iambestfeed/halong_colbert

PyLate model based on hiieu/halong_embedding

This is a PyLate model finetuned from hiieu/halong_embedding on the msmacro-vi dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Model Details

Model Description

• Model Type: PyLate model
• Base model: hiieu/halong_embedding
• Document Length: 180 tokens
• Query Length: 32 tokens
• Output Dimensionality: 128 tokens
• Similarity Function: MaxSim
• Training Dataset:
  • msmacro-vi

Model Sources

• Documentation: PyLate Documentation
• Repository: PyLate on GitHub
• Hugging Face: PyLate models on Hugging Face

Full Model Architecture

ColBERT(
  (0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.

Indexing documents

First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path=iambestfeed/halong_colbert,
)

# Step 2: Initialize the Voyager index
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path=iambestfeed/halong_colbert,
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Training Details

Training Dataset

msmacro-vi

• Dataset: msmacro-vi at ed2394d
• Size: 200,000 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	836: ~0.10% 3582: ~0.10% 4599: ~0.10% 4645: ~0.10% 4853: ~0.10% 5154: ~0.10% 7504: ~0.10% 12283: ~0.10% 12335: ~0.10% 12916: ~0.10% 14049: ~0.10% 14828: ~0.10% 15674: ~0.10% 15813: ~0.10% 16728: ~0.10% 22006: ~0.10% 23675: ~0.10% 24199: ~0.10% 25323: ~0.10% 28517: ~0.10% 29213: ~0.10% 32344: ~0.10% 34071: ~0.10% 34604: ~0.10% 35424: ~0.10% 35445: ~0.10% 36148: ~0.10% 37078: ~0.10% 37826: ~0.10% 38185: ~0.10% 40855: ~0.10% 42077: ~0.10% 43614: ~0.10% 45073: ~0.10% 46289: ~0.10% 47507: ~0.10% 48005: ~0.10% 48629: ~0.10% 48785: ~0.10% 49216: ~0.10% 49636: ~0.10% 49970: ~0.10% 52075: ~0.10% 52725: ~0.10% 54142: ~0.10% 54210: ~0.10% 55032: ~0.10% 59546: ~0.10% 60087: ~0.10% 60862: ~0.10% 60941: ~0.10% 61037: ~0.10% 61762: ~0.10% 62649: ~0.10% 63333: ~0.10% 64197: ~0.10% 64879: ~0.10% 67608: ~0.10% 67627: ~0.10% 69463: ~0.10% 70002: ~0.10% 70429: ~0.10% 72166: ~0.10% 72518: ~0.10% 72607: ~0.10% 72791: ~0.10% 73325: ~0.10% 74078: ~0.10% 74857: ~0.10% 75323: ~0.10% 75816: ~0.10% 76929: ~0.10% 77845: ~0.10% 77889: ~0.10% 78077: ~0.10% 78256: ~0.10% 78401: ~0.10% 78798: ~0.10% 80871: ~0.10% 81089: ~0.10% 82179: ~0.10% 82883: ~0.10% 84168: ~0.10% 86891: ~0.10% 88282: ~0.10% 89346: ~0.10% 89386: ~0.10% 90699: ~0.10% 90795: ~0.10% 91367: ~0.10% 91795: ~0.10% 92070: ~0.10% 92523: ~0.10% 92597: ~0.10% 92753: ~0.10% 92787: ~0.10% 96382: ~0.10% 96455: ~0.10% 97274: ~0.10% 97603: ~0.10% 100904: ~0.10% 101205: ~0.10% 101305: ~0.10% 102707: ~0.10% 103074: ~0.10% 105437: ~0.10% 108207: ~0.10% 109776: ~0.10% 112056: ~0.10% 112955: ~0.10% 112977: ~0.10% 113635: ~0.10% 115280: ~0.10% 115551: ~0.10% 116098: ~0.10% 117658: ~0.10% 120255: ~0.10% 120298: ~0.10% 121437: ~0.10% 123429: ~0.10% 125043: ~0.10% 125979: ~0.10% 126851: ~0.10% 128218: ~0.10% 128804: ~0.10% 129598: ~0.10% 131299: ~0.10% 132114: ~0.10% 133696: ~0.10% 134460: ~0.10% 137602: ~0.10% 137679: ~0.10% 138121: ~0.10% 138260: ~0.10% 138823: ~0.10% 139039: ~0.10% 140392: ~0.10% 140651: ~0.10% 142305: ~0.10% 145653: ~0.10% 145683: ~0.10% 145763: ~0.10% 150202: ~0.10% 151135: ~0.10% 152307: ~0.10% 152675: ~0.10% 153693: ~0.10% 154470: ~0.10% 155587: ~0.10% 157602: ~0.10% 157779: ~0.10% 158565: ~0.10% 159177: ~0.10% 159224: ~0.10% 159341: ~0.10% 159892: ~0.10% 161881: ~0.10% 162414: ~0.10% 163765: ~0.10% 165888: ~0.10% 168048: ~0.10% 168425: ~0.10% 168894: ~0.10% 169991: ~0.10% 170731: ~0.10% 171705: ~0.10% 176165: ~0.10% 176798: ~0.10% 180259: ~0.10% 181243: ~0.10% 182102: ~0.10% 182660: ~0.10% 183426: ~0.10% 183930: ~0.10% 184045: ~0.10% 184676: ~0.10% 185294: ~0.10% 186475: ~0.10% 187155: ~0.10% 188198: ~0.10% 191383: ~0.10% 192165: ~0.10% 193507: ~0.10% 194207: ~0.10% 195056: ~0.10% 197377: ~0.10% 198224: ~0.10% 198546: ~0.10% 202122: ~0.10% 203519: ~0.10% 206220: ~0.10% 209739: ~0.10% 210554: ~0.10% 212638: ~0.10% 213096: ~0.10% 213410: ~0.10% 214255: ~0.10% 217541: ~0.10% 219718: ~0.10% 220993: ~0.10% 223241: ~0.10% 224657: ~0.10% 227101: ~0.10% 227497: ~0.10% 227726: ~0.10% 228099: ~0.10% 228451: ~0.10% 230413: ~0.10% 231416: ~0.10% 233312: ~0.10% 234348: ~0.10% 235869: ~0.10% 237784: ~0.10% 240739: ~0.10% 246495: ~0.10% 246821: ~0.10% 248675: ~0.10% 249798: ~0.10% 249962: ~0.10% 249977: ~0.10% 250019: ~0.10% 250548: ~0.10% 251089: ~0.10% 254878: ~0.10% 255183: ~0.10% 255727: ~0.10% 256321: ~0.10% 258276: ~0.10% 260993: ~0.10% 261247: ~0.10% 262123: ~0.10% 262508: ~0.10% 266047: ~0.10% 267089: ~0.10% 267192: ~0.10% 268642: ~0.10% 269025: ~0.10% 273171: ~0.10% 273864: ~0.10% 274521: ~0.10% 274586: ~0.10% 275037: ~0.10% 275643: ~0.10% 276744: ~0.10% 277212: ~0.10% 277990: ~0.10% 279931: ~0.10% 280012: ~0.10% 281699: ~0.10% 282128: ~0.10% 283298: ~0.10% 284268: ~0.10% 285697: ~0.10% 285905: ~0.10% 287456: ~0.10% 287506: ~0.10% 288154: ~0.10% 289046: ~0.10% 292211: ~0.10% 292588: ~0.10% 293357: ~0.10% 293661: ~0.10% 294123: ~0.10% 299287: ~0.10% 300622: ~0.10% 302135: ~0.10% 303224: ~0.10% 304353: ~0.10% 304820: ~0.10% 310215: ~0.10% 310236: ~0.10% 310409: ~0.10% 311231: ~0.10% 312821: ~0.10% 314244: ~0.10% 314415: ~0.10% 314745: ~0.10% 316385: ~0.10% 316883: ~0.10% 317442: ~0.10% 318639: ~0.10% 318652: ~0.10% 320855: ~0.10% 321867: ~0.10% 322114: ~0.10% 323196: ~0.10% 324868: ~0.10% 327581: ~0.10% 329337: ~0.10% 331572: ~0.10% 331650: ~0.10% 331993: ~0.10% 332500: ~0.10% 334757: ~0.10% 336561: ~0.10% 336791: ~0.10% 337002: ~0.10% 338332: ~0.10% 338456: ~0.10% 339065: ~0.10% 339870: ~0.10% 340599: ~0.10% 341156: ~0.10% 342121: ~0.10% 342381: ~0.10% 343411: ~0.10% 344860: ~0.10% 345924: ~0.10% 346421: ~0.10% 346425: ~0.10% 348157: ~0.10% 351281: ~0.10% 351858: ~0.10% 352641: ~0.10% 353748: ~0.10% 357399: ~0.10% 359787: ~0.10% 359893: ~0.10% 360094: ~0.10% 360168: ~0.10% 361127: ~0.10% 362220: ~0.10% 362560: ~0.10% 366835: ~0.10% 367185: ~0.10% 369045: ~0.10% 371113: ~0.10% 376044: ~0.10% 376524: ~0.10% 377231: ~0.10% 377735: ~0.10% 378574: ~0.10% 379749: ~0.10% 379953: ~0.10% 381834: ~0.10% 384039: ~0.10% 384364: ~0.10% 384398: ~0.10% 384751: ~0.10% 385758: ~0.10% 385893: ~0.10% 386098: ~0.10% 387205: ~0.10% 387374: ~0.10% 388450: ~0.10% 388589: ~0.10% 388593: ~0.10% 389571: ~0.10% 389572: ~0.10% 391531: ~0.10% 391857: ~0.10% 393174: ~0.10% 393426: ~0.10% 396601: ~0.10% 396905: ~0.10% 397801: ~0.10% 398011: ~0.10% 398132: ~0.10% 398721: ~0.10% 399016: ~0.10% 401601: ~0.10% 403876: ~0.10% 403897: ~0.10% 404830: ~0.10% 406102: ~0.10% 406397: ~0.10% 407151: ~0.10% 409373: ~0.10% 410084: ~0.10% 410859: ~0.10% 411693: ~0.10% 411984: ~0.10% 412214: ~0.10% 412560: ~0.10% 413117: ~0.10% 416391: ~0.10% 417066: ~0.10% 417198: ~0.10% 417751: ~0.10% 417778: ~0.10% 420257: ~0.10% 420787: ~0.10% 421001: ~0.10% 421045: ~0.10% 421354: ~0.10% 428114: ~0.10% 429057: ~0.10% 429459: ~0.10% 430319: ~0.10% 431215: ~0.10% 431332: ~0.10% 431488: ~0.10% 432097: ~0.10% 432283: ~0.10% 434131: ~0.10% 434934: ~0.10% 435353: ~0.10% 437793: ~0.10% 438297: ~0.10% 438806: ~0.10% 439016: ~0.10% 439129: ~0.10% 439217: ~0.10% 439755: ~0.10% 440343: ~0.10% 440506: ~0.10% 441030: ~0.10% 441509: ~0.10% 443408: ~0.10% 443686: ~0.10% 445516: ~0.10% 445999: ~0.10% 447039: ~0.10% 447219: ~0.10% 447298: ~0.10% 453040: ~0.10% 453745: ~0.10% 454869: ~0.10% 456224: ~0.10% 456251: ~0.10% 457065: ~0.10% 459890: ~0.10% 460010: ~0.10% 463716: ~0.10% 465235: ~0.10% 470470: ~0.10% 471875: ~0.10% 472462: ~0.10% 474016: ~0.10% 479266: ~0.10% 479360: ~0.10% 480621: ~0.10% 483014: ~0.10% 484553: ~0.10% 485031: ~0.10% 485828: ~0.10% 486664: ~0.10% 488266: ~0.10% 489488: ~0.10% 490992: ~0.10% 491894: ~0.10% 491983: ~0.10% 492620: ~0.10% 493035: ~0.10% 493461: ~0.10% 494255: ~0.10% 496473: ~0.10% 496474: ~0.10% 496516: ~0.10% 496813: ~0.10% 496853: ~0.10% 499553: ~0.10% 499565: ~0.10% 499737: ~0.10% 500057: ~0.10% 500546: ~0.10% 501510: ~0.10% 501978: ~0.10% 503905: ~0.10% 510559: ~0.10% 511473: ~0.10% 512440: ~0.10% 513832: ~0.10% 514106: ~0.10% 514902: ~0.10% 515053: ~0.10% 515507: ~0.10% 516205: ~0.10% 517903: ~0.10% 518096: ~0.10% 520796: ~0.10% 521570: ~0.10% 522112: ~0.10% 523814: ~0.10% 525505: ~0.10% 525583: ~0.10% 525764: ~0.10% 528105: ~0.10% 530985: ~0.10% 532014: ~0.10% 534952: ~0.10% 538836: ~0.10% 539326: ~0.10% 539504: ~0.10% 541861: ~0.10% 542925: ~0.10% 543525: ~0.10% 544853: ~0.10% 545091: ~0.10% 546527: ~0.10% 546753: ~0.10% 548007: ~0.10% 548100: ~0.10% 554548: ~0.10% 555064: ~0.10% 560255: ~0.10% 560711: ~0.10% 561084: ~0.10% 561114: ~0.10% 561329: ~0.10% 561838: ~0.10% 561946: ~0.10% 564894: ~0.10% 566884: ~0.10% 568110: ~0.10% 569541: ~0.10% 570881: ~0.10% 571286: ~0.10% 571515: ~0.10% 571577: ~0.10% 572354: ~0.10% 573015: ~0.10% 573283: ~0.10% 577767: ~0.10% 578249: ~0.10% 578805: ~0.10% 580872: ~0.10% 581072: ~0.10% 581684: ~0.10% 582341: ~0.10% 583169: ~0.10% 583322: ~0.10% 583889: ~0.10% 584173: ~0.10% 585406: ~0.10% 585523: ~0.10% 585660: ~0.10% 587005: ~0.10% 587399: ~0.10% 588010: ~0.10% 588337: ~0.10% 590946: ~0.10% 593319: ~0.10% 595246: ~0.10% 597157: ~0.10% 597215: ~0.10% 597368: ~0.10% 597453: ~0.10% 598538: ~0.10% 601120: ~0.10% 604762: ~0.10% 605111: ~0.10% 605547: ~0.10% 606244: ~0.10% 606935: ~0.10% 607099: ~0.10% 609731: ~0.10% 609910: ~0.10% 610485: ~0.10% 613040: ~0.10% 614720: ~0.10% 615525: ~0.10% 616416: ~0.10% 618280: ~0.10% 619151: ~0.10% 619170: ~0.10% 622593: ~0.10% 622755: ~0.10% 623529: ~0.10% 625333: ~0.10% 625780: ~0.10% 626317: ~0.10% 626670: ~0.10% 628299: ~0.10% 628510: ~0.10% 629166: ~0.10% 630995: ~0.10% 632641: ~0.10% 634324: ~0.10% 634750: ~0.10% 636542: ~0.10% 637420: ~0.10% 641046: ~0.10% 643232: ~0.10% 643901: ~0.10% 644517: ~0.10% 645962: ~0.10% 647293: ~0.10% 647443: ~0.10% 648173: ~0.10% 649204: ~0.10% 650521: ~0.10% 651961: ~0.10% 652493: ~0.10% 655888: ~0.10% 656535: ~0.10% 658715: ~0.10% 659035: ~0.10% 659593: ~0.10% 660535: ~0.10% 662154: ~0.10% 662784: ~0.10% 663142: ~0.10% 666319: ~0.10% 666386: ~0.10% 666561: ~0.10% 668151: ~0.10% 668862: ~0.10% 670341: ~0.10% 671801: ~0.10% 673081: ~0.10% 673634: ~0.10% 673875: ~0.10% 673881: ~0.10% 674082: ~0.10% 675319: ~0.10% 675492: ~0.10% 676147: ~0.10% 676238: ~0.10% 676318: ~0.10% 676431: ~0.10% 677459: ~0.10% 678468: ~0.10% 679216: ~0.10% 679307: ~0.10% 680354: ~0.10% 681098: ~0.10% 681873: ~0.10% 684800: ~0.10% 685613: ~0.10% 685690: ~0.10% 686886: ~0.10% 689687: ~0.10% 689748: ~0.10% 694425: ~0.10% 694466: ~0.10% 698130: ~0.10% 702137: ~0.10% 703138: ~0.10% 704067: ~0.10% 704460: ~0.10% 705420: ~0.10% 706199: ~0.10% 706878: ~0.10% 708333: ~0.10% 710580: ~0.10% 710897: ~0.10% 713539: ~0.10% 713584: ~0.10% 714733: ~0.10% 718172: ~0.10% 719545: ~0.10% 719580: ~0.10% 720471: ~0.10% 720690: ~0.10% 722394: ~0.10% 723568: ~0.10% 724334: ~0.10% 724700: ~0.10% 727908: ~0.10% 728088: ~0.10% 729096: ~0.10% 730499: ~0.10% 730711: ~0.10% 733963: ~0.10% 734912: ~0.10% 736431: ~0.10% 738012: ~0.10% 738173: ~0.10% 739026: ~0.10% 739605: ~0.10% 740181: ~0.10% 742066: ~0.10% 742298: ~0.10% 745799: ~0.10% 748392: ~0.10% 748838: ~0.10% 749148: ~0.10% 751762: ~0.10% 752092: ~0.10% 752527: ~0.10% 753568: ~0.10% 755386: ~0.10% 756558: ~0.10% 756736: ~0.10% 758706: ~0.10% 759523: ~0.10% 760550: ~0.10% 762688: ~0.10% 762918: ~0.10% 763569: ~0.10% 763766: ~0.10% 765769: ~0.10% 766789: ~0.10% 768119: ~0.10% 768537: ~0.10% 773106: ~0.10% 775589: ~0.10% 775964: ~0.10% 776055: ~0.10% 777088: ~0.10% 777529: ~0.10% 778375: ~0.10% 781066: ~0.10% 782328: ~0.10% 783231: ~0.10% 784413: ~0.10% 785781: ~0.10% 786250: ~0.10% 786845: ~0.10% 788012: ~0.10% 791857: ~0.10% 792788: ~0.10% 793182: ~0.10% 794187: ~0.10% 794308: ~0.10% 794318: ~0.10% 796097: ~0.10% 796117: ~0.10% 797182: ~0.10% 798215: ~0.10% 802050: ~0.10% 802669: ~0.10% 804168: ~0.10% 804253: ~0.10% 804461: ~0.10% 805743: ~0.10% 808416: ~0.10% 808455: ~0.10% 810577: ~0.10% 811702: ~0.10% 811843: ~0.10% 815923: ~0.10% 816475: ~0.10% 818312: ~0.10% 818521: ~0.10% 819278: ~0.10% 820890: ~0.10% 821615: ~0.10% 823136: ~0.10% 823735: ~0.10% 829476: ~0.10% 830591: ~0.10% 832433: ~0.10% 832597: ~0.10% 833053: ~0.10% 835043: ~0.10% 835759: ~0.10% 837731: ~0.10% 837942: ~0.10% 839448: ~0.10% 840228: ~0.10% 840417: ~0.10% 841851: ~0.10% 843327: ~0.10% 843622: ~0.10% 844870: ~0.10% 846084: ~0.10% 846807: ~0.10% 847076: ~0.10% 847535: ~0.10% 847977: ~0.10% 848075: ~0.10% 848326: ~0.10% 852725: ~0.10% 853465: ~0.10% 856427: ~0.10% 857186: ~0.10% 858377: ~0.10% 858543: ~0.10% 860426: ~0.10% 863804: ~0.10% 866039: ~0.10% 866406: ~0.10% 867180: ~0.10% 868280: ~0.10% 872156: ~0.10% 872791: ~0.10% 872953: ~0.10% 872959: ~0.10% 875015: ~0.10% 876522: ~0.10% 878407: ~0.10% 878710: ~0.10% 878855: ~0.10% 880495: ~0.10% 882732: ~0.10% 884335: ~0.10% 884941: ~0.10% 885893: ~0.10% 886713: ~0.10% 887068: ~0.10% 887751: ~0.10% 888027: ~0.10% 890152: ~0.10% 891137: ~0.10% 891890: ~0.10% 892662: ~0.10% 892973: ~0.10% 893360: ~0.10% 893915: ~0.10% 893976: ~0.10% 894324: ~0.10% 895709: ~0.10% 897065: ~0.10% 898387: ~0.10% 899291: ~0.10% 899604: ~0.10% 900513: ~0.10% 900619: ~0.10% 901170: ~0.10% 902794: ~0.10% 903238: ~0.10% 904294: ~0.10% 904520: ~0.10% 904992: ~0.10% 907212: ~0.10% 908062: ~0.10% 908561: ~0.10% 911034: ~0.10% 911982: ~0.10% 913716: ~0.10% 914819: ~0.10% 915750: ~0.10% 915766: ~0.10% 916125: ~0.10% 916648: ~0.10% 917285: ~0.10% 918194: ~0.10% 926035: ~0.10% 927726: ~0.10% 929821: ~0.10% 930300: ~0.10% 930796: ~0.10% 931617: ~0.10% 932719: ~0.10% 933784: ~0.10% 934378: ~0.10% 935900: ~0.10% 936118: ~0.10% 936336: ~0.10% 937231: ~0.10% 938420: ~0.10% 939184: ~0.10% 939567: ~0.10% 941588: ~0.10% 944093: ~0.10% 944912: ~0.10% 945069: ~0.10% 945659: ~0.10% 946110: ~0.10% 950044: ~0.10% 954101: ~0.10% 954147: ~0.10% 958697: ~0.10% 959530: ~0.10% 961721: ~0.10% 963582: ~0.10% 964471: ~0.10% 965026: ~0.10% 966573: ~0.10% 967330: ~0.10% 968346: ~0.10% 970649: ~0.10% 970873: ~0.10% 971636: ~0.10% 971664: ~0.10% 973555: ~0.10% 973851: ~0.10% 974207: ~0.10% 976896: ~0.10% 981402: ~0.10% 983723: ~0.10% 984358: ~0.10% 984653: ~0.10% 987107: ~0.10% 987167: ~0.10% 994360: ~0.10% 995049: ~0.10% 1002688: ~0.10% 1004305: ~0.10% 1004650: ~0.10% 1004849: ~0.10% 1005118: ~0.10% 1005614: ~0.10% 1005626: ~0.10% 1005669: ~0.10% 1006835: ~0.10% 1011008: ~0.10% 1012299: ~0.10% 1014010: ~0.10% 1014030: ~0.10% 1016549: ~0.10% 1017016: ~0.10% 1017335: ~0.10% 1018386: ~0.10% 1020640: ~0.10% 1021041: ~0.10% 1021411: ~0.10% 1025341: ~0.10% 1025423: ~0.10% 1025767: ~0.10% 1026066: ~0.10% 1026434: ~0.10% 1027516: ~0.10% 1027703: ~0.10% 1028119: ~0.10% 1028642: ~0.10% 1031554: ~0.10% 1032300: ~0.10% 1033639: ~0.10% 1033660: ~0.10% 1034832: ~0.10% 1035274: ~0.10% 1037432: ~0.10% 1037536: ~0.10% 1037759: ~0.10% 1039860: ~0.10% 1041131: ~0.10% 1041892: ~0.10% 1043066: ~0.10% 1044326: ~0.10% 1044905: ~0.10% 1047848: ~0.10% 1048534: ~0.10% 1049477: ~0.10% 1050531: ~0.10% 1052073: ~0.10% 1052617: ~0.10% 1054049: ~0.10% 1055142: ~0.10% 1056933: ~0.10% 1057358: ~0.10% 1057911: ~0.10% 1061411: ~0.10% 1062328: ~0.10% 1062485: ~0.10% 1062534: ~0.10% 1062794: ~0.10% 1063269: ~0.10% 1063467: ~0.10% 1064568: ~0.10% 1064868: ~0.10% 1065481: ~0.10% 1065565: ~0.10% 1067970: ~0.10% 1068014: ~0.10% 1070203: ~0.10% 1070708: ~0.10% 1072038: ~0.10% 1072214: ~0.10% 1074885: ~0.10% 1075308: ~0.10% 1078872: ~0.10% 1078979: ~0.10% 1079266: ~0.10% 1079736: ~0.10% 1080075: ~0.10% 1081716: ~0.10% 1137391: ~0.10% 1138530: ~0.10% 1139697: ~0.10% 1140119: ~0.10% 1140869: ~0.10% 1141527: ~0.10% 1144693: ~0.10% 1145425: ~0.10% 1149162: ~0.10% 1149207: ~0.10% 1150086: ~0.10% 1150398: ~0.10% 1150731: ~0.10% 1151256: ~0.10% 1151403: ~0.10% 1152236: ~0.10% 1153693: ~0.10% 1155859: ~0.10% 1156918: ~0.10% 1158007: ~0.10% 1158559: ~0.10% 1158952: ~0.10% 1159165: ~0.10% 1161242: ~0.10% 1163227: ~0.10% 1166023: ~0.10% 1166231: ~0.10% 1167002: ~0.10% 1169844: ~0.10% 1170663: ~0.10% 1171580: ~0.10% 1172072: ~0.10% 1172083: ~0.10% 1173371: ~0.10% 1173809: ~0.10% 1174049: ~0.10% 1175044: ~0.10% 1175745: ~0.10% 1176061: ~0.10% 1176414: ~0.10% 1176993: ~0.10% 1177449: ~0.10% 1178311: ~0.10% 1179029: ~0.10% 1179069: ~0.10% 1180579: ~0.10% 1181077: ~0.10% 1183293: ~0.10% 1184313: ~0.10% 1185090: ~0.10% 1185669: ~0.10%	size: 32 elements	size: 32 elements

