PyLate model based on cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR

This is a PyLate model finetuned from cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR. 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: cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR
Document Length: 96 tokens
Query Length: 96 tokens
Output Dimensionality: 128 tokens
Similarity Function: MaxSim

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': 95, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search.

Indexing documents

Load the ColBERT model and initialize the PLAID 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="pylate_model_id",
)

# Step 2: Initialize the PLAID index
index = indexes.PLAID(
    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.PLAID(
    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="pylate_model_id",
)

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

Unnamed Dataset

Size: 598,752 training samples
Columns: query, positive, query_id, target_id, group_id, negative_0, negative_1, and negative_2

Approximate statistics based on the first 1000 samples:

	query	positive	query_id	target_id	group_id	negative_0	negative_1	negative_2
type	string	string	string	int	int	string	string	string
details	min: 5 tokens mean: 17.16 tokens max: 94 tokens	min: 4 tokens mean: 16.37 tokens max: 86 tokens	min: 7 tokens mean: 9.15 tokens max: 10 tokens	529303: ~0.10% 529411: ~0.10% 586875: ~0.10% 702640: ~0.10% 702867: ~0.10% 710062: ~0.10% 712643: ~0.10% 715233: ~