10% 383776: ~0.10% 383778: ~0.10% 384229: ~0.10% 385972: ~0.10% 386328: ~0.10% 386696: ~0.10% 388504: ~0.10% 389671: ~0.10% 391365: ~0.10% 391608: ~0.10% 395021: ~0.10% 398055: ~0.10% 399076: ~0.10% 399902: ~0.10% 401876: ~0.10% 402054: ~0.10% 402309: ~0.10% 402540: ~0.10% 402969: ~0.10% 404353: ~0.10% 404588: ~0.10% 407831: ~0.10% 408840: ~0.10% 409489: ~0.10% 410010: ~0.10% 413386: ~0.10% 418309: ~0.10% 418324: ~0.10% 418912: ~0.10% 420466: ~0.10% 420657: ~0.10% 421096: ~0.10% 421776: ~0.10% 422053: ~0.10% 423710: ~0.10% 425081: ~0.10% 425383: ~0.10% 425667: ~0.10% 427742: ~0.10% 427871: ~0.10% 428572: ~0.10% 430170: ~0.10% 430514: ~0.10% 431264: ~0.10% 432533: ~0.10% 433072: ~0.10% 433649: ~0.10% 435384: ~0.10% 437259: ~0.10% 438828: ~0.10% 439139: ~0.10% 440043: ~0.10% 440726: ~0.10% 440776: ~0.10% 445080: ~0.10% 446384: ~0.10% 448073: ~0.10% 448936: ~0.10% 452736: ~0.10% 455292: ~0.10% 456598: ~0.10% 458294: ~0.10% 459877: ~0.10% 460160: ~0.10% 460627: ~0.10% 461049: ~0.10% 461647: ~0.10% 462862: ~0.10% 463131: ~0.10% 463675: ~0.10% 463809: ~0.10% 464495: ~0.10% 465273: ~0.10% 465451: ~0.10% 466172: ~0.10% 467472: ~0.10% 467643: ~0.10% 467891: ~0.10% 469360: ~0.10% 469437: ~0.10% 470844: ~0.10% 474532: ~0.10% 476324: ~0.10% 476966: ~0.10% 477082: ~0.10% 477171: ~0.10% 477226: ~0.10% 477464: ~0.10% 478256: ~0.10% 478595: ~0.10% 479932: ~0.10% 481111: ~0.10% 481888: ~0.10% 482334: ~0.10% 483196: ~0.10% 484138: ~0.10% 484771: ~0.10% 485666: ~0.10% 487989: ~0.10% 488181: ~0.10% 489137: ~0.10% 491991: ~0.10% 492914: ~0.10% 495360: ~0.10% 495820: ~0.10% 496194: ~0.10% 497543: ~0.10% 498076: ~0.10% 498298: ~0.10% 499399: ~0.10% 500328: ~0.10% 500814: ~0.10% 500881: ~0.10% 501904: ~0.10% 501934: ~0.10% 502185: ~0.10% 502774: ~0.10% 503593: ~0.10% 504028: ~0.10% 504132: ~0.10% 504358: ~0.10% 505777: ~0.10% 508250: ~0.10% 510744: ~0.10% 510865: ~0.10% 510996: ~0.10% 511967: ~0.10% 513142: ~0.10% 515520: ~0.10% 516560: ~0.10% 518086: ~0.10% 518519: ~0.10% 520930: ~0.10% 521293: ~0.10% 521325: ~0.10% 522264: ~0.10% 524841: ~0.10% 529403: ~0.10% 529739: ~0.10% 530362: ~0.10% 532849: ~0.10% 533314: ~0.10% 534103: ~0.10% 534164: ~0.10% 534217: ~0.10% 536431: ~0.10% 537416: ~0.10% 539195: ~0.10% 539649: ~0.10% 540178: ~0.10% 542707: ~0.10% 542830: ~0.10% 545148: ~0.10% 546250: ~0.10% 548158: ~0.10% 550628: ~0.10% 551931: ~0.10% 552532: ~0.10% 552871: ~0.10% 554937: ~0.10% 555226: ~0.10% 555228: ~0.10% 555256: ~0.10% 555582: ~0.10% 560173: ~0.10% 560299: ~0.10% 561385: ~0.10% 564456: ~0.10% 564708: ~0.10% 565840: ~0.10% 568780: ~0.10% 569955: ~0.10% 572074: ~0.10% 572863: ~0.10% 573082: ~0.10% 573246: ~0.10% 573668: ~0.10% 575010: ~0.10% 576301: ~0.10% 577714: ~0.10% 580068: ~0.10% 580285: ~0.10% 580738: ~0.10% 581464: ~0.10% 581773: ~0.10% 581862: ~0.10% 582944: ~0.10% 583537: ~0.10% 584347: ~0.10% 586746: ~0.10% 586988: ~0.10% 588021: ~0.10% 593313: ~0.10% 593648: ~0.10% 594059: ~0.10% 594099: ~0.10% 594172: ~0.10% 594407: ~0.10% 596503: ~0.10% 596508: ~0.10% 596626: ~0.10% 597037: ~0.10% 598496: ~0.10% 598741: ~0.10% 598753: ~0.10% 600083: ~0.10% 601179: ~0.10% 602832: ~0.10% 602876: ~0.10% 603900: ~0.10% 605862: ~0.10% 606912: ~0.10% 606936: ~0.10% 607768: ~0.10% 608485: ~0.10% 609304: ~0.10% 609808: ~0.10% 611784: ~0.10% 613176: ~0.10% 613221: ~0.10% 613307: ~0.10% 613364: ~0.10% 614760: ~0.10% 616502: ~0.10% 616656: ~0.10% 616676: ~0.10% 616998: ~0.10% 617186: ~0.10% 618447: ~0.10% 619004: ~0.10% 619677: ~0.10% 620357: ~0.10% 620419: ~0.10% 623560: ~0.10% 626825: ~0.10% 626988: ~0.10% 627073: ~0.10% 627742: ~0.10% 629548: ~0.10% 629832: ~0.10% 629835: ~0.10% 630403: ~0.10% 632582: ~0.10% 632599: ~0.10% 634095: ~0.10% 634828: ~0.10% 635190: ~0.10% 635314: ~0.10% 638387: ~0.10% 639716: ~0.10% 641671: ~0.10% 642608: ~0.10% 644255: ~0.10% 644318: ~0.10% 644845: ~0.10% 648374: ~0.10% 649169: ~0.10% 649274: ~0.10% 653757: ~0.10% 654471: ~0.10% 654638: ~0.10% 654645: ~0.10% 656026: ~0.10% 656913: ~0.10% 658496: ~0.10% 658638: ~0.10% 658890: ~0.10% 658902: ~0.10% 659137: ~0.10% 659318: ~0.10% 659532: ~0.10% 662758: ~0.10% 665483: ~0.10% 667240: ~0.10% 667266: ~0.10% 667333: ~0.10% 668486: ~0.10% 670280: ~0.10% 671110: ~0.10% 672129: ~0.10% 673697: ~0.10% 674596: ~0.10% 674941: ~0.10% 676599: ~0.10% 677257: ~0.10% 677449: ~0.10% 680709: ~0.10% 681143: ~0.10% 681886: ~0.10% 684120: ~0.10% 686078: ~0.10% 686767: ~0.10% 686860: ~0.10% 688005: ~0.10% 689134: ~0.10% 689597: ~0.10% 697548: ~0.10% 699021: ~0.10% 699479: ~0.10% 700234: ~0.10% 700894: ~0.10% 702641: ~0.10% 703451: ~0.10% 703646: ~0.10% 704152: ~0.10% 708313: ~0.10% 710857: ~0.10% 712902: ~0.10% 712921: ~0.10% 714758: ~0.10% 717164: ~0.10% 720343: ~0.10% 720939: ~0.10% 721406: ~0.10% 721904: ~0.10% 721933: ~0.10% 722297: ~0.10% 723264: ~0.10% 723903: ~0.10% 729397: ~0.10% 730316: ~0.10% 734116: ~0.10% 734644: ~0.10% 736209: ~0.10% 737324: ~0.10% 740094: ~0.10% 740935: ~0.10% 741421: ~0.10% 744363: ~0.10% 745385: ~0.10% 745908: ~0.10% 746236: ~0.10% 749033: ~0.10% 750837: ~0.10% 753291: ~0.10% 753334: ~0.10% 753441: ~0.10% 754532: ~0.10% 756549: ~0.10% 757686: ~0.10% 757745: ~0.10% 757804: ~0.10% 758835: ~0.10% 758852: ~0.10% 759582: ~0.10% 761695: ~0.10% 767660: ~0.10% 770320: ~0.10% 770706: ~0.10% 771840: ~0.10% 772752: ~0.10% 775892: ~0.10% 776015: ~0.10% 776096: ~0.10% 777284: ~0.10% 779462: ~0.10% 782968: ~0.10% 786950: ~0.10% 788354: ~0.10% 788974: ~0.10% 789407: ~0.10% 789825: ~0.10% 790356: ~0.10% 792581: ~0.10% 794935: ~0.10% 795990: ~0.10% 797638: ~0.10% 797935: ~0.10% 799597: ~0.10% 800558: ~0.10% 801339: ~0.10% 803026: ~0.10% 804080: ~0.10% 804453: ~0.10% 804485: ~0.10% 805239: ~0.10% 805443: ~0.10% 807253: ~0.10% 808985: ~0.10% 811129: ~0.10% 811298: ~0.10% 814066: ~0.10% 818588: ~0.10% 819232: ~0.10% 819338: ~0.10% 820433: ~0.10% 821288: ~0.10% 823361: ~0.10% 823882: ~0.10% 826560: ~0.10% 826891: ~0.10% 827498: ~0.10% 828249: ~0.10% 828305: ~0.10% 831977: ~0.10% 834468: ~0.10% 835178: ~0.10% 837394: ~0.10% 837576: ~0.10% 838484: ~0.10% 838658: ~0.10% 839365: ~0.10% 840048: ~0.10% 841398: ~0.10% 842301: ~0.10% 842848: ~0.10% 844304: ~0.10% 845943: ~0.10% 846070: ~0.10% 847894: ~0.10% 848130: ~0.10% 849862: ~0.10% 850273: ~0.10% 852734: ~0.10% 855530: ~0.10% 855947: ~0.10% 856532: ~0.10% 856753: ~0.10% 857857: ~0.10% 858600: ~0.10% 858698: ~0.10% 863268: ~0.10% 863448: ~0.10% 863959: ~0.10% 865041: ~0.10% 868672: ~0.10% 869359: ~0.10% 869815: ~0.10% 869864: ~0.10% 870006: ~0.10% 873847: ~0.10% 874855: ~0.10% 875455: ~0.10% 875934: ~0.10% 876005: ~0.10% 877473: ~0.10% 877512: ~0.10% 880072: ~0.10% 882074: ~0.10% 885590: ~0.10% 886772: ~0.10% 887054: ~0.10% 887583: ~0.10% 887846: ~0.10% 889563: ~0.10% 890311: ~0.10% 890652: ~0.10% 890788: ~0.10% 892614: ~0.10% 893015: ~0.10% 894787: ~0.10% 895015: ~0.10% 896906: ~0.10% 897667: ~0.10% 898612: ~0.10% 899275: ~0.10% 900194: ~0.10% 901959: ~0.10% 903261: ~0.10% 903423: ~0.10% 904148: ~0.10% 906680: ~0.10% 907621: ~0.10% 908947: ~0.10% 910373: ~0.10% 911582: ~0.10% 914895: ~0.10% 919488: ~0.10% 920951: ~0.10% 922984: ~0.10% 928625: ~0.10% 929210: ~0.10% 929571: ~0.10% 930488: ~0.10% 931267: ~0.10% 931421: ~0.10% 931996: ~0.10% 933406: ~0.10% 933522: ~0.10% 934288: ~0.10% 939321: ~0.10% 941850: ~0.10% 949563: ~0.10% 952528: ~0.10% 953880: ~0.10% 953980: ~0.10% 957520: ~0.10% 958983: ~0.10% 960086: ~0.10% 961220: ~0.10% 963277: ~0.10% 963937: ~0.10% 965088: ~0.10% 965509: ~0.10% 965925: ~0.10% 967656: ~0.10% 967896: ~0.10% 968908: ~0.10% 969036: ~0.10% 969941: ~0.10% 970039: ~0.10% 971348: ~0.10% 972177: ~0.10% 972761: ~0.10% 974874: ~0.10% 975862: ~0.10% 976246: ~0.10% 978726: ~0.20% 978788: ~0.10% 978877: ~0.10% 979127: ~0.10% 979237: ~0.10% 980809: ~0.10% 985746: ~0.10% 986873: ~0.10% 987014: ~0.10% 987764: ~0.10% 987957: ~0.10% 988697: ~0.10% 989482: ~0.10% 990252: ~0.10% 991593: ~0.10% 992630: ~0.10% 993322: ~0.10% 993762: ~0.10% 994319: ~0.10% 994436: ~0.10% 995956: ~0.10% 996198: ~0.10% 997766: ~0.10% 997821: ~0.10% 997952: ~0.10% 999182: ~0.10% 1000020: ~0.20% 1000802: ~0.10% 1004378: ~0.10% 1004568: ~0.10% 1004650: ~0.10% 1005308: ~0.10% 1006544: ~0.10% 1009331: ~0.10% 1009704: ~0.10% 1011357: ~0.10% 1011905: ~0.10% 1013876: ~0.10% 1014134: ~0.10% 1015387: ~0.10% 1016946: ~0.10% 1017134: ~0.10% 1018892: ~0.10% 1019906: ~0.10% 1020412: ~0.10% 1020425: ~0.10% 1020970: ~0.10% 1022690: ~0.10% 1026393: ~0.10% 1027389: ~0.10% 1029474: ~0.10% 1029690: ~0.10% 1030434: ~0.10% 1032390: ~0.10% 1035119: ~0.10% 1036169: ~0.10% 1037938: ~0.10% 1038353: ~0.10% 1038762: ~0.10% 1038779: ~0.10% 1038966: ~0.10% 1040025: ~0.10% 1040244: ~0.10% 1040375: ~0.10% 1041331: ~0.10% 1042445: ~0.10% 1043004: ~0.10% 1043528: ~0.10% 1044727: ~0.10% 1048251: ~0.10% 1049535: ~0.10% 1049560: ~0.10% 1052888: ~0.10% 1052997: ~0.10% 1055533: ~0.10% 1057778: ~0.10% 1058550: ~0.10% 1059857: ~0.10% 1060423: ~0.10% 1060787: ~0.10% 1060962: ~0.10% 1062192: ~0.10% 1062549: ~0.10% 1063575: ~0.10% 1064950: ~0.10% 1064951: ~0.10% 1068069: ~0.10% 1069837: ~0.10% 1071973: ~0.10% 1072588: ~0.10% 1072655: ~0.10% 1073029: ~0.10% 1074193: ~0.10% 1075414: ~0.10% 1075808: ~0.10% 1076406: ~0.10% 1076447: ~0.10% 1077766: ~0.10% 1079884: ~0.10% 1082109: ~0.10% 1137812: ~0.10% 1138205: ~0.10% 1140773: ~0.10% 1140941: ~0.10% 1142000: ~0.10% 1142043: ~0.10% 1142233: ~0.10% 1142467: ~0.10% 1143079: ~0.10% 1145775: ~0.20% 1145856: ~0.10% 1146234: ~0.10% 1146633: ~0.10% 1146988: ~0.10% 1148975: ~0.10% 1150504: ~0.10% 1150633: ~0.10% 1151342: ~0.10% 1151358: ~0.10% 1152288: ~0.10% 1152306: ~0.10% 1153290: ~0.10% 1156403: ~0.10% 1157202: ~0.10% 1157778: ~0.10% 1158029: ~0.10% 1158264: ~0.10% 1160343: ~0.10% 1160469: ~0.10% 1161305: ~0.10% 1161739: ~0.10% 1163570: ~0.10% 1164068: ~0.10% 1165501: ~0.10% 1167427: ~0.10% 1167513: ~0.10% 1167747: ~0.10% 1169658: ~0.10% 1169691: ~0.10% 1169916: ~0.10% 1170374: ~0.10% 1170444: ~0.10% 1170949: ~0.10% 1172588: ~0.10% 1173538: ~0.10% 1175263: ~0.10% 1175393: ~0.10% 1175660: ~0.10% 1178659: ~0.10% 1180918: ~0.10% 1181077: ~0.10% 1181121: ~0.10% 1182612: ~0.10% 1183470: ~0.10% 1183720: ~0.10% 1184278: ~0.10% 1184692: ~0.10% 1185824: ~0.10%	size: 32 elements	size: 32 elements