• Samples:

  query_id	document_ids	scores
  685613	[7546874, 1176459, 197677, 2306318, 8541504, ...]	[0.9999999992804947, 0.24845418756716053, 0.7594154013647826, 0.26644182105618575, 0.390668914839766, ...]
  237784	[6366584, 4034101, 2325374, 6914618, 6042146, ...]	[0.9999999991784339, 0.42233632827946693, 0.5956354295491569, 0.12644415907455164, 0.6636713730105909, ...]
  904294	[448408, 8743975, 49600, 7339401, 2714261, ...]	[0.9999999991841937, 0.877629062381539, 0.8330146583389045, 0.3116634796692611, 0.4633524534142185, ...]

• Loss: pylate.losses.distillation.Distillation

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 4
• learning_rate: 1e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• save_safetensors: False
• fp16: True
• push_to_hub: True
• hub_model_id: iambestfeed/halong_colbert

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 1e-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: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: False
• 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: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 1
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: True
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• 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}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• 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: True
• resume_from_checkpoint: None
• hub_model_id: iambestfeed/halong_colbert
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• 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
• dispatch_batches: None
• split_batches: 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
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0004	10	0.0329
0.0008	20	0.0301
0.0012	30	0.0312
0.0016	40	0.0313
0.002	50	0.0346
0.0024	60	0.0315
0.0028	70	0.0329
0.0032	80	0.0325
0.0036	90	0.0309
0.004	100	0.0316
0.0044	110	0.032
0.0048	120	0.0304
0.0052	130	0.0309
0.0056	140	0.035
0.006	150	0.0301
0.0064	160	0.0323
0.0068	170	0.0293
0.0072	180	0.0326
0.0076	190	0.0339
0.008	200	0.0335
0.0084	210	0.0329
0.0088	220	0.0294
0.0092	230	0.0311
0.0096	240	0.0292
0.01	250	0.0313
0.0104	260	0.0307
0.0108	270	0.0293
0.0112	280	0.0281
0.0116	290	0.0289
0.012	300	0.03
0.0124	310	0.0304
0.0128	320	0.0292
0.0132	330	0.0304
0.0136	340	0.0304
0.014	350	0.0277
0.0144	360	0.0306
0.0148	370	0.0304
0.0152	380	0.0304
0.0156	390	0.0284
0.016	400	0.0295
0.0164	410	0.0276
0.0168	420	0.0282
0.0172	430	0.0282
0.0176	440	0.0305
0.018	450	0.0284
0.0184	460	0.0284
0.0188	470	0.0327
0.0192	480	0.0294
0.0196	490	0.0291
0.02	500	0.0274
0.0204	510	0.0276
0.0208	520	0.027
0.0212	530	0.0273
0.0216	540	0.0267
0.022	550	0.033
0.0224	560	0.03
0.0228	570	0.0314
0.0232	580	0.0287
0.0236	590	0.0257
0.024	600	0.0292
0.0244	610	0.0282
0.0248	620	0.0285
0.0252	630	0.028
0.0256	640	0.0299
0.026	650	0.0291
0.0264	660	0.0255
0.0268	670	0.0275
0.0272	680	0.0281
0.0276	690	0.0306
0.028	700	0.0269
0.0284	710	0.0283
0.0288	720	0.027
0.0292	730	0.0272
0.0296	740	0.0277
0.03	750	0.0279
0.0304	760	0.0287
0.0308	770	0.0274
0.0312	780	0.0289
0.0316	790	0.0253
0.032	800	0.0288
0.0324	810	0.0288
0.0328	820	0.029
0.0332	830	0.0266
0.0336	840	0.0247
0.034	850	0.0281
0.0344	860	0.0277
0.0348	870	0.0274
0.0352	880	0.0251
0.0356	890	0.0269
0.036	900	0.0277
0.0364	910	0.0258
0.0368	920	0.0275
0.0372	930	0.0274
0.0376	940	0.0271
0.038	950	0.0291
0.0384	960	0.0284
0.0388	970	0.0283
0.0392	980	0.0294
0.0396	990	0.0266
0.04	1000	0.0266
0.0404	1010	0.0271
0.0408	1020	0.0294
0.0412	1030	0.0281
0.0416	1040	0.0273
0.042	1050	0.0284
0.0424	1060	0.0283
0.0428	1070	0.0244
0.0432	1080	0.0273
0.0436	1090	0.0267
0.044	1100	0.0264
0.0444	1110	0.0269
0.0448	1120	0.0281
0.0452	1130	0.0256
0.0456	1140	0.0271
0.046	1150	0.027
0.0464	1160	0.0278
0.0468	1170	0.0261
0.0472	1180	0.0263
0.0476	1190	0.0272
0.048	1200	0.0268
0.0484	1210	0.0283
0.0488	1220	0.0261
0.0492	1230	0.0259
0.0496	1240	0.0282
0.05	1250	0.0258
0.0504	1260	0.028
0.0508	1270	0.0253
0.0512	1280	0.0274
0.0516	1290	0.0259
0.052	1300	0.0254
0.0524	1310	0.0265
0.0528	1320	0.0235
0.0532	1330	0.0268
0.0536	1340	0.0291
0.054	1350	0.028
0.0544	1360	0.0259
0.0548	1370	0.0284
0.0552	1380	0.0268
0.0556	1390	0.0279
0.056	1400	0.0284
0.0564	1410	0.0265
0.0568	1420	0.0276
0.0572	1430	0.0278
0.0576	1440	0.0264
0.058	1450	0.025
0.0584	1460	0.0243
0.0588	1470	0.0262
0.0592	1480	0.0292
0.0596	1490	0.0279
0.06	1500	0.0274
0.0604	1510	0.0261
0.0608	1520	0.0271
0.0612	1530	0.0268
0.0616	1540	0.0249
0.062	1550	0.0294
0.0624	1560	0.0252
0.0628	1570	0.0282
0.0632	1580	0.0246
0.0636	1590	0.0267
0.064	1600	0.0264
0.0644	1610	0.027
0.0648	1620	0.0262
0.0652	1630	0.0287
0.0656	1640	0.0257
0.066	1650	0.0244
0.0664	1660	0.027
0.0668	1670	0.0246
0.0672	1680	0.0273
0.0676	1690	0.026
0.068	1700	0.0264
0.0684	1710	0.0278
0.0688	1720	0.0243
0.0692	1730	0.0227
0.0696	1740	0.0268
0.07	1750	0.0227
0.0704	1760	0.0248
0.0708	1770	0.0272
0.0712	1780	0.0238
0.0716	1790	0.0262
0.072	1800	0.0255
0.0724	1810	0.0272
0.0728	1820	0.0246
0.0732	1830	0.0279
0.0736	1840	0.0265
0.074	1850	0.0264
0.0744	1860	0.0253
0.0748	1870	0.0255
0.0752	1880	0.0264
0.0756	1890	0.0253
0.076	1900	0.0265
0.0764	1910	0.0258
0.0768	1920	0.0265
0.0772	1930	0.0252
0.0776	1940	0.0234
0.078	1950	0.026
0.0784	1960	0.0264
0.0788	1970	0.0268
0.0792	1980	0.0258
0.0796	1990	0.0253
0.08	2000	0.0254
0.0804	2010	0.0271
0.0808	2020	0.0242
0.0812	2030	0.0265
0.0816	2040	0.0261
0.082	2050	0.0271
0.0824	2060	0.0236
0.0828	2070	0.0258
0.0832	2080	0.0231
0.0836	2090	0.0273
0.084	2100	0.0231
0.0844	2110	0.0255
0.0848	2120	0.0243
0.0852	2130	0.0248
0.0856	2140	0.0229
0.086	2150	0.0225
0.0864	2160	0.0237
0.0868	2170	0.0241
0.0872	2180	0.025
0.0876	2190	0.0254
0.088	2200	0.0234
0.0884	2210	0.0224
0.0888	2220	0.0248
0.0892	2230	0.0239
0.0896	2240	0.025
0.09	2250	0.0246
0.0904	2260	0.0219
0.0908	2270	0.0248
0.0912	2280	0.0233
0.0916	2290	0.0243
0.092	2300	0.0237
0.0924	2310	0.0243
0.0928	2320	0.0256
0.0932	2330	0.0238
0.0936	2340	0.0222