Samples:

query_id	document_ids	scores
`603900`	`[7010623, 6442837, 7350525, 8692778, 2372626, ...]`	`[29.75, 16.375, 13.5390625, 15.875, 14.4140625, ...]`
`584347`	`[4872208, 725251, 7237781, 6220558, 2359322, ...]`	`[27.421875, 16.765625, 23.921875, 14.7578125, 16.515625, ...]`
`745908`	`[1682756, 5541499, 7449637, 8635692, 8126024, ...]`	`[23.5, 21.046875, 15.484375, 14.4375, 14.1875, ...]`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)",
    "document_regularizer_weight": 0.0003,
    "query_regularizer_weight": 0.0003
}

Evaluation Dataset

ms-marco-en-bge-gemma

Dataset: ms-marco-en-bge-gemma at 1a1ffe7
Size: 10,000 evaluation samples
Columns: query_id, document_ids, and scores

Approximate statistics based on the first 1000 samples:

	query_id	document_ids	scores
type	int	list	list
details	4390: ~0.10% 5379: ~0.10% 6815: ~0.10% 8748: ~0.10% 8765: ~0.10% 9140: ~0.10% 9386: ~0.10% 10308: ~0.10% 10372: ~0.10% 11823: ~0.10% 12403: ~0.10% 12543: ~0.10% 14592: ~0.10% 15699: ~0.10% 15907: ~0.10% 16274: ~0.10% 18265: ~0.10% 18536: ~0.10% 18557: ~0.10% 19620: ~0.10% 20301: ~0.10% 20465: ~0.10% 22689: ~0.10% 24329: ~0.10% 24450: ~0.10% 25777: ~0.10% 26901: ~0.10% 27718: ~0.10% 28725: ~0.10% 28942: ~0.10% 28947: ~0.10% 28963: ~0.10% 29234: ~0.10% 29862: ~0.10% 30280: ~0.10% 31031: ~0.10% 31316: ~0.10% 31995: ~0.10% 32918: ~0.10% 35583: ~0.10% 35707: ~0.10% 36802: ~0.10% 38125: ~0.10% 39033: ~0.10% 42644: ~0.10% 42987: ~0.10% 44710: ~0.10% 45107: ~0.10% 45117: ~0.10% 45201: ~0.10% 46358: ~0.10% 48190: ~0.10% 48302: ~0.10% 48404: ~0.10% 48502: ~0.10% 52179: ~0.10% 52736: ~0.10% 55890: ~0.10% 56747: ~0.10% 57517: ~0.10% 58099: ~0.10% 64359: ~0.10% 65300: ~0.10% 67160: ~0.10% 68349: ~0.10% 69921: ~0.10% 71500: ~0.10% 72470: ~0.10% 72494: ~0.10% 73175: ~0.10% 73555: ~0.10% 73580: ~0.10% 73647: ~0.10% 76405: ~0.10% 78261: ~0.10% 79380: ~0.10% 79382: ~0.10% 79434: ~0.10% 81156: ~0.10% 82596: ~0.10% 83221: ~0.10% 85126: ~0.10% 85335: ~0.10% 86559: ~0.10% 88566: ~0.10% 88908: ~0.10% 89154: ~0.10% 90817: ~0.10% 91316: ~0.10% 94344: ~0.10% 94491: ~0.10% 95068: ~0.10% 95130: ~0.10% 95539: ~0.10% 97149: ~0.10% 98344: ~0.10% 98363: ~0.10% 99513: ~0.10% 99538: ~0.10% 101768: ~0.10% 103847: ~0.10% 105234: ~0.10% 109619: ~0.10% 110448: ~0.10% 111149: ~0.10% 114093: ~0.10% 114138: ~0.10% 114513: ~0.10% 114772: ~0.10% 114849: ~0.10% 115435: ~0.10% 116332: ~0.10% 116477: ~0.10% 116757: ~0.10% 120662: ~0.10% 121037: ~0.10% 122697: ~0.10% 123325: ~0.10% 123927: ~0.10% 124017: ~0.10% 126980: ~0.10% 127423: ~0.10% 127690: ~0.10% 133003: ~0.10% 135011: ~0.10% 136008: ~0.10% 137997: ~0.10% 139521: ~0.10% 140495: ~0.10% 142428: ~0.10% 144183: ~0.10% 144563: ~0.10% 145750: ~0.10% 146287: ~0.10% 146759: ~0.10% 148194: ~0.10% 149092: ~0.10% 149687: ~0.10% 149772: ~0.10% 151119: ~0.10% 152092: ~0.10% 152489: ~0.10% 152887: ~0.10% 154400: ~0.10% 156833: ~0.10% 159791: ~0.10% 160567: ~0.10% 160743: ~0.10% 161655: ~0.10% 161685: ~0.10% 162200: ~0.10% 162727: ~0.10% 162889: ~0.10% 163712: ~0.10% 165610: ~0.10% 166822: ~0.10% 167374: ~0.10% 169480: ~0.10% 169765: ~0.10% 170119: ~0.10% 172817: ~0.10% 173840: ~0.10% 175828: ~0.10% 176368: ~0.10% 177622: ~0.10% 178336: ~0.10% 179350: ~0.10% 180727: ~0.10% 182199: ~0.10% 183437: ~0.10% 185083: ~0.10% 185101: ~0.10% 185726: ~0.10% 187409: ~0.10% 188087: ~0.10% 188697: ~0.10% 189876: ~0.10% 191685: ~0.10% 192074: ~0.10% 194598: ~0.10% 194709: ~0.10% 198606: ~0.10% 198877: ~0.10% 199614: ~0.10% 199998: ~0.10% 200047: ~0.10% 201526: ~0.10% 205011: ~0.10% 205014: ~0.10% 205462: ~0.10% 205798: ~0.10% 206334: ~0.10% 206878: ~0.10% 207093: ~0.10% 207324: ~0.10% 208729: ~0.10% 209430: ~0.10% 211495: ~0.10% 213754: ~0.10% 214524: ~0.10% 215205: ~0.10% 217617: ~0.10% 218074: ~0.10% 219386: ~0.10% 221646: ~0.10% 224108: ~0.10% 225314: ~0.10% 225494: ~0.10% 226375: ~0.10% 228400: ~0.10% 231912: ~0.10% 232072: ~0.10% 232150: ~0.10% 233593: ~0.10% 235058: ~0.10% 237719: ~0.10% 238734: ~0.10% 239329: ~0.10% 239841: ~0.10% 242797: ~0.10% 243838: ~0.10% 244555: ~0.10% 246261: ~0.10% 246810: ~0.10% 246971: ~0.10% 248800: ~0.10% 249238: ~0.10% 250489: ~0.10% 250583: ~0.10% 251134: ~0.10% 254463: ~0.10% 255700: ~0.10% 256147: ~0.10% 256934: ~0.10% 257539: ~0.10% 257874: ~0.10% 258361: ~0.10% 258984: ~0.10% 261536: ~0.10% 264371: ~0.10% 264581: ~0.10% 265204: ~0.10% 268939: ~0.10% 269159: ~0.10% 270239: ~0.10% 270562: ~0.10% 271024: ~0.10% 273476: ~0.10% 273987: ~0.10% 275693: ~0.10% 279412: ~0.10% 279978: ~0.10% 281341: ~0.10% 281843: ~0.10% 281984: ~0.10% 282398: ~0.10% 282477: ~0.10% 284101: ~0.10% 284897: ~0.10% 286024: ~0.10% 286613: ~0.10% 288238: ~0.10% 288637: ~0.10% 289182: ~0.10% 290333: ~0.10% 292101: ~0.10% 292304: ~0.10% 292706: ~0.10% 293467: ~0.10% 294227: ~0.10% 298077: ~0.10% 298606: ~0.10% 299814: ~0.10% 302072: ~0.10% 303286: ~0.10% 304290: ~0.10% 305258: ~0.10% 305759: ~0.10% 308281: ~0.10% 309366: ~0.10% 309841: ~0.10% 309876: ~0.10% 310895: ~0.10% 312620: ~0.10% 313065: ~0.10% 313355: ~0.10% 314200: ~0.10% 314249: ~0.10% 314502: ~0.10% 314607: ~0.10% 316523: ~0.10% 317004: ~0.10% 317296: ~0.10% 318169: ~0.10% 319162: ~0.10% 319326: ~0.10% 321813: ~0.10% 322557: ~0.10% 324028: ~0.10% 326029: ~0.10% 326239: ~0.10% 328079: ~0.10% 328398: ~0.10% 328658: ~0.10% 329648: ~0.10% 330144: ~0.10% 331125: ~0.10% 331497: ~0.10% 332125: ~0.10% 333638: ~0.10% 334805: ~0.10% 334886: ~0.10% 335513: ~0.10% 336447: ~0.10% 337444: ~0.10% 338146: ~0.10% 339364: ~0.10% 344138: ~0.10% 346118: ~0.10% 346190: ~0.10% 347596: ~0.10% 349548: ~0.10% 355788: ~0.10% 355994: ~0.10% 356466: ~0.10% 356636: ~0.10% 360379: ~0.10% 361433: ~0.10% 372327: ~0.10% 372589: ~0.10% 377248: ~0.10% 378064: ~0.10% 378574: ~0.10% 378811: ~0.10% 381583: ~0.10% 382505: ~0.10% 383267: ~0.10% 386557: ~0.10% 386680: ~0.10% 386788: ~0.10% 387113: ~0.10% 387800: ~0.10% 388099: ~0.10% 388169: ~0.10% 388596: ~0.10% 391329: ~0.10% 391525: ~0.10% 391833: ~0.10% 395828: ~0.10% 398039: ~0.10% 403645: ~0.10% 403718: ~0.10% 404689: ~0.10% 407568: ~0.10% 407775: ~0.10% 408655: ~0.10% 410328: ~0.10% 410813: ~0.10% 411000: ~0.10% 414825: ~0.10% 415279: ~0.10% 416019: ~0.10% 416263: ~0.10% 418411: ~0.10% 422007: ~0.10% 422709: ~0.10% 425753: ~0.10% 426507: ~0.10% 427031: ~0.10% 427791: ~0.10% 428229: ~0.10% 428342: ~0.10% 428767: ~0.10% 428964: ~0.10% 431237: ~0.10% 432733: ~0.10% 433713: ~0.10% 434796: ~0.10% 435869: ~0.10% 436969: ~0.10% 437099: ~0.10% 439932: ~0.10% 440104: ~0.10% 441089: ~0.10% 441394: ~0.10% 445288: ~0.10% 445787: ~0.10% 446270: ~0.10% 446589: ~0.10% 446998: ~0.10% 448736: ~0.10% 450628: ~0.10% 451218: ~0.10% 451855: ~0.10% 451926: ~0.10% 454878: ~0.10% 455408: ~0.10% 455962: ~0.10% 457016: ~0.10% 457496: ~0.10% 457586: ~0.10% 458596: ~0.10% 458880: ~0.10% 459273: ~0.10% 460043: ~0.10% 460693: ~0.10% 460958: ~0.10% 462373: ~0.10% 462413: ~0.10% 462591: ~0.10% 462668: ~0.10% 463159: ~0.10% 464182: ~0.10% 465724: ~0.10% 466532: ~0.10% 466933: ~0.10% 467242: ~0.10% 468308: ~0.10% 469153: ~0.10% 470723: ~0.10% 470957: ~0.10% 474030: ~0.10% 474441: ~0.10% 475203: ~0.10% 475424: ~0.10% 476021: ~0.10% 476278: ~0.10% 477667: ~0.10% 479459: ~0.10% 480209: ~0.10% 480428: ~0.10% 482759: ~0.10% 485734: ~0.10% 486087: ~0.10% 487942: ~0.10% 487980: ~0.10% 488439: ~0.10% 489535: ~0.10% 489972: ~0.10% 494081: ~0.10% 496313: ~0.10% 497423: ~0.10% 497738: ~0.10% 498408: ~0.10% 499051: ~0.10% 499566: ~0.10% 499922: ~0.10% 500175: ~0.10% 500972: ~0.10% 502160: ~0.10% 502372: ~0.10% 503184: ~0.10% 505278: ~0.10% 507420: ~0.10% 508988: ~0.10% 509041: ~0.10% 510181: ~0.10% 511524: ~0.10% 513945: ~0.10% 514472: ~0.10% 516184: ~0.10% 516259: ~0.10% 517168: ~0.10% 522875: ~0.10% 524129: ~0.10% 524976: ~0.10% 525601: ~0.10% 526066: ~0.10% 526269: ~0.10% 526447: ~0.10% 527296: ~0.10% 530124: ~0.10% 531044: ~0.10% 532932: ~0.10% 533295: ~0.10% 534805: ~0.10% 536734: ~0.10% 536843: ~0.10% 536879: ~0.10% 537699: ~0.10% 541861: ~0.10% 542948: ~0.10% 543308: ~0.10% 544558: ~0.10% 544595: ~0.10% 545585: ~0.10% 548392: ~0.10% 551242: ~0.10% 553831: ~0.10% 553965: ~0.10% 554159: ~0.10% 554644: ~0.10% 554727: ~0.10% 556800: ~0.10% 559252: ~0.10% 559405: ~0.10% 561027: ~0.10% 562403: ~0.10% 562819: ~0.10% 563992: ~0.10% 564352: ~0.10% 565590: ~0.10% 566165: ~0.10% 566984: ~0.10% 567002: ~0.10% 567150: ~0.10% 567900: ~0.10% 569410: ~0.10% 570279: ~0.10% 571778: ~0.10% 574781: ~0.10% 575000: ~0.10% 575009: ~0.10% 577385: ~0.10% 579145: ~0.10% 580503: ~0.10% 580506: ~0.10% 581730: ~0.10% 582200: ~0.10% 582703: ~0.10% 585083: ~0.10% 587134: ~0.10% 588012: ~0.10% 589149: ~0.10% 591167: ~0.10% 593843: ~0.10% 595681: ~0.10% 595886: ~0.10% 596055: ~0.10% 597284: ~0.10% 597897: ~0.10% 598828: ~0.10% 599825: ~0.10% 599874: ~0.10% 600431: ~0.10% 600613: ~0.10% 601291: ~0.10% 602177: ~0.10% 602837: 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~0.10% 916339: ~0.10% 916702: ~0.10% 916793: ~0.10% 917637: ~0.10% 924031: ~0.10% 924074: ~0.10% 924330: ~0.10% 924361: ~0.10% 925188: ~0.10% 925448: ~0.10% 927797: ~0.10% 927862: ~0.10% 927874: ~0.10% 927927: ~0.10% 929520: ~0.10% 930520: ~0.10% 930903: ~0.10% 931405: ~0.10% 931443: ~0.10% 931844: ~0.10% 931852: ~0.10% 932335: ~0.10% 934611: ~0.10% 935253: ~0.10% 937642: ~0.10% 938118: ~0.10% 938696: ~0.10% 939597: ~0.10% 939716: ~0.10% 940029: ~0.10% 942747: ~0.10% 942995: ~0.10% 943396: ~0.10% 943861: ~0.10% 944311: ~0.10% 944857: ~0.10% 945383: ~0.10% 945405: ~0.10% 945466: ~0.10% 945573: ~0.10% 946162: ~0.10% 946908: ~0.10% 948360: ~0.10% 953152: ~0.10% 953358: ~0.10% 953564: ~0.10% 956548: ~0.10% 957731: ~0.10% 957782: ~0.10% 958750: ~0.10% 959932: ~0.10% 961245: ~0.10% 961852: ~0.10% 963616: ~0.10% 966517: ~0.10% 968972: ~0.10% 969055: ~0.10% 969534: ~0.10% 969850: ~0.10% 969867: ~0.10% 973368: ~0.10% 974052: ~0.10% 977944: ~0.10% 978331: ~0.10% 980472: ~0.10% 980806: ~0.10% 981141: ~0.10% 983260: ~0.10% 985532: ~0.10% 985986: ~0.10% 987109: ~0.10% 987384: ~0.10% 987451: ~0.10% 988323: ~0.10% 991664: ~0.10% 992749: ~0.10% 994631: ~0.10% 999236: ~0.10% 999502: ~0.10% 1001411: ~0.10% 1001615: ~0.10% 1003117: ~0.10% 1004755: ~0.10% 1004766: ~0.10% 1005602: ~0.10% 1005624: ~0.10% 1008214: ~0.10% 1009646: ~0.10% 1010228: ~0.10% 1010404: ~0.10% 1011112: ~0.10% 1013680: ~0.10% 1014376: ~0.10% 1015814: ~0.10% 1016044: ~0.10% 1016535: ~0.10% 1020859: ~0.10% 1022344: ~0.10% 1025474: ~0.10% 1026369: ~0.10% 1026448: ~0.10% 1030958: ~0.10% 1031703: ~0.10% 1032696: ~0.10% 1032764: ~0.10% 1033836: ~0.10% 1035690: ~0.10% 1036183: ~0.10% 1036198: ~0.10% 1036601: ~0.10% 1039825: ~0.10% 1041717: ~0.10% 1043007: ~0.10% 1046670: ~0.10% 1051622: ~0.10% 1052063: ~0.10% 1052987: ~0.10% 1055244: ~0.10% 1056365: ~0.10% 1056443: ~0.10% 1058066: ~0.10% 1058535: ~0.10% 1058761: ~0.10% 1059701: ~0.10% 1059990: ~0.10% 1060906: ~0.10% 1062716: ~0.10% 1063601: ~0.10% 1064574: ~0.10% 1066906: ~0.10% 1067094: ~0.10% 1067991: ~0.10% 1068057: ~0.10% 1069859: ~0.10% 1072501: ~0.10% 1073422: ~0.10% 1073899: ~0.10% 1074409: ~0.10% 1075728: ~0.10% 1077197: ~0.10% 1078339: ~0.10% 1079378: ~0.10% 1079951: ~0.10% 1080355: ~0.10% 1082024: ~0.10% 1138486: ~0.10% 1139547: ~0.10% 1140274: ~0.10% 1140505: ~0.10% 1144490: ~0.10% 1144985: ~0.10% 1145987: ~0.10% 1147832: ~0.10% 1148612: ~0.10% 1148955: ~0.10% 1149257: ~0.10% 1149720: ~0.10% 1153279: ~0.10% 1153931: ~0.10% 1154610: ~0.10% 1155191: ~0.10% 1156637: ~0.10% 1156847: ~0.10% 1157090: ~0.10% 1157689: ~0.10% 1159825: ~0.10% 1160119: ~0.10% 1161025: ~0.10% 1162682: ~0.10% 1163146: ~0.10% 1163282: ~0.10% 1164573: ~0.10% 1164600: ~0.10% 1165879: ~0.10% 1167372: ~0.10% 1167913: ~0.10% 1169013: ~0.10% 1170906: ~0.10% 1171878: ~0.10% 1172026: ~0.10% 1172887: ~0.10% 1173174: ~0.10% 1173630: ~0.10% 1176430: ~0.10% 1177098: ~0.10% 1177277: ~0.10% 1177908: ~0.10% 1179525: ~0.10% 1180242: ~0.10% 1180326: ~0.10% 1180507: ~0.10% 1181571: ~0.10% 1182496: ~0.10% 1183420: ~0.10% 1184026: ~0.10%	size: 32 elements	size: 32 elements

Samples:

query_id	document_ids	scores
`22689`	`[3288373, 6422931, 1902940, 1448260, 975132, ...]`	`[26.03125, 17.9375, 16.375, 16.6875, 15.8046875, ...]`
`232150`	`[6017640, 6833904, 3846509, 2171280, 2048650, ...]`	`[26.390625, 15.3671875, 13.75, 15.28125, 15.65625, ...]`
`408655`	`[6648930, 6213451, 4063763, 2316914, 8553477, ...]`	`[24.796875, 11.6640625, 9.4296875, 11.4765625, 10.765625, ...]`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)",
    "document_regularizer_weight": 0.0003,
    "query_regularizer_weight": 0.0003
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
gradient_accumulation_steps: 5
num_train_epochs: 8
bf16: True
dataloader_drop_last: True
dataloader_num_workers: 4
dataloader_prefetch_factor: 2
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 5
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 8
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
dataloader_num_workers: 4
dataloader_prefetch_factor: 2
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	NanoSciFact_dot_ndcg@10	NanoMSMARCO_dot_ndcg@10	NanoTouche2020_dot_ndcg@10	NanoSCIDOCS_dot_ndcg@10	NanoNFCorpus_dot_ndcg@10	NanoBEIR_mean_dot_ndcg@10
0.0019	10	2994.9721	-	-	-	-	-	-	-
0.0038	20	2876.0482	-	-	-	-	-	-	-
0.0057	30	2768.7385	-	-	-	-	-	-	-
0.0076	40	2717.3891	-	-	-	-	-	-	-
0.0095	50	2624.9818	-	-	-	-	-	-	-
0.0114	60	2428.0463	-	-	-	-	-	-	-
0.0133	70	2306.2432	-	-	-	-	-	-	-
0.0152	80	2424.6492	-	-	-	-	-	-	-
0.0171	90	2182.7701	-	-	-	-	-	-	-
0.0190	100	2140.4197	-	-	-	-	-	-	-
0.0210	110	1905.7422	-	-	-	-	-	-	-
0.0229	120	1834.1852	-	-	-	-	-	-	-
0.0248	130	1724.2908	-	-	-	-	-	-	-
0.0267	140	1673.0305	-	-	-	-	-	-	-
0.0286	150	1547.898	-	-	-	-	-	-	-
0.0305	160	1617.1649	-	-	-	-	-	-	-
0.0324	170	1454.8863	-	-	-	-	-	-	-
0.0343	180	1477.4618	-	-	-	-	-	-	-
0.0362	190	1239.2485	-	-	-	-	-	-	-
0.0381	200	1289.3698	-	-	-	-	-	-	-
0.04	210	1229.1923	-	-	-	-	-	-	-
0.0419	220	1147.4083	-	-	-	-	-	-	-
0.0438	230	987.7668	-	-	-	-	-	-	-
0.0457	240	925.5002	-	-	-	-	-	-	-
0.0476	250	888.7103	-	-	-	-	-	-	-
0.0495	260	899.1394	-	-	-	-	-	-	-
0.0514	270	875.8373	-	-	-	-	-	-	-
0.0533	280	687.2472	-	-	-	-	-	-	-
0.0552	290	721.9673	-	-	-	-	-	-	-
0.0571	300	692.0023	-	-	-	-	-	-	-
0.0590	310	583.9026	-	-	-	-	-	-	-
0.0610	320	558.3605	-	-	-	-	-	-	-
0.0629	330	490.3735	-	-	-	-	-	-	-
0.0648	340	521.5393	-	-	-	-	-	-	-
0.0667	350	461.9679	-	-	-	-	-	-	-
0.0686	360	396.0208	-	-	-	-	-	-	-
0.0705	370	363.3042	-	-	-	-	-	-	-
0.0724	380	348.6711	-	-	-	-	-	-	-
0.0743	390	303.4701	-	-	-	-	-	-	-
0.0762	400	319.4116	-	-	-	-	-	-	-
0.0781	410	261.4831	-	-	-	-	-	-	-
0.08	420	262.3394	-	-	-	-	-	-	-
0.0819	430	221.0059	-	-	-	-	-	-	-
0.0838	440	203.8153	-	-	-	-	-	-	-
0.0857	450	199.7135	-	-	-	-	-	-	-
0.0876	460	159.9872	-	-	-	-	-	-	-
0.0895	470	160.8683	-	-	-	-	-	-	-
0.0914	480	150.8584	-	-	-	-	-	-	-
0.0933	490	131.823	-	-	-	-	-	-	-
0.0952	500	125.4891	-	-	-	-	-	-	-
0.0971	510	120.108	-	-	-	-	-	-	-
0.0990	520	107.8229	-	-	-	-	-	-	-
0.1010	530	103.3676	-	-	-	-	-	-	-
0.1029	540	75.0782	-	-	-	-	-	-	-
0.1048	550	69.389	-	-	-	-	-	-	-
0.1067	560	68.8503	-	-	-	-	-	-	-
0.1086	570	54.7002	-	-	-	-	-	-	-
0.1105	580	60.1035	-	-	-	-	-	-	-
0.1124	590	44.971	-	-	-	-	-	-	-
0.1143	600	48.8526	-	-	-	-	-	-	-
0.1162	610	43.7209	-	-	-	-	-	-	-
0.1181	620	41.3409	-	-	-	-	-	-	-
0.12	630	38.5651	-	-	-	-	-	-	-
0.1219	640	34.1392	-	-	-	-	-	-	-
0.1238	650	33.8514	-	-	-	-	-	-	-
0.1257	660	30.2743	-	-	-	-	-	-	-
0.1276	670	28.3113	-	-	-	-	-	-	-
0.1295	680	26.8586	-	-	-	-	-	-	-
0.1314	690	24.6085	-	-	-	-	-	-	-
0.1333	700	23.4412	-	-	-	-	-	-	-
0.1352	710	28.0492	-	-	-	-	-	-	-
0.1371	720	22.425	-	-	-	-	-	-	-
0.1390	730	17.9734	-	-	-	-	-	-	-
0.1410	740	20.1586	-	-	-	-	-	-	-
0.1429	750	18.4404	-	-	-	-	-	-	-
0.1448	760	18.549	-	-	-	-	-	-	-
0.1467	770	19.2777	-	-	-	-	-	-	-
0.1486	780	15.5817	-	-	-	-	-	-	-
0.1505	790	18.5389	-	-	-	-	-	-	-
0.1524	800	13.2143	-	-	-	-	-	-	-
0.1543	810	15.1378	-	-	-	-	-	-	-
0.1562	820	14.5266	-	-	-	-	-	-	-
0.1581	830	14.6869	-	-	-	-	-	-	-
0.16	840	14.9698	-	-	-	-	-	-	-
0.1619	850	11.251	-	-	-	-	-	-	-
0.1638	860	11.3852	-	-	-	-	-	-	-
0.1657	870	11.9011	-	-	-	-	-	-	-
0.1676	880	11.7494	-	-	-	-	-	-	-
0.1695	890	11.6186	-	-	-	-	-	-	-
0.1714	900	13.4544	10.5766	0.0859	0.3452	0.1982	0.0637	0.0859	0.1558
0.1733	910	8.5937	-	-	-	-	-	-	-
0.1752	920	9.8296	-	-	-	-	-	-	-
0.1771	930	8.8069	-	-	-	-	-	-	-
0.1790	940	9.0285	-	-	-	-	-	-	-
0.1810	950	8.0358	-	-	-	-	-	-	-
0.1829	960	7.581	-	-	-	-	-	-	-
0.1848	970	7.0625	-	-	-	-	-	-	-
0.1867	980	9.0603	-	-	-	-	-	-	-
0.1886	990	9.0297	-	-	-	-	-	-	-
0.1905	1000	8.5907	-	-	-	-	-	-	-
0.1924	1010	7.7399	-	-	-	-	-	-	-
0.1943	1020	6.6667	-	-	-	-	-	-	-
0.1962	1030	6.8515	-	-	-	-	-	-	-
0.1981	1040	6.6372	-	-	-	-	-	-	-
0.2	1050	7.6055	-	-	-	-	-	-	-
0.2019	1060	6.3491	-	-	-	-	-	-	-
0.2038	1070	6.1223	-	-	-	-	-	-	-
0.2057	1080	6.1731	-	-	-	-	-	-	-
0.2076	1090	6.8914	-	-	-	-	-	-	-
0.2095	1100	6.061	-	-	-	-	-	-	-
0.2114	1110	4.254	-	-	-	-	-	-	-
0.2133	1120	4.7267	-	-	-	-	-	-	-
0.2152	1130	5.7299	-	-	-	-	-	-	-
0.2171	1140	5.6166	-	-	-	-	-	-	-
0.2190	1150	5.1883	-	-	-	-	-	-	-
0.2210	1160	6.2679	-	-	-	-	-	-	-
0.2229	1170	5.366	-	-	-	-	-	-	-
0.2248	1180	4.7502	-	-	-	-	-	-	-
0.2267	1190	4.2778	-	-	-	-	-	-	-
0.2286	1200	4.651	-	-	-	-	-	-	-
0.2305	1210	4.0077	-	-	-	-	-	-	-
0.2324	1220	3.9847	-	-	-	-	-	-	-
0.2343	1230	4.4999	-	-	-	-	-	-	-
0.2362	1240	4.3909	-	-	-	-	-	-	-
0.2381	1250	4.0078	-	-	-	-	-	-	-
0.24	1260	4.0769	-	-	-	-	-	-	-
0.2419	1270	4.0876	-	-	-	-	-	-	-
0.2438	1280	3.1802	-	-	-	-	-	-	-
0.2457	1290	4.4312	-	-	-	-	-	-	-
0.2476	1300	3.122	-	-	-	-	-	-	-
0.2495	1310	3.2243	-	-	-	-	-	-	-
0.2514	1320	2.972	-	-	-	-	-	-	-
0.2533	1330	2.7484	-	-	-	-	-	-	-
0.2552	1340	3.1545	-	-	-	-	-	-	-
0.2571	1350	2.9258	-	-	-	-	-	-	-
0.2590	1360	3.223	-	-	-	-	-	-	-
0.2610	1370	2.5571	-	-	-	-	-	-	-
0.2629	1380	2.7505	-	-	-	-	-	-	-
0.2648	1390	3.1795	-	-	-	-	-	-	-
0.2667	1400	2.4692	-	-	-	-	-	-	-
0.2686	1410	2.4969	-	-	-	-	-	-	-
0.2705	1420	2.2375	-	-	-	-	-	-	-
0.2724	1430	2.2089	-	-	-	-	-	-	-
0.2743	1440	2.0929	-	-	-	-	-	-	-
0.2762	1450	2.2541	-	-	-	-	-	-	-
0.2781	1460	2.073	-	-	-	-	-	-	-
0.28	1470	1.7509	-	-	-	-	-	-	-
0.2819	1480	1.88	-	-	-	-	-	-	-
0.2838	1490	2.0452	-	-	-	-	-	-	-
0.2857	1500	2.0427	-	-	-	-	-	-	-
0.2876	1510	1.8692	-	-	-	-	-	-	-
0.2895	1520	1.6245	-	-	-	-	-	-	-
0.2914	1530	1.4086	-	-	-	-	-	-	-
0.2933	1540	1.3233	-	-	-	-	-	-	-
0.2952	1550	1.6868	-	-	-	-	-	-	-
0.2971	1560	1.6039	-	-	-	-	-	-	-
0.2990	1570	1.4004	-	-	-	-	-	-	-
0.3010	1580	1.7067	-	-	-	-	-	-	-
0.3029	1590	1.2969	-	-	-	-	-	-	-
0.3048	1600	1.086	-	-	-	-	-	-	-
0.3067	1610	1.2547	-	-	-	-	-	-	-
0.3086	1620	1.3724	-	-	-	-	-	-	-
0.3105	1630	1.2126	-	-	-	-	-	-	-
0.3124	1640	1.5234	-	-	-	-	-	-	-
0.3143	1650	0.9665	-	-	-	-	-	-	-
0.3162	1660	1.0516	-	-	-	-	-	-	-
0.3181	1670	1.1848	-	-	-	-	-	-	-
0.32	1680	1.0042	-	-	-	-	-	-	-
0.3219	1690	1.0611	-	-	-	-	-	-	-
0.3238	1700	0.8439	-	-	-	-	-	-	-