0.094	2350	0.0245
0.0944	2360	0.0242
0.0948	2370	0.0247
0.0952	2380	0.0251
0.0956	2390	0.0225
0.096	2400	0.0259
0.0964	2410	0.0236
0.0968	2420	0.0226
0.0972	2430	0.0231
0.0976	2440	0.0237
0.098	2450	0.0219
0.0984	2460	0.0236
0.0988	2470	0.0211
0.0992	2480	0.0249
0.0996	2490	0.0245
0.1	2500	0.0239
0.1004	2510	0.0249
0.1008	2520	0.0223
0.1012	2530	0.0208
0.1016	2540	0.026
0.102	2550	0.0227
0.1024	2560	0.0238
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0.1032	2580	0.024
0.1036	2590	0.0237
0.104	2600	0.0227
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0.1048	2620	0.0219
0.1052	2630	0.0226
0.1056	2640	0.0224
0.106	2650	0.0226
0.1064	2660	0.0234
0.1068	2670	0.0224
0.1072	2680	0.0215
0.1076	2690	0.0196
0.108	2700	0.0247
0.1084	2710	0.0233
0.1088	2720	0.0233
0.1092	2730	0.0237
0.1096	2740	0.0234
0.11	2750	0.02
0.1104	2760	0.0228
0.1108	2770	0.0223
0.1112	2780	0.025
0.1116	2790	0.021
0.112	2800	0.023
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0.1128	2820	0.0201
0.1132	2830	0.0244
0.1136	2840	0.0238
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0.1148	2870	0.0248
0.1152	2880	0.0247
0.1156	2890	0.0255
0.116	2900	0.0213
0.1164	2910	0.0231
0.1168	2920	0.0218
0.1172	2930	0.0237
0.1176	2940	0.0236
0.118	2950	0.0229
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0.1192	2980	0.0225
0.1196	2990	0.0219
0.12	3000	0.0239
0.1204	3010	0.024
0.1208	3020	0.023
0.1212	3030	0.0216
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0.1232	3080	0.0215
0.1236	3090	0.0221
0.124	3100	0.0213
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0.1256	3140	0.0221
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0.128	3200	0.0242
0.1284	3210	0.02
0.1288	3220	0.0232
0.1292	3230	0.0221
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0.13	3250	0.0211
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0.1308	3270	0.025
0.1312	3280	0.0205
0.1316	3290	0.0212
0.132	3300	0.0244
0.1324	3310	0.0197
0.1328	3320	0.0216
0.1332	3330	0.0199
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0.134	3350	0.0241
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0.1352	3380	0.023
0.1356	3390	0.0228
0.136	3400	0.0223
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0.1372	3430	0.0208
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0.138	3450	0.0222
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0.1392	3480	0.0244
0.1396	3490	0.0196
0.14	3500	0.0233
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0.1408	3520	0.0232
0.1412	3530	0.0235
0.1416	3540	0.0229
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0.1444	3610	0.0209
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0.1452	3630	0.0215
0.1456	3640	0.0212
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0.1468	3670	0.0205
0.1472	3680	0.0209
0.1476	3690	0.0217
0.148	3700	0.0193
0.1484	3710	0.0222
0.1488	3720	0.0183
0.1492	3730	0.0189
0.1496	3740	0.023
0.15	3750	0.0206
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0.1544	3860	0.02
0.1548	3870	0.0204
0.1552	3880	0.0208
0.1556	3890	0.0199
0.156	3900	0.0216
0.1564	3910	0.0192
0.1568	3920	0.0217
0.1572	3930	0.021
0.1576	3940	0.0204
0.158	3950	0.0236
0.1584	3960	0.0209
0.1588	3970	0.022
0.1592	3980	0.0201
0.1596	3990	0.0184
0.16	4000	0.0242
0.1604	4010	0.0224
0.1608	4020	0.0199
0.1612	4030	0.022
0.1616	4040	0.024
0.162	4050	0.0207
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0.1636	4090	0.0197
0.164	4100	0.0204
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0.1656	4140	0.0218
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0.1668	4170	0.0225
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0.168	4200	0.0197
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0.1692	4230	0.0191
0.1696	4240	0.025
0.17	4250	0.0186
0.1704	4260	0.0198
0.1708	4270	0.0187
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0.1716	4290	0.0207
0.172	4300	0.0214
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0.1728	4320	0.0199
0.1732	4330	0.0203
0.1736	4340	0.0202
0.174	4350	0.0191
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0.1748	4370	0.018
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0.1756	4390	0.0227
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0.1768	4420	0.0193
0.1772	4430	0.0201
0.1776	4440	0.0176
0.178	4450	0.0214
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0.1792	4480	0.0197
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0.18	4500	0.02
0.1804	4510	0.0207
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0.1812	4530	0.02
0.1816	4540	0.0215
0.182	4550	0.0201
0.1824	4560	0.0229
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0.1832	4580	0.0202
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0.184	4600	0.0202
0.1844	4610	0.0199
0.1848	4620	0.0171
0.1852	4630	0.0218
0.1856	4640	0.0213
0.186	4650	0.0198
0.1864	4660	0.0203
0.1868	4670	0.0225
0.1872	4680	0.022
0.1876	4690	0.0196
0.188	4700	0.0211
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0.1888	4720	0.021
0.1892	4730	0.0196
0.1896	4740	0.0222
0.19	4750	0.0214
0.1904	4760	0.022
0.1908	4770	0.0193
0.1912	4780	0.0193
0.1916	4790	0.0182
0.192	4800	0.0203
0.1924	4810	0.0184
0.1928	4820	0.0213
0.1932	4830	0.0211
0.1936	4840	0.0185
0.194	4850	0.0208
0.1944	4860	0.0206
0.1948	4870	0.0195
0.1952	4880	0.0185
0.1956	4890	0.0235
0.196	4900	0.0203
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0.1968	4920	0.0193
0.1972	4930	0.0248
0.1976	4940	0.0206
0.198	4950	0.0205
0.1984	4960	0.0193
0.1988	4970	0.0219
0.1992	4980	0.0217
0.1996	4990	0.0203
0.2	5000	0.0202
0.2004	5010	0.0229
0.2008	5020	0.0201
0.2012	5030	0.02
0.2016	5040	0.0206
0.202	5050	0.0197
0.2024	5060	0.0186
0.2028	5070	0.0207
0.2032	5080	0.0203
0.2036	5090	0.0212
0.204	5100	0.0197
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0.2056	5140	0.0211
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0.2064	5160	0.0191
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0.2072	5180	0.0226
0.2076	5190	0.0232
0.208	5200	0.0192
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0.2096	5240	0.0232