0.3257	1710	0.7997	-	-	-	-	-	-	-
0.3276	1720	0.8426	-	-	-	-	-	-	-
0.3295	1730	0.9351	-	-	-	-	-	-	-
0.3314	1740	0.7281	-	-	-	-	-	-	-
0.3333	1750	0.7766	-	-	-	-	-	-	-
0.3352	1760	0.6817	-	-	-	-	-	-	-
0.3371	1770	0.6998	-	-	-	-	-	-	-
0.3390	1780	0.6222	-	-	-	-	-	-	-
0.3410	1790	0.8199	-	-	-	-	-	-	-
0.3429	1800	0.7196	0.6711	0.4851	0.5472	0.4257	0.2587	0.2207	0.3875
0.3448	1810	0.6843	-	-	-	-	-	-	-
0.3467	1820	0.7703	-	-	-	-	-	-	-
0.3486	1830	0.5474	-	-	-	-	-	-	-
0.3505	1840	0.7604	-	-	-	-	-	-	-
0.3524	1850	0.785	-	-	-	-	-	-	-
0.3543	1860	0.6056	-	-	-	-	-	-	-
0.3562	1870	0.606	-	-	-	-	-	-	-
0.3581	1880	0.7331	-	-	-	-	-	-	-
0.36	1890	0.6224	-	-	-	-	-	-	-
0.3619	1900	0.6242	-	-	-	-	-	-	-
0.3638	1910	0.5157	-	-	-	-	-	-	-
0.3657	1920	0.4508	-	-	-	-	-	-	-
0.3676	1930	0.5699	-	-	-	-	-	-	-
0.3695	1940	0.5149	-	-	-	-	-	-	-
0.3714	1950	0.6027	-	-	-	-	-	-	-
0.3733	1960	0.5385	-	-	-	-	-	-	-
0.3752	1970	0.5341	-	-	-	-	-	-	-
0.3771	1980	0.5347	-	-	-	-	-	-	-
0.3790	1990	0.4511	-	-	-	-	-	-	-
0.3810	2000	0.3902	-	-	-	-	-	-	-
0.3829	2010	0.4864	-	-	-	-	-	-	-
0.3848	2020	0.5201	-	-	-	-	-	-	-
0.3867	2030	0.3924	-	-	-	-	-	-	-
0.3886	2040	0.4049	-	-	-	-	-	-	-
0.3905	2050	0.3745	-	-	-	-	-	-	-
0.3924	2060	0.3956	-	-	-	-	-	-	-
0.3943	2070	0.3577	-	-	-	-	-	-	-
0.3962	2080	0.5173	-	-	-	-	-	-	-
0.3981	2090	0.3534	-	-	-	-	-	-	-
0.4	2100	0.4583	-	-	-	-	-	-	-
0.4019	2110	0.5102	-	-	-	-	-	-	-
0.4038	2120	0.3882	-	-	-	-	-	-	-
0.4057	2130	0.3965	-	-	-	-	-	-	-
0.4076	2140	0.3527	-	-	-	-	-	-	-
0.4095	2150	0.4152	-	-	-	-	-	-	-
0.4114	2160	0.3532	-	-	-	-	-	-	-
0.4133	2170	0.3674	-	-	-	-	-	-	-
0.4152	2180	0.3552	-	-	-	-	-	-	-
0.4171	2190	0.3714	-	-	-	-	-	-	-
0.4190	2200	0.3299	-	-	-	-	-	-	-
0.4210	2210	0.3013	-	-	-	-	-	-	-
0.4229	2220	0.2713	-	-	-	-	-	-	-
0.4248	2230	0.3343	-	-	-	-	-	-	-
0.4267	2240	0.3158	-	-	-	-	-	-	-
0.4286	2250	0.3622	-	-	-	-	-	-	-
0.4305	2260	0.3067	-	-	-	-	-	-	-
0.4324	2270	0.2912	-	-	-	-	-	-	-
0.4343	2280	0.2962	-	-	-	-	-	-	-
0.4362	2290	0.2615	-	-	-	-	-	-	-
0.4381	2300	0.2582	-	-	-	-	-	-	-
0.44	2310	0.2544	-	-	-	-	-	-	-
0.4419	2320	0.3232	-	-	-	-	-	-	-
0.4438	2330	0.2816	-	-	-	-	-	-	-
0.4457	2340	0.2564	-	-	-	-	-	-	-
0.4476	2350	0.2867	-	-	-	-	-	-	-
0.4495	2360	0.1888	-	-	-	-	-	-	-
0.4514	2370	0.2519	-	-	-	-	-	-	-
0.4533	2380	0.227	-	-	-	-	-	-	-
0.4552	2390	0.2984	-	-	-	-	-	-	-
0.4571	2400	0.2478	-	-	-	-	-	-	-
0.4590	2410	0.2474	-	-	-	-	-	-	-
0.4610	2420	0.2382	-	-	-	-	-	-	-
0.4629	2430	0.2639	-	-	-	-	-	-	-
0.4648	2440	0.2179	-	-	-	-	-	-	-
0.4667	2450	0.2397	-	-	-	-	-	-	-
0.4686	2460	0.2447	-	-	-	-	-	-	-
0.4705	2470	0.1972	-	-	-	-	-	-	-
0.4724	2480	0.2139	-	-	-	-	-	-	-
0.4743	2490	0.2533	-	-	-	-	-	-	-
0.4762	2500	0.1891	-	-	-	-	-	-	-
0.4781	2510	0.2657	-	-	-	-	-	-	-
0.48	2520	0.2003	-	-	-	-	-	-	-
0.4819	2530	0.2903	-	-	-	-	-	-	-
0.4838	2540	0.261	-	-	-	-	-	-	-
0.4857	2550	0.2382	-	-	-	-	-	-	-
0.4876	2560	0.2203	-	-	-	-	-	-	-
0.4895	2570	0.2086	-	-	-	-	-	-	-
0.4914	2580	0.2496	-	-	-	-	-	-	-
0.4933	2590	0.1983	-	-	-	-	-	-	-
0.4952	2600	0.1764	-	-	-	-	-	-	-
0.4971	2610	0.2269	-	-	-	-	-	-	-
0.4990	2620	0.2609	-	-	-	-	-	-	-
0.5010	2630	0.198	-	-	-	-	-	-	-
0.5029	2640	0.228	-	-	-	-	-	-	-
0.5048	2650	0.2264	-	-	-	-	-	-	-
0.5067	2660	0.1897	-	-	-	-	-	-	-
0.5086	2670	0.2747	-	-	-	-	-	-	-
0.5105	2680	0.2352	-	-	-	-	-	-	-
0.5124	2690	0.1734	-	-	-	-	-	-	-
0.5143	2700	0.2031	0.2062	0.6812	0.5776	0.5619	0.3029	0.3408	0.4929
0.5162	2710	0.2193	-	-	-	-	-	-	-
0.5181	2720	0.2243	-	-	-	-	-	-	-
0.52	2730	0.1608	-	-	-	-	-	-	-
0.5219	2740	0.2202	-	-	-	-	-	-	-
0.5238	2750	0.217	-	-	-	-	-	-	-
0.5257	2760	0.2281	-	-	-	-	-	-	-
0.5276	2770	0.1824	-	-	-	-	-	-	-
0.5295	2780	0.2127	-	-	-	-	-	-	-
0.5314	2790	0.2175	-	-	-	-	-	-	-
0.5333	2800	0.2005	-	-	-	-	-	-	-
0.5352	2810	0.2042	-	-	-	-	-	-	-
0.5371	2820	0.2473	-	-	-	-	-	-	-
0.5390	2830	0.1918	-	-	-	-	-	-	-
0.5410	2840	0.1892	-	-	-	-	-	-	-
0.5429	2850	0.1693	-	-	-	-	-	-	-
0.5448	2860	0.1463	-	-	-	-	-	-	-
0.5467	2870	0.2621	-	-	-	-	-	-	-
0.5486	2880	0.2264	-	-	-	-	-	-	-
0.5505	2890	0.2188	-	-	-	-	-	-	-
0.5524	2900	0.2193	-	-	-	-	-	-	-
0.5543	2910	0.1659	-	-	-	-	-	-	-
0.5562	2920	0.2343	-	-	-	-	-	-	-
0.5581	2930	0.1463	-	-	-	-	-	-	-
0.56	2940	0.1944	-	-	-	-	-	-	-
0.5619	2950	0.2839	-	-	-	-	-	-	-
0.5638	2960	0.1778	-	-	-	-	-	-	-
0.5657	2970	0.1925	-	-	-	-	-	-	-
0.5676	2980	0.197	-	-	-	-	-	-	-
0.5695	2990	0.166	-	-	-	-	-	-	-
0.5714	3000	0.1675	-	-	-	-	-	-	-
0.5733	3010	0.156	-	-	-	-	-	-	-
0.5752	3020	0.1874	-	-	-	-	-	-	-
0.5771	3030	0.1958	-	-	-	-	-	-	-
0.5790	3040	0.2071	-	-	-	-	-	-	-
0.5810	3050	0.1922	-	-	-	-	-	-	-
0.5829	3060	0.1653	-	-	-	-	-	-	-
0.5848	3070	0.1841	-	-	-	-	-	-	-
0.5867	3080	0.1577	-	-	-	-	-	-	-
0.5886	3090	0.1389	-	-	-	-	-	-	-
0.5905	3100	0.1687	-	-	-	-	-	-	-
0.5924	3110	0.1814	-	-	-	-	-	-	-
0.5943	3120	0.2289	-	-	-	-	-	-	-
0.5962	3130	0.1774	-	-	-	-	-	-	-
0.5981	3140	0.1677	-	-	-	-	-	-	-
0.6	3150	0.1667	-	-	-	-	-	-	-
0.6019	3160	0.1713	-	-	-	-	-	-	-
0.6038	3170	0.1737	-	-	-	-	-	-	-
0.6057	3180	0.2101	-	-	-	-	-	-	-
0.6076	3190	0.1972	-	-	-	-	-	-	-
0.6095	3200	0.1263	-	-	-	-	-	-	-
0.6114	3210	0.2261	-	-	-	-	-	-	-
0.6133	3220	0.1755	-	-	-	-	-	-	-
0.6152	3230	0.2031	-	-	-	-	-	-	-
0.6171	3240	0.1794	-	-	-	-	-	-	-
0.6190	3250	0.1495	-	-	-	-	-	-	-
0.6210	3260	0.1845	-	-	-	-	-	-	-
0.6229	3270	0.1631	-	-	-	-	-	-	-
0.6248	3280	0.1681	-	-	-	-	-	-	-
0.6267	3290	0.1784	-	-	-	-	-	-	-
0.6286	3300	0.1847	-	-	-	-	-	-	-
0.6305	3310	0.1734	-	-	-	-	-	-	-
0.6324	3320	0.1999	-	-	-	-	-	-	-
0.6343	3330	0.1977	-	-	-	-	-	-	-
0.6362	3340	0.1961	-	-	-	-	-	-	-
0.6381	3350	0.1845	-	-	-	-	-	-	-
0.64	3360	0.1885	-	-	-	-	-	-	-
0.6419	3370	0.1583	-	-	-	-	-	-	-
0.6438	3380	0.1868	-	-	-	-	-	-	-
0.6457	3390	0.1878	-	-	-	-	-	-	-
0.6476	3400	0.1325	-	-	-	-	-	-	-
0.6495	3410	0.1728	-	-	-	-	-	-	-
0.6514	3420	0.1923	-	-	-	-	-	-	-
0.6533	3430	0.1656	-	-	-	-	-	-	-
0.6552	3440	0.1809	-	-	-	-	-	-	-
0.6571	3450	0.1791	-	-	-	-	-	-	-
0.6590	3460	0.1577	-	-	-	-	-	-	-
0.6610	3470	0.1796	-	-	-	-	-	-	-
0.6629	3480	0.185	-	-	-	-	-	-	-
0.6648	3490	0.1544	-	-	-	-	-	-	-
0.6667	3500	0.1352	-	-	-	-	-	-	-
0.6686	3510	0.1613	-	-	-	-	-	-	-
0.6705	3520	0.156	-	-	-	-	-	-	-
0.6724	3530	0.1734	-	-	-	-	-	-	-
0.6743	3540	0.1714	-	-	-	-	-	-	-
0.6762	3550	0.192	-	-	-	-	-	-	-
0.6781	3560	0.1526	-	-	-	-	-	-	-
0.68	3570	0.1626	-	-	-	-	-	-	-
0.6819	3580	0.1856	-	-	-	-	-	-	-
0.6838	3590	0.1363	-	-	-	-	-	-	-
0.6857	3600	0.1738	0.1684	0.6720	0.5840	0.5840	0.3305	0.3560	0.5053
0.6876	3610	0.1508	-	-	-	-	-	-	-
0.6895	3620	0.1361	-	-	-	-	-	-	-
0.6914	3630	0.18	-	-	-	-	-	-	-
0.6933	3640	0.1681	-	-	-	-	-	-	-
0.6952	3650	0.1427	-	-	-	-	-	-	-
0.6971	3660	0.1654	-	-	-	-	-	-	-
0.6990	3670	0.1433	-	-	-	-	-	-	-
0.7010	3680	0.1457	-	-	-	-	-	-	-
0.7029	3690	0.1967	-	-	-	-	-	-	-
0.7048	3700	0.1669	-	-	-	-	-	-	-
0.7067	3710	0.1686	-	-	-	-	-	-	-
0.7086	3720	0.1872	-	-	-	-	-	-	-
0.7105	3730	0.1652	-	-	-	-	-	-	-
0.7124	3740	0.1481	-	-	-	-	-	-	-
0.7143	3750	0.1453	-	-	-	-	-	-	-
0.7162	3760	0.1544	-	-	-	-	-	-	-
0.7181	3770	0.1825	-	-	-	-	-	-	-
0.72	3780	0.1838	-	-	-	-	-	-	-
0.7219	3790	0.1796	-	-	-	-	-	-	-
0.7238	3800	0.1738	-	-	-	-	-	-	-
0.7257	3810	0.1773	-	-	-	-	-	-	-
0.7276	3820	0.1646	-	-	-	-	-	-	-
0.7295	3830	0.1672	-	-	-	-	-	-	-
0.7314	3840	0.1674	-	-	-	-	-	-	-
0.7333	3850	0.1923	-	-	-	-	-	-	-
0.7352	3860	0.1782	-	-	-	-	-	-	-
0.7371	3870	0.1605	-	-	-	-	-	-	-
0.7390	3880	0.1912	-	-	-	-	-	-	-
0.7410	3890	0.1744	-	-	-	-	-	-	-
0.7429	3900	0.1718	-	-	-	-	-	-	-
0.7448	3910	0.1653	-	-	-	-	-	-	-
0.7467	3920	0.192	-	-	-	-	-	-	-
0.7486	3930	0.1554	-	-	-	-	-	-	-
0.7505	3940	0.1679	-	-	-	-	-	-	-
0.7524	3950	0.1705	-	-	-	-	-	-	-
0.7543	3960	0.1429	-	-	-	-	-	-	-
0.7562	3970	0.1712	-	-	-	-	-	-	-
0.7581	3980	0.1954	-	-	-	-	-	-	-
0.76	3990	0.1663	-	-	-	-	-	-	-
0.7619	4000	0.177	-	-	-	-	-	-	-
0.7638	4010	0.196	-	-	-	-	-	-	-
0.7657	4020	0.1676	-	-	-	-	-	-	-
0.7676	4030	0.1607	-	-	-	-	-	-	-
0.7695	4040	0.1646	-	-	-	-	-	-	-
0.7714	4050	0.1503	-	-	-	-	-	-	-
0.7733	4060	0.1586	-	-	-	-	-	-	-
0.7752	4070	0.1715	-	-	-	-	-	-	-
0.7771	4080	0.1133	-	-	-	-	-	-	-
0.7790	4090	0.1601	-	-	-	-	-	-	-
0.7810	4100	0.1521	-	-	-	-	-	-	-
0.7829	4110	0.1447	-	-	-	-	-	-	-
0.7848	4120	0.1517	-	-	-	-	-	-	-
0.7867	4130	0.1604	-	-	-	-	-	-	-
0.7886	4140	0.1341	-	-	-	-	-	-	-
0.7905	4150	0.1862	-	-	-	-	-	-	-
0.7924	4160	0.1715	-	-	-	-	-	-	-
0.7943	4170	0.1567	-	-	-	-	-	-	-
0.7962	4180	0.159	-	-	-	-	-	-	-
0.7981	4190	0.1498	-	-	-	-	-	-	-
0.8	4200	0.1457	-	-	-	-	-	-	-
0.8019	4210	0.1778	-	-	-	-	-	-	-
0.8038	4220	0.1345	-	-	-	-	-	-	-
0.8057	4230	0.1382	-	-	-	-	-	-	-
0.8076	4240	0.1749	-	-	-	-	-	-	-
0.8095	4250	0.1493	-	-	-	-	-	-	-
0.8114	4260	0.1399	-	-	-	-	-	-	-
0.8133	4270	0.1482	-	-	-	-	-	-	-
0.8152	4280	0.1802	-	-	-	-	-	-	-
0.8171	4290	0.1334	-	-	-	-	-	-	-
0.8190	4300	0.1539	-	-	-	-	-	-	-
0.8210	4310	0.1483	-	-	-	-	-	-	-
0.8229	4320	0.1849	-	-	-	-	-	-	-
0.8248	4330	0.2092	-	-	-	-	-	-	-
0.8267	4340	0.1925	-	-	-	-	-	-	-
0.8286	4350	0.1685	-	-	-	-	-	-	-
0.8305	4360	0.1733	-	-	-	-	-	-	-
0.8324	4370	0.1506	-	-	-	-	-	-	-
0.8343	4380	0.1535	-	-	-	-	-	-	-
0.8362	4390	0.1461	-	-	-	-	-	-	-
0.8381	4400	0.1422	-	-	-	-	-	-	-
0.84	4410	0.1546	-	-	-	-	-	-	-
0.8419	4420	0.1741	-	-	-	-	-	-	-
0.8438	4430	0.1604	-	-	-	-	-	-	-
0.8457	4440	0.1515	-	-	-	-	-	-	-
0.8476	4450	0.1938	-	-	-	-	-	-	-
0.8495	4460	0.1556	-	-	-	-	-	-	-
0.8514	4470	0.1664	-	-	-	-	-	-	-
0.8533	4480	0.1452	-	-	-	-	-	-	-
0.8552	4490	0.1682	-	-	-	-	-	-	-
0.8571	4500	0.1787	0.1570	0.6702	0.6053	0.5783	0.3425	0.3502	0.5093
0.8590	4510	0.1643	-	-	-	-	-	-	-
0.8610	4520	0.1686	-	-	-	-	-	-	-
0.8629	4530	0.1452	-	-	-	-	-	-	-
0.8648	4540	0.1964	-	-	-	-	-	-	-
0.8667	4550	0.1282	-	-	-	-	-	-	-
0.8686	4560	0.1853	-	-	-	-	-	-	-
0.8705	4570	0.1655	-	-	-	-	-	-	-
0.8724	4580	0.1847	-	-	-	-	-	-	-
0.8743	4590	0.1787	-	-	-	-	-	-	-
0.8762	4600	0.1564	-	-	-	-	-	-	-
0.8781	4610	0.1567	-	-	-	-	-	-	-
0.88	4620	0.1573	-	-	-	-	-	-	-
0.8819	4630	0.1392	-	-	-	-	-	-	-
0.8838	4640	0.1402	-	-	-	-	-	-	-
0.8857	4650	0.1608	-	-	-	-	-	-	-
0.8876	4660	0.1664	-	-	-	-	-	-	-
0.8895	4670	0.1675	-	-	-	-	-	-	-
0.8914	4680	0.1847	-	-	-	-	-	-	-
0.8933	4690	0.1346	-	-	-	-	-	-	-
0.8952	4700	0.1429	-	-	-	-	-	-	-
0.8971	4710	0.1465	-	-	-	-	-	-	-
0.8990	4720	0.1652	-	-	-	-	-	-	-
0.9010	4730	0.1496	-	-	-	-	-	-	-
0.9029	4740	0.168	-	-	-	-	-	-	-
0.9048	4750	0.1834	-	-	-	-	-	-	-
0.9067	4760	0.1598	-	-	-	-	-	-	-
0.9086	4770	0.1576	-	-	-	-	-	-	-
0.9105	4780	0.1571	-	-	-	-	-	-	-
0.9124	4790	0.1521	-	-	-	-	-	-	-
0.9143	4800	0.1373	-	-	-	-	-	-	-
0.9162	4810	0.1937	-	-	-	-	-	-	-
0.9181	4820	0.1478	-	-	-	-	-	-	-
0.92	4830	0.1594	-	-	-	-	-	-	-
0.9219	4840	0.1147	-	-	-	-	-	-	-
0.9238	4850	0.1434	-	-	-	-	-	-	-
0.9257	4860	0.106	-	-	-	-	-	-	-
0.9276	4870	0.1487	-	-	-	-	-	-	-
0.9295	4880	0.1634	-	-	-	-	-	-	-
0.9314	4890	0.1636	-	-	-	-	-	-	-
0.9333	4900	0.1691	-	-	-	-	-	-	-
0.9352	4910	0.1547	-	-	-	-	-	-	-
0.9371	4920	0.1572	-	-	-	-	-	-	-
0.9390	4930	0.1738	-	-	-	-	-	-	-
0.9410	4940	0.1555	-	-	-	-	-	-	-
0.9429	4950	0.1487	-	-	-	-	-	-	-
0.9448	4960	0.1634	-	-	-	-	-	-	-
0.9467	4970	0.1401	-	-	-	-	-	-	-
0.9486	4980	0.1691	-	-	-	-	-	-	-
0.9505	4990	0.1699	-	-	-	-	-	-	-
0.9524	5000	0.1236	-	-	-	-	-	-	-
0.9543	5010	0.1682	-	-	-	-	-	-	-
0.9562	5020	0.1652	-	-	-	-	-	-	-
0.9581	5030	0.1479	-	-	-	-	-	-	-
0.96	5040	0.1363	-	-	-	-	-	-	-
0.9619	5050	0.1251	-	-	-	-	-	-	-
0.9638	5060	0.1527	-	-	-	-	-	-	-
0.9657	5070	0.1263	-	-	-	-	-	-	-
0.9676	5080	0.1295	-	-	-	-	-	-	-
0.9695	5090	0.1291	-	-	-	-	-	-	-
0.9714	5100	0.1391	-	-	-	-	-	-	-
0.9733	5110	0.1493	-	-	-	-	-	-	-
0.9752	5120	0.142	-	-	-	-	-	-	-
0.9771	5130	0.1572	-	-	-	-	-	-	-
0.9790	5140	0.1406	-	-	-	-	-	-	-
0.9810	5150	0.1739	-	-	-	-	-	-	-
0.9829	5160	0.1322	-	-	-	-	-	-	-
0.9848	5170	0.1674	-	-	-	-	-	-	-
0.9867	5180	0.1413	-	-	-	-	-	-	-
0.9886	5190	0.138	-	-	-	-	-	-	-
0.9905	5200	0.0982	-	-	-	-	-	-	-
0.9924	5210	0.1631	-	-	-	-	-	-	-
0.9943	5220	0.1506	-	-	-	-	-	-	-
0.9962	5230	0.1593	-	-	-	-	-	-	-
0.9981	5240	0.1295	-	-	-	-	-	-	-
1.0	5250	0.1315	-	-	-	-	-	-	-
1.0019	5260	0.1575	-	-	-	-	-	-	-
1.0038	5270	0.1702	-	-	-	-	-	-	-
1.0057	5280	0.159	-	-	-	-	-	-	-
1.0076	5290	0.1554	-	-	-	-	-	-	-
1.0095	5300	0.1546	-	-	-	-	-	-	-
1.0114	5310	0.121	-	-	-	-	-	-	-
1.0133	5320	0.143	-	-	-	-	-	-	-
1.0152	5330	0.1196	-	-	-	-	-	-	-
1.0171	5340	0.1312	-	-	-	-	-	-	-
1.0190	5350	0.1495	-	-	-	-	-	-	-
1.0210	5360	0.1659	-	-	-	-	-	-	-
1.0229	5370	0.1261	-	-	-	-	-	-	-
1.0248	5380	0.1252	-	-	-	-	-	-	-
1.0267	5390	0.1584	-	-	-	-	-	-	-
1.0286	5400	0.1707	0.1352	0.6903	0.6174	0.5862	0.3335	0.3463	0.5148
1.0305	5410	0.1157	-	-	-	-	-	-	-
1.0324	5420	0.128	-	-	-	-	-	-	-
1.0343	5430	0.1365	-	-	-	-	-	-	-
1.0362	5440	0.1535	-	-	-	-	-	-	-
1.0381	5450	0.155	-	-	-	-	-	-	-
1.04	5460	0.1572	-	-	-	-	-	-	-
1.0419	5470	0.1295	-	-	-	-	-	-	-
1.0438	5480	0.128	-	-	-	-	-	-	-
1.0457	5490	0.1498	-	-	-	-	-	-	-
1.0476	5500	0.129	-	-	-	-	-	-	-
1.0495	5510	0.1368	-	-	-	-	-	-	-
1.0514	5520	0.1252	-	-	-	-	-	-	-
1.0533	5530	0.152	-	-	-	-	-	-	-
1.0552	5540	0.1224	-	-	-	-	-	-	-
1.0571	5550	0.1491	-	-	-	-	-	-	-
1.0590	5560	0.1338	-	-	-	-	-	-	-
1.0610	5570	0.1542	-	-	-	-	-	-	-
1.0629	5580	0.169	-	-	-	-	-	-	-
1.0648	5590	0.1483	-	-	-	-	-	-	-
1.0667	5600	0.1352	-	-	-	-	-	-	-
1.0686	5610	0.1361	-	-	-	-	-	-	-
1.0705	5620	0.1301	-	-	-	-	-	-	-
1.0724	5630	0.1579	-	-	-	-	-	-	-
1.0743	5640	0.1336	-	-	-	-	-	-	-
1.0762	5650	0.1207	-	-	-	-	-	-	-
1.0781	5660	0.1464	-	-	-	-	-	-	-
1.08	5670	0.1511	-	-	-	-	-	-	-
1.0819	5680	0.12	-	-	-	-	-	-	-
1.0838	5690	0.1614	-	-	-	-	-	-	-
1.0857	5700	0.1628	-	-	-	-	-	-	-
1.0876	5710	0.1642	-	-	-	-	-	-	-
1.0895	5720	0.1446	-	-	-	-	-	-	-
1.0914	5730	0.1336	-	-	-	-	-	-	-
1.0933	5740	0.1272	-	-	-	-	-	-	-
1.0952	5750	0.1557	-	-	-	-	-	-	-
1.0971	5760	0.1812	-	-	-	-	-	-	-
1.0990	5770	0.1023	-	-	-	-	-	-	-
1.1010	5780	0.1344	-	-	-	-	-	-	-
1.1029	5790	0.1461	-	-	-	-	-	-	-
1.1048	5800	0.1191	-	-	-	-	-	-	-
1.1067	5810	0.1107	-	-	-	-	-	-	-