0.21	5250	0.0193
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0.2116	5290	0.0185
0.212	5300	0.0207
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0.2132	5330	0.0188
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0.214	5350	0.0199
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0.2156	5390	0.0182
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0.2172	5430	0.0193
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0.22	5500	0.0216
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0.2276	5690	0.019
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0.24	6000	0.0215
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0.26	6500	0.0203
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0.3	7500	0.0201
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0.316	7900	0.0182
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0.318	7950	0.0176
0.3184	7960	0.019
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0.3196	7990	0.0178
0.32	8000	0.0197
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0.3212	8030	0.0195
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0.3252	8130	0.0194
0.3256	8140	0.0188
0.326	8150	0.0178
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0.3296	8240	0.0204
0.33	8250	0.0189
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0.332	8300	0.018
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0.38	9500	0.0177
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0.384	9600	0.018
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0.39	9750	0.0183
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0.396	9900	0.0189
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0.3992	9980	0.0178
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0.4	10000	0.0182
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0.8024	20060	0.0179
0.8028	20070	0.0172
0.8032	20080	0.0176
0.8036	20090	0.0181
0.804	20100	0.0157
0.8044	20110	0.0176
0.8048	20120	0.0164
0.8052	20130	0.0176
0.8056	20140	0.0184
0.806	20150	0.0174
0.8064	20160	0.0175
0.8068	20170	0.0182
0.8072	20180	0.0177
0.8076	20190	0.0186
0.808	20200	0.0173
0.8084	20210	0.0177
0.8088	20220	0.0201
0.8092	20230	0.0177
0.8096	20240	0.0162
0.81	20250	0.0173
0.8104	20260	0.0177
0.8108	20270	0.0169
0.8112	20280	0.0176
0.8116	20290	0.0155
0.812	20300	0.0171
0.8124	20310	0.0181
0.8128	20320	0.0162
0.8132	20330	0.0191
0.8136	20340	0.016
0.814	20350	0.0171
0.8144	20360	0.0195
0.8148	20370	0.0164
0.8152	20380	0.0166
0.8156	20390	0.0205
0.816	20400	0.0171
0.8164	20410	0.0175
0.8168	20420	0.0185
0.8172	20430	0.0171
0.8176	20440	0.0144
0.818	20450	0.0165
0.8184	20460	0.018
0.8188	20470	0.0174
0.8192	20480	0.0182
0.8196	20490	0.0156
0.82	20500	0.0167
0.8204	20510	0.0187
0.8208	20520	0.0171
0.8212	20530	0.0166
0.8216	20540	0.0169
0.822	20550	0.0179
0.8224	20560	0.0158
0.8228	20570	0.0166
0.8232	20580	0.0172
0.8236	20590	0.0171
0.824	20600	0.0149
0.8244	20610	0.0156
0.8248	20620	0.018
0.8252	20630	0.0178
0.8256	20640	0.0139
0.826	20650	0.0167
0.8264	20660	0.0174
0.8268	20670	0.0184
0.8272	20680	0.0178
0.8276	20690	0.0161
0.828	20700	0.0165
0.8284	20710	0.0146
0.8288	20720	0.0181
0.8292	20730	0.0194
0.8296	20740	0.0153
0.83	20750	0.0159
0.8304	20760	0.0179
0.8308	20770	0.0145
0.8312	20780	0.0168
0.8316	20790	0.0184
0.832	20800	0.0159
0.8324	20810	0.0188
0.8328	20820	0.0148
0.8332	20830	0.0166
0.8336	20840	0.0176
0.834	20850	0.0162
0.8344	20860	0.0181
0.8348	20870	0.0166
0.8352	20880	0.0158
0.8356	20890	0.0176
0.836	20900	0.0195
0.8364	20910	0.0183
0.8368	20920	0.016
0.8372	20930	0.0175
0.8376	20940	0.0157
0.838	20950	0.0174
0.8384	20960	0.0167
0.8388	20970	0.0185
0.8392	20980	0.0182
0.8396	20990	0.0166
0.84	21000	0.0188
0.8404	21010	0.0166
0.8408	21020	0.0179
0.8412	21030	0.0183
0.8416	21040	0.0179
0.842	21050	0.0194
0.8424	21060	0.0164
0.8428	21070	0.0193
0.8432	21080	0.0184
0.8436	21090	0.0151
0.844	21100	0.0166
0.8444	21110	0.0149
0.8448	21120	0.0151
0.8452	21130	0.0163
0.8456	21140	0.0179
0.846	21150	0.017
0.8464	21160	0.0169
0.8468	21170	0.0161
0.8472	21180	0.018
0.8476	21190	0.0163
0.848	21200	0.0167
0.8484	21210	0.0186
0.8488	21220	0.0177
0.8492	21230	0.016
0.8496	21240	0.0176
0.85	21250	0.0162
0.8504	21260	0.0163
0.8508	21270	0.0162
0.8512	21280	0.0179
0.8516	21290	0.0184
0.852	21300	0.0172
0.8524	21310	0.0162
0.8528	21320	0.0158
0.8532	21330	0.015
0.8536	21340	0.0182
0.854	21350	0.0182
0.8544	21360	0.0176
0.8548	21370	0.0172
0.8552	21380	0.0177
0.8556	21390	0.0173
0.856	21400	0.0154
0.8564	21410	0.0182
0.8568	21420	0.0191
0.8572	21430	0.0189
0.8576	21440	0.0168
0.858	21450	0.0157
0.8584	21460	0.0154
0.8588	21470	0.0153
0.8592	21480	0.0154
0.8596	21490	0.0182
0.86	21500	0.0159
0.8604	21510	0.0161
0.8608	21520	0.017
0.8612	21530	0.0179
0.8616	21540	0.018
0.862	21550	0.0167
0.8624	21560	0.0188
0.8628	21570	0.0157
0.8632	21580	0.017
0.8636	21590	0.0178
0.864	21600	0.0168
0.8644	21610	0.0178
0.8648	21620	0.0154
0.8652	21630	0.0178
0.8656	21640	0.0167
0.866	21650	0.0196
0.8664	21660	0.0176
0.8668	21670	0.0176
0.8672	21680	0.0174
0.8676	21690	0.0169
0.868	21700	0.0159
0.8684	21710	0.0176
0.8688	21720	0.017
0.8692	21730	0.0168
0.8696	21740	0.0165
0.87	21750	0.0197
0.8704	21760	0.0163
0.8708	21770	0.0161
0.8712	21780	0.0169
0.8716	21790	0.0175
0.872	21800	0.0197
0.8724	21810	0.0191
0.8728	21820	0.0182
0.8732	21830	0.0189
0.8736	21840	0.0152
0.874	21850	0.0167
0.8744	21860	0.0157
0.8748	21870	0.0163
0.8752	21880	0.0151
0.8756	21890	0.0184
0.876	21900	0.0181
0.8764	21910	0.018
0.8768	21920	0.0173
0.8772	21930	0.0142
0.8776	21940	0.0171
0.878	21950	0.0174
0.8784	21960	0.0168
0.8788	21970	0.0173
0.8792	21980	0.0173
0.8796	21990	0.0182
0.88	22000	0.0162
0.8804	22010	0.0166
0.8808	22020	0.0174
0.8812	22030	0.0152
0.8816	22040	0.0182
0.882	22050	0.0177
0.8824	22060	0.0148
0.8828	22070	0.0167
0.8832	22080	0.0164
0.8836	22090	0.018
0.884	22100	0.018
0.8844	22110	0.0174
0.8848	22120	0.0179
0.8852	22130	0.0156
0.8856	22140	0.0186
0.886	22150	0.0189