1.1086	5820	0.137	-	-	-	-	-	-	-
1.1105	5830	0.1466	-	-	-	-	-	-	-
1.1124	5840	0.164	-	-	-	-	-	-	-
1.1143	5850	0.149	-	-	-	-	-	-	-
1.1162	5860	0.161	-	-	-	-	-	-	-
1.1181	5870	0.1357	-	-	-	-	-	-	-
1.12	5880	0.1544	-	-	-	-	-	-	-
1.1219	5890	0.1216	-	-	-	-	-	-	-
1.1238	5900	0.1505	-	-	-	-	-	-	-
1.1257	5910	0.1293	-	-	-	-	-	-	-
1.1276	5920	0.1392	-	-	-	-	-	-	-
1.1295	5930	0.1392	-	-	-	-	-	-	-
1.1314	5940	0.1264	-	-	-	-	-	-	-
1.1333	5950	0.1434	-	-	-	-	-	-	-
1.1352	5960	0.1543	-	-	-	-	-	-	-
1.1371	5970	0.1446	-	-	-	-	-	-	-
1.1390	5980	0.143	-	-	-	-	-	-	-
1.1410	5990	0.1547	-	-	-	-	-	-	-
1.1429	6000	0.1367	-	-	-	-	-	-	-
1.1448	6010	0.1198	-	-	-	-	-	-	-
1.1467	6020	0.1419	-	-	-	-	-	-	-
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1.8838	9890	0.1292	-	-	-	-	-	-	-
1.8857	9900	0.0996	0.1180	0.6834	0.6319	0.5603	0.3487	0.3457	0.5140
1.8876	9910	0.1511	-	-	-	-	-	-	-
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1.92	10080	0.1263	-	-	-	-	-	-	-
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1.9410	10190	0.1295	-	-	-	-	-	-	-
1.9429	10200	0.1361	-	-	-	-	-	-	-
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1.9714	10350	0.1436	-	-	-	-	-	-	-
1.9733	10360	0.118	-	-	-	-	-	-	-
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1.9886	10440	0.1226	-	-	-	-	-	-	-
1.9905	10450	0.1319	-	-	-	-	-	-	-
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1.9962	10480	0.1503	-	-	-	-	-	-	-
1.9981	10490	0.1137	-	-	-	-	-	-	-
2.0	10500	0.1436	-	-	-	-	-	-	-
2.0019	10510	0.1324	-	-	-	-	-	-	-
2.0038	10520	0.0877	-	-	-	-	-	-	-
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2.0171	10590	0.0876	-	-	-	-	-	-	-
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2.0210	10610	0.1279	-	-	-	-	-	-	-
2.0229	10620	0.1188	-	-	-	-	-	-	-
2.0248	10630	0.1144	-	-	-	-	-	-	-
2.0267	10640	0.0952	-	-	-	-	-	-	-
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2.0343	10680	0.1229	-	-	-	-	-	-	-
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2.0533	10780	0.109	-	-	-	-	-	-	-
2.0552	10790	0.1125	-	-	-	-	-	-	-
2.0571	10800	0.1103	0.1243	0.7002	0.6499	0.5681	0.3396	0.3576	0.5231
2.0590	10810	0.1119	-	-	-	-	-	-	-
2.0610	10820	0.0972	-	-	-	-	-	-	-
2.0629	10830	0.1055	-	-	-	-	-	-	-
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2.0876	10960	0.1193	-	-	-	-	-	-	-
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2.0914	10980	0.1222	-	-	-	-	-	-	-
2.0933	10990	0.1207	-	-	-	-	-	-	-
2.0952	11000	0.124	-	-	-	-	-	-	-
2.0971	11010	0.1299	-	-	-	-	-	-	-
2.0990	11020	0.0828	-	-	-	-	-	-	-
2.1010	11030	0.0842	-	-	-	-	-	-	-
2.1029	11040	0.1108	-	-	-	-	-	-	-
2.1048	11050	0.0979	-	-	-	-	-	-	-
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2.1162	11110	0.1229	-	-	-	-	-	-	-
2.1181	11120	0.1372	-	-	-	-	-	-	-
2.12	11130	0.1355	-	-	-	-	-	-	-
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2.1238	11150	0.1304	-	-	-	-	-	-	-
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2.1314	11190	0.1324	-	-	-	-	-	-	-
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2.1352	11210	0.1256	-	-	-	-	-	-	-
2.1371	11220	0.1223	-	-	-	-	-	-	-
2.1390	11230	0.0972	-	-	-	-	-	-	-
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2.1524	11300	0.1253	-	-	-	-	-	-	-
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2.1581	11330	0.1075	-	-	-	-	-	-	-
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2.1695	11390	0.1242	-	-	-	-	-	-	-
2.1714	11400	0.1076	-	-	-	-	-	-	-
2.1733	11410	0.0986	-	-	-	-	-	-	-
2.1752	11420	0.1048	-	-	-	-	-	-	-
2.1771	11430	0.1227	-	-	-	-	-	-	-
2.1790	11440	0.1085	-	-	-	-	-	-	-
2.1810	11450	0.1076	-	-	-	-	-	-	-
2.1829	11460	0.0934	-	-	-	-	-	-	-
2.1848	11470	0.0861	-	-	-	-	-	-	-
2.1867	11480	0.0984	-	-	-	-	-	-	-
2.1886	11490	0.1223	-	-	-	-	-	-	-
2.1905	11500	0.1045	-	-	-	-	-	-	-
2.1924	11510	0.1032	-	-	-	-	-	-	-
2.1943	11520	0.1351	-	-	-	-	-	-	-
2.1962	11530	0.1022	-	-	-	-	-	-	-
2.1981	11540	0.1193	-	-	-	-	-	-	-
2.2	11550	0.0992	-	-	-	-	-	-	-
2.2019	11560	0.1453	-	-	-	-	-	-	-
2.2038	11570	0.1123	-	-	-	-	-	-	-
2.2057	11580	0.098	-	-	-	-	-	-	-
2.2076	11590	0.1077	-	-	-	-	-	-	-
2.2095	11600	0.1029	-	-	-	-	-	-	-
2.2114	11610	0.1168	-	-	-	-	-	-	-
2.2133	11620	0.1275	-	-	-	-	-	-	-
2.2152	11630	0.0886	-	-	-	-	-	-	-
2.2171	11640	0.1261	-	-	-	-	-	-	-
2.2190	11650	0.0996	-	-	-	-	-	-	-
2.2210	11660	0.1407	-	-	-	-	-	-	-
2.2229	11670	0.1142	-	-	-	-	-	-	-
2.2248	11680	0.1001	-	-	-	-	-	-	-
2.2267	11690	0.1225	-	-	-	-	-	-	-
2.2286	11700	0.1394	0.1200	0.7203	0.6512	0.5742	0.3299	0.3434	0.5238
2.2305	11710	0.0865	-	-	-	-	-	-	-
2.2324	11720	0.1186	-	-	-	-	-	-	-
2.2343	11730	0.0934	-	-	-	-	-	-	-
2.2362	11740	0.1018	-	-	-	-	-	-	-
2.2381	11750	0.0956	-	-	-	-	-	-	-
2.24	11760	0.1118	-	-	-	-	-	-	-
2.2419	11770	0.1101	-	-	-	-	-	-	-
2.2438	11780	0.1107	-	-	-	-	-	-	-
2.2457	11790	0.1189	-	-	-	-	-	-	-
2.2476	11800	0.1215	-	-	-	-	-	-	-
2.2495	11810	0.09	-	-	-	-	-	-	-
2.2514	11820	0.1262	-	-	-	-	-	-	-
2.2533	11830	0.1013	-	-	-	-	-	-	-
2.2552	11840	0.1222	-	-	-	-	-	-	-
2.2571	11850	0.0946	-	-	-	-	-	-	-
2.2590	11860	0.1318	-	-	-	-	-	-	-
2.2610	11870	0.0958	-	-	-	-	-	-	-
2.2629	11880	0.1176	-	-	-	-	-	-	-
2.2648	11890	0.1165	-	-	-	-	-	-	-
2.2667	11900	0.1371	-	-	-	-	-	-	-
2.2686	11910	0.1115	-	-	-	-	-	-	-
2.2705	11920	0.1382	-	-	-	-	-	-	-
2.2724	11930	0.0997	-	-	-	-	-	-	-
2.2743	11940	0.0886	-	-	-	-	-	-	-
2.2762	11950	0.0796	-	-	-	-	-	-	-
2.2781	11960	0.1072	-	-	-	-	-	-	-
2.2800	11970	0.0844	-	-	-	-	-	-	-
2.2819	11980	0.1484	-	-	-	-	-	-	-
2.2838	11990	0.135	-	-	-	-	-	-	-
2.2857	12000	0.118	-	-	-	-	-	-	-
2.2876	12010	0.1373	-	-	-	-	-	-	-
2.2895	12020	0.1039	-	-	-	-	-	-	-
2.2914	12030	0.098	-	-	-	-	-	-	-
2.2933	12040	0.1183	-	-	-	-	-	-	-
2.2952	12050	0.1079	-	-	-	-	-	-	-
2.2971	12060	0.1363	-	-	-	-	-	-	-
2.2990	12070	0.1407	-	-	-	-	-	-	-
2.3010	12080	0.1073	-	-	-	-	-	-	-
2.3029	12090	0.0965	-	-	-	-	-	-	-
2.3048	12100	0.1083	-	-	-	-	-	-	-
2.3067	12110	0.0976	-	-	-	-	-	-	-
2.3086	12120	0.1215	-	-	-	-	-	-	-
2.3105	12130	0.1009	-	-	-	-	-	-	-
2.3124	12140	0.0894	-	-	-	-	-	-	-
2.3143	12150	0.1295	-	-	-	-	-	-	-
2.3162	12160	0.095	-	-	-	-	-	-	-
2.3181	12170	0.1283	-	-	-	-	-	-	-
2.32	12180	0.1287	-	-	-	-	-	-	-
2.3219	12190	0.1246	-	-	-	-	-	-	-
2.3238	12200	0.1449	-	-	-	-	-	-	-
2.3257	12210	0.1378	-	-	-	-	-	-	-
2.3276	12220	0.0935	-	-	-	-	-	-	-
2.3295	12230	0.1173	-	-	-	-	-	-	-
2.3314	12240	0.1078	-	-	-	-	-	-	-
2.3333	12250	0.1062	-	-	-	-	-	-	-
2.3352	12260	0.1241	-	-	-	-	-	-	-
2.3371	12270	0.1291	-	-	-	-	-	-	-
2.3390	12280	0.1142	-	-	-	-	-	-	-
2.3410	12290	0.1274	-	-	-	-	-	-	-
2.3429	12300	0.1134	-	-	-	-	-	-	-
2.3448	12310	0.1196	-	-	-	-	-	-	-
2.3467	12320	0.1172	-	-	-	-	-	-	-
2.3486	12330	0.1322	-	-	-	-	-	-	-
2.3505	12340	0.0904	-	-	-	-	-	-	-
2.3524	12350	0.1021	-	-	-	-	-	-	-
2.3543	12360	0.0776	-	-	-	-	-	-	-
2.3562	12370	0.0933	-	-	-	-	-	-	-
2.3581	12380	0.0945	-	-	-	-	-	-	-
2.36	12390	0.1064	-	-	-	-	-	-	-
2.3619	12400	0.131	-	-	-	-	-	-	-
2.3638	12410	0.113	-	-	-	-	-	-	-
2.3657	12420	0.1272	-	-	-	-	-	-	-
2.3676	12430	0.1297	-	-	-	-	-	-	-
2.3695	12440	0.1068	-	-	-	-	-	-	-
2.3714	12450	0.1249	-	-	-	-	-	-	-
2.3733	12460	0.1139	-	-	-	-	-	-	-
2.3752	12470	0.1117	-	-	-	-	-	-	-
2.3771	12480	0.1197	-	-	-	-	-	-	-
2.3790	12490	0.1002	-	-	-	-	-	-	-
2.3810	12500	0.102	-	-	-	-	-	-	-
2.3829	12510	0.0988	-	-	-	-	-	-	-
2.3848	12520	0.1236	-	-	-	-	-	-	-
2.3867	12530	0.1009	-	-	-	-	-	-	-
2.3886	12540	0.1153	-	-	-	-	-	-	-
2.3905	12550	0.1037	-	-	-	-	-	-	-
2.3924	12560	0.1135	-	-	-	-	-	-	-
2.3943	12570	0.1089	-	-	-	-	-	-	-
2.3962	12580	0.123	-	-	-	-	-	-	-
2.3981	12590	0.1247	-	-	-	-	-	-	-
2.4	12600	0.1119	0.1180	0.7021	0.6520	0.5717	0.3359	0.3598	0.5243
2.4019	12610	0.1551	-	-	-	-	-	-	-
2.4038	12620	0.1059	-	-	-	-	-	-	-
2.4057	12630	0.141	-	-	-	-	-	-	-
2.4076	12640	0.1076	-	-	-	-	-	-	-
2.4095	12650	0.0939	-	-	-	-	-	-	-
2.4114	12660	0.1182	-	-	-	-	-	-	-
2.4133	12670	0.1389	-	-	-	-	-	-	-
2.4152	12680	0.1374	-	-	-	-	-	-	-
2.4171	12690	0.0903	-	-	-	-	-	-	-
2.4190	12700	0.1112	-	-	-	-	-	-	-
2.4210	12710	0.1305	-	-	-	-	-	-	-
2.4229	12720	0.1094	-	-	-	-	-	-	-
2.4248	12730	0.0971	-	-	-	-	-	-	-
2.4267	12740	0.1148	-	-	-	-	-	-	-
2.4286	12750	0.0922	-	-	-	-	-	-	-
2.4305	12760	0.1223	-	-	-	-	-	-	-
2.4324	12770	0.1094	-	-	-	-	-	-	-
2.4343	12780	0.1122	-	-	-	-	-	-	-
2.4362	12790	0.1242	-	-	-	-	-	-	-
2.4381	12800	0.1356	-	-	-	-	-	-	-
2.44	12810	0.1128	-	-	-	-	-	-	-
2.4419	12820	0.1162	-	-	-	-	-	-	-
2.4438	12830	0.1172	-	-	-	-	-	-	-
2.4457	12840	0.1064	-	-	-	-	-	-	-
2.4476	12850	0.1212	-	-	-	-	-	-	-
2.4495	12860	0.1016	-	-	-	-	-	-	-
2.4514	12870	0.1265	-	-	-	-	-	-	-
2.4533	12880	0.1187	-	-	-	-	-	-	-
2.4552	12890	0.1071	-	-	-	-	-	-	-
2.4571	12900	0.114	-	-	-	-	-	-	-
2.4590	12910	0.1057	-	-	-	-	-	-	-
2.4610	12920	0.128	-	-	-	-	-	-	-
2.4629	12930	0.1476	-	-	-	-	-	-	-
2.4648	12940	0.0971	-	-	-	-	-	-	-
2.4667	12950	0.0998	-	-	-	-	-	-	-
2.4686	12960	0.0999	-	-	-	-	-	-	-
2.4705	12970	0.111	-	-	-	-	-	-	-
2.4724	12980	0.1057	-	-	-	-	-	-	-
2.4743	12990	0.1027	-	-	-	-	-	-	-
2.4762	13000	0.1248	-	-	-	-	-	-	-
2.4781	13010	0.113	-	-	-	-	-	-	-
2.48	13020	0.1349	-	-	-	-	-	-	-
2.4819	13030	0.1154	-	-	-	-	-	-	-
2.4838	13040	0.1055	-	-	-	-	-	-	-
2.4857	13050	0.0961	-	-	-	-	-	-	-
2.4876	13060	0.1039	-	-	-	-	-	-	-
2.4895	13070	0.1124	-	-	-	-	-	-	-
2.4914	13080	0.1023	-	-	-	-	-	-	-
2.4933	13090	0.0949	-	-	-	-	-	-	-
2.4952	13100	0.107	-	-	-	-	-	-	-
2.4971	13110	0.1124	-	-	-	-	-	-	-
2.4990	13120	0.116	-	-	-	-	-	-	-
2.5010	13130	0.1462	-	-	-	-	-	-	-
2.5029	13140	0.0809	-	-	-	-	-	-	-
2.5048	13150	0.1232	-	-	-	-	-	-	-
2.5067	13160	0.1274	-	-	-	-	-	-	-
2.5086	13170	0.1116	-	-	-	-	-	-	-
2.5105	13180	0.1087	-	-	-	-	-	-	-
2.5124	13190	0.0913	-	-	-	-	-	-	-
2.5143	13200	0.12	-	-	-	-	-	-	-
2.5162	13210	0.107	-	-	-	-	-	-	-
2.5181	13220	0.0847	-	-	-	-	-	-	-
2.52	13230	0.1296	-	-	-	-	-	-	-
2.5219	13240	0.1256	-	-	-	-	-	-	-
2.5238	13250	0.1005	-	-	-	-	-	-	-
2.5257	13260	0.1121	-	-	-	-	-	-	-
2.5276	13270	0.1139	-	-	-	-	-	-	-
2.5295	13280	0.1409	-	-	-	-	-	-	-
2.5314	13290	0.1247	-	-	-	-	-	-	-
2.5333	13300	0.1015	-	-	-	-	-	-	-
2.5352	13310	0.1178	-	-	-	-	-	-	-
2.5371	13320	0.1068	-	-	-	-	-	-	-
2.5390	13330	0.1175	-	-	-	-	-	-	-
2.5410	13340	0.0957	-	-	-	-	-	-	-
2.5429	13350	0.1114	-	-	-	-	-	-	-
2.5448	13360	0.0782	-	-	-	-	-	-	-
2.5467	13370	0.1068	-	-	-	-	-	-	-
2.5486	13380	0.111	-	-	-	-	-	-	-
2.5505	13390	0.0872	-	-	-	-	-	-	-
2.5524	13400	0.1143	-	-	-	-	-	-	-
2.5543	13410	0.1094	-	-	-	-	-	-	-
2.5562	13420	0.1356	-	-	-	-	-	-	-
2.5581	13430	0.099	-	-	-	-	-	-	-
2.56	13440	0.1077	-	-	-	-	-	-	-
2.5619	13450	0.1035	-	-	-	-	-	-	-
2.5638	13460	0.1154	-	-	-	-	-	-	-
2.5657	13470	0.1036	-	-	-	-	-	-	-
2.5676	13480	0.1088	-	-	-	-	-	-	-
2.5695	13490	0.095	-	-	-	-	-	-	-
2.5714	13500	0.1046	0.1164	0.6934	0.6458	0.5786	0.3359	0.3570	0.5221
2.5733	13510	0.1277	-	-	-	-	-	-	-
2.5752	13520	0.1552	-	-	-	-	-	-	-
2.5771	13530	0.1107	-	-	-	-	-	-	-
2.5790	13540	0.103	-	-	-	-	-	-	-
2.5810	13550	0.1061	-	-	-	-	-	-	-
2.5829	13560	0.1261	-	-	-	-	-	-	-
2.5848	13570	0.1226	-	-	-	-	-	-	-
2.5867	13580	0.1016	-	-	-	-	-	-	-
2.5886	13590	0.1066	-	-	-	-	-	-	-
2.5905	13600	0.1027	-	-	-	-	-	-	-
2.5924	13610	0.1089	-	-	-	-	-	-	-
2.5943	13620	0.1129	-	-	-	-	-	-	-
2.5962	13630	0.1132	-	-	-	-	-	-	-
2.5981	13640	0.1141	-	-	-	-	-	-	-
2.6	13650	0.1077	-	-	-	-	-	-	-
2.6019	13660	0.1003	-	-	-	-	-	-	-
2.6038	13670	0.1237	-	-	-	-	-	-	-
2.6057	13680	0.1077	-	-	-	-	-	-	-
2.6076	13690	0.1178	-	-	-	-	-	-	-
2.6095	13700	0.1109	-	-	-	-	-	-	-
2.6114	13710	0.1577	-	-	-	-	-	-	-
2.6133	13720	0.0998	-	-	-	-	-	-	-
2.6152	13730	0.1133	-	-	-	-	-	-	-
2.6171	13740	0.1183	-	-	-	-	-	-	-
2.6190	13750	0.1087	-	-	-	-	-	-	-
2.6210	13760	0.1341	-	-	-	-	-	-	-
2.6229	13770	0.0932	-	-	-	-	-	-	-
2.6248	13780	0.0974	-	-	-	-	-	-	-
2.6267	13790	0.106	-	-	-	-	-	-	-
2.6286	13800	0.1125	-	-	-	-	-	-	-
2.6305	13810	0.1294	-	-	-	-	-	-	-
2.6324	13820	0.1363	-	-	-	-	-	-	-
2.6343	13830	0.1105	-	-	-	-	-	-	-
2.6362	13840	0.1142	-	-	-	-	-	-	-
2.6381	13850	0.1169	-	-	-	-	-	-	-
2.64	13860	0.0916	-	-	-	-	-	-	-
2.6419	13870	0.1444	-	-	-	-	-	-	-
2.6438	13880	0.105	-	-	-	-	-	-	-
2.6457	13890	0.1087	-	-	-	-	-	-	-
2.6476	13900	0.1257	-	-	-	-	-	-	-
2.6495	13910	0.1139	-	-	-	-	-	-	-
2.6514	13920	0.1025	-	-	-	-	-	-	-
2.6533	13930	0.1186	-	-	-	-	-	-	-
2.6552	13940	0.1029	-	-	-	-	-	-	-
2.6571	13950	0.1214	-	-	-	-	-	-	-
2.6590	13960	0.1238	-	-	-	-	-	-	-
2.6610	13970	0.1045	-	-	-	-	-	-	-
2.6629	13980	0.1197	-	-	-	-	-	-	-
2.6648	13990	0.1119	-	-	-	-	-	-	-
2.6667	14000	0.0778	-	-	-	-	-	-	-
2.6686	14010	0.0992	-	-	-	-	-	-	-
2.6705	14020	0.1321	-	-	-	-	-	-	-
2.6724	14030	0.0834	-	-	-	-	-	-	-
2.6743	14040	0.0948	-	-	-	-	-	-	-
2.6762	14050	0.095	-	-	-	-	-	-	-
2.6781	14060	0.1137	-	-	-	-	-	-	-
2.68	14070	0.1232	-	-	-	-	-	-	-
2.6819	14080	0.0945	-	-	-	-	-	-	-
2.6838	14090	0.1173	-	-	-	-	-	-	-
2.6857	14100	0.1048	-	-	-	-	-	-	-
2.6876	14110	0.1239	-	-	-	-	-	-	-
2.6895	14120	0.1093	-	-	-	-	-	-	-
2.6914	14130	0.1248	-	-	-	-	-	-	-
2.6933	14140	0.1005	-	-	-	-	-	-	-
2.6952	14150	0.1101	-	-	-	-	-	-	-
2.6971	14160	0.1204	-	-	-	-	-	-	-
2.6990	14170	0.0829	-	-	-	-	-	-	-
2.7010	14180	0.1163	-	-	-	-	-	-	-
2.7029	14190	0.0943	-	-	-	-	-	-	-
2.7048	14200	0.1186	-	-	-	-	-	-	-
2.7067	14210	0.1029	-	-	-	-	-	-	-
2.7086	14220	0.0959	-	-	-	-	-	-	-
2.7105	14230	0.1085	-	-	-	-	-	-	-
2.7124	14240	0.0993	-	-	-	-	-	-	-
2.7143	14250	0.1101	-	-	-	-	-	-	-
2.7162	14260	0.101	-	-	-	-	-	-	-
2.7181	14270	0.141	-	-	-	-	-	-	-
2.7200	14280	0.1115	-	-	-	-	-	-	-
2.7219	14290	0.1057	-	-	-	-	-	-	-
2.7238	14300	0.1299	-	-	-	-	-	-	-
2.7257	14310	0.1232	-	-	-	-	-	-	-
2.7276	14320	0.1015	-	-	-	-	-	-	-
2.7295	14330	0.1182	-	-	-	-	-	-	-
2.7314	14340	0.0965	-	-	-	-	-	-	-
2.7333	14350	0.1122	-	-	-	-	-	-	-
2.7352	14360	0.0978	-	-	-	-	-	-	-
2.7371	14370	0.0952	-	-	-	-	-	-	-
2.7390	14380	0.1503	-	-	-	-	-	-	-
2.7410	14390	0.1207	-	-	-	-	-	-	-
2.7429	14400	0.0953	0.1156	0.7149	0.6432	0.5802	0.3361	0.3488	0.5246