0.8864	22160	0.0158
0.8868	22170	0.0164
0.8872	22180	0.0159
0.8876	22190	0.0162
0.888	22200	0.0151
0.8884	22210	0.0179
0.8888	22220	0.0183
0.8892	22230	0.016
0.8896	22240	0.0167
0.89	22250	0.018
0.8904	22260	0.0167
0.8908	22270	0.0189
0.8912	22280	0.0151
0.8916	22290	0.0183
0.892	22300	0.0164
0.8924	22310	0.0187
0.8928	22320	0.0154
0.8932	22330	0.0188
0.8936	22340	0.0153
0.894	22350	0.0166
0.8944	22360	0.0152
0.8948	22370	0.0165
0.8952	22380	0.0197
0.8956	22390	0.0165
0.896	22400	0.0173
0.8964	22410	0.0168
0.8968	22420	0.0162
0.8972	22430	0.018
0.8976	22440	0.0161
0.898	22450	0.0159
0.8984	22460	0.0162
0.8988	22470	0.0222
0.8992	22480	0.0162
0.8996	22490	0.0154
0.9	22500	0.0205
0.9004	22510	0.0173
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0.9016	22540	0.0173
0.902	22550	0.0167
0.9024	22560	0.0152
0.9028	22570	0.017
0.9032	22580	0.0177
0.9036	22590	0.0153
0.904	22600	0.0163
0.9044	22610	0.0164
0.9048	22620	0.0157
0.9052	22630	0.0191
0.9056	22640	0.0152
0.906	22650	0.0176
0.9064	22660	0.017
0.9068	22670	0.0169
0.9072	22680	0.0158
0.9076	22690	0.0188
0.908	22700	0.015
0.9084	22710	0.0166
0.9088	22720	0.0173
0.9092	22730	0.0151
0.9096	22740	0.0179
0.91	22750	0.0163
0.9104	22760	0.0162
0.9108	22770	0.0162
0.9112	22780	0.0179
0.9116	22790	0.0186
0.912	22800	0.017
0.9124	22810	0.0153
0.9128	22820	0.0158
0.9132	22830	0.0184
0.9136	22840	0.0177
0.914	22850	0.0169
0.9144	22860	0.0183
0.9148	22870	0.0181
0.9152	22880	0.0179
0.9156	22890	0.0155
0.916	22900	0.0188
0.9164	22910	0.0163
0.9168	22920	0.0168
0.9172	22930	0.0181
0.9176	22940	0.0171
0.918	22950	0.0187
0.9184	22960	0.0157
0.9188	22970	0.0168
0.9192	22980	0.0189
0.9196	22990	0.0156
0.92	23000	0.0194
0.9204	23010	0.0154
0.9208	23020	0.0167
0.9212	23030	0.0145
0.9216	23040	0.0171
0.922	23050	0.0172
0.9224	23060	0.0183
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0.9232	23080	0.0168
0.9236	23090	0.0165
0.924	23100	0.0181
0.9244	23110	0.0174
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0.9256	23140	0.0173
0.926	23150	0.019
0.9264	23160	0.0137
0.9268	23170	0.0167
0.9272	23180	0.0165
0.9276	23190	0.0181
0.928	23200	0.0155
0.9284	23210	0.0185
0.9288	23220	0.015
0.9292	23230	0.0169
0.9296	23240	0.0179
0.93	23250	0.0194
0.9304	23260	0.017
0.9308	23270	0.0176
0.9312	23280	0.0181
0.9316	23290	0.0169
0.932	23300	0.0161
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0.9328	23320	0.0151
0.9332	23330	0.0153
0.9336	23340	0.0157
0.934	23350	0.0172
0.9344	23360	0.0139
0.9348	23370	0.019
0.9352	23380	0.0181
0.9356	23390	0.016
0.936	23400	0.0185
0.9364	23410	0.0174
0.9368	23420	0.02
0.9372	23430	0.0157
0.9376	23440	0.02
0.938	23450	0.0154
0.9384	23460	0.0159
0.9388	23470	0.0177
0.9392	23480	0.0157
0.9396	23490	0.0172
0.94	23500	0.0182
0.9404	23510	0.014
0.9408	23520	0.0179
0.9412	23530	0.0177
0.9416	23540	0.0163
0.942	23550	0.0171
0.9424	23560	0.0178
0.9428	23570	0.0155
0.9432	23580	0.0177
0.9436	23590	0.0168
0.944	23600	0.0201
0.9444	23610	0.0167
0.9448	23620	0.017
0.9452	23630	0.0162
0.9456	23640	0.0167
0.946	23650	0.0193
0.9464	23660	0.016
0.9468	23670	0.0171
0.9472	23680	0.0157
0.9476	23690	0.0156
0.948	23700	0.0187
0.9484	23710	0.0164
0.9488	23720	0.0181
0.9492	23730	0.0178
0.9496	23740	0.017
0.95	23750	0.0185
0.9504	23760	0.0158
0.9508	23770	0.0174
0.9512	23780	0.015
0.9516	23790	0.0159
0.952	23800	0.016
0.9524	23810	0.0148
0.9528	23820	0.0174
0.9532	23830	0.0163
0.9536	23840	0.016
0.954	23850	0.0173
0.9544	23860	0.0194
0.9548	23870	0.0167
0.9552	23880	0.017
0.9556	23890	0.0172
0.956	23900	0.0155
0.9564	23910	0.0176
0.9568	23920	0.0177
0.9572	23930	0.0182
0.9576	23940	0.0169
0.958	23950	0.0189
0.9584	23960	0.0164
0.9588	23970	0.0166
0.9592	23980	0.0178
0.9596	23990	0.0191
0.96	24000	0.0149
0.9604	24010	0.0177
0.9608	24020	0.0169
0.9612	24030	0.0151
0.9616	24040	0.0168
0.962	24050	0.0146
0.9624	24060	0.015
0.9628	24070	0.0222
0.9632	24080	0.0169
0.9636	24090	0.0164
0.964	24100	0.0171
0.9644	24110	0.0179
0.9648	24120	0.0173
0.9652	24130	0.0154
0.9656	24140	0.0178
0.966	24150	0.018
0.9664	24160	0.0182
0.9668	24170	0.0194
0.9672	24180	0.0189
0.9676	24190	0.0174
0.968	24200	0.019
0.9684	24210	0.0157
0.9688	24220	0.0178
0.9692	24230	0.0155
0.9696	24240	0.0157
0.97	24250	0.0188
0.9704	24260	0.0167
0.9708	24270	0.0169
0.9712	24280	0.0172
0.9716	24290	0.0159
0.972	24300	0.0175
0.9724	24310	0.0159
0.9728	24320	0.0205
0.9732	24330	0.018
0.9736	24340	0.0187
0.974	24350	0.0164
0.9744	24360	0.0158
0.9748	24370	0.0186
0.9752	24380	0.0172
0.9756	24390	0.0161
0.976	24400	0.0191
0.9764	24410	0.0176
0.9768	24420	0.0163
0.9772	24430	0.0204
0.9776	24440	0.0178
0.978	24450	0.0176
0.9784	24460	0.0168
0.9788	24470	0.0163
0.9792	24480	0.0151
0.9796	24490	0.0178
0.98	24500	0.0167
0.9804	24510	0.0152
0.9808	24520	0.0175
0.9812	24530	0.0177
0.9816	24540	0.0146
0.982	24550	0.0176
0.9824	24560	0.0179
0.9828	24570	0.0175
0.9832	24580	0.0161
0.9836	24590	0.0172
0.984	24600	0.0171
0.9844	24610	0.0192
0.9848	24620	0.0183
0.9852	24630	0.0133
0.9856	24640	0.0176
0.986	24650	0.0155
0.9864	24660	0.019
0.9868	24670	0.0164
0.9872	24680	0.0169
0.9876	24690	0.0159
0.988	24700	0.0147
0.9884	24710	0.0191
0.9888	24720	0.0159
0.9892	24730	0.0182
0.9896	24740	0.0165
0.99	24750	0.0172
0.9904	24760	0.0189
0.9908	24770	0.0156
0.9912	24780	0.0176
0.9916	24790	0.0169
0.992	24800	0.0176
0.9924	24810	0.0178
0.9928	24820	0.0184
0.9932	24830	0.0173
0.9936	24840	0.0175
0.994	24850	0.0154
0.9944	24860	0.0155
0.9948	24870	0.0177
0.9952	24880	0.0189
0.9956	24890	0.0154
0.996	24900	0.018
0.9964	24910	0.0165
0.9968	24920	0.019
0.9972	24930	0.0168
0.9976	24940	0.0157
0.998	24950	0.014
0.9984	24960	0.018
0.9988	24970	0.0157
0.9992	24980	0.0165
0.9996	24990	0.0176
1.0	25000	0.0176

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 4.0.2
• PyLate: 1.2.0
• Transformers: 4.48.2
• PyTorch: 2.5.1+cu121
• Accelerate: 1.2.1
• Datasets: 3.3.1
• Tokenizers: 0.21.0

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"
}

PyLate

@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}