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.1.1
Transformers: 4.56.2
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.1.1
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

SpladeLoss

@misc{formal2022distillationhardnegativesampling,
      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
      year={2022},
      eprint={2205.04733},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2205.04733},
}

SparseMultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

FlopsLoss

@article{paria2020minimizing,
    title={Minimizing flops to learn efficient sparse representations},
    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
    journal={arXiv preprint arXiv:2004.05665},
    year={2020}
}

Downloads last month: 2

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for drexalt/splade-NeoBERT-msmarco-triplets-muon

Base model

chandar-lab/NeoBERT

Finetuned

drexalt/NeoBERT-RetroMAE-pretrain

Finetuned

(2)

this model

Dataset used to train drexalt/splade-NeoBERT-msmarco-triplets-muon

Papers for drexalt/splade-NeoBERT-msmarco-triplets-muon

From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective

Paper • 2205.04733 • Published May 10, 2022 • 3

Evaluation results

Dot Accuracy@1 on NanoSciFact
self-reported

0.600
Dot Accuracy@3 on NanoSciFact
self-reported

0.740
Dot Accuracy@5 on NanoSciFact
self-reported

0.820
Dot Accuracy@10 on NanoSciFact
self-reported

0.860
Dot Precision@1 on NanoSciFact
self-reported

0.600
Dot Precision@3 on NanoSciFact
self-reported

0.260
Dot Precision@5 on NanoSciFact
self-reported

0.176
Dot Precision@10 on NanoSciFact
self-reported

0.094
Dot Recall@1 on NanoSciFact
self-reported

0.575
Dot Recall@3 on NanoSciFact
self-reported

0.705
Dot Recall@5 on NanoSciFact
self-reported

0.795
Dot Recall@10 on NanoSciFact
self-reported

0.840
Dot Ndcg@10 on NanoSciFact
self-reported

0.715
Dot Mrr@10 on NanoSciFact
self-reported

0.684
Dot Map@100 on NanoSciFact
self-reported

0.674
Query Active Dims on NanoSciFact
self-reported

72.840
Query Sparsity Ratio on NanoSciFact
self-reported

0.998
Corpus Active Dims on NanoSciFact
self-reported

197.205
Corpus Sparsity Ratio on NanoSciFact
self-reported

0.994
Avg Flops on NanoSciFact
self-reported

8.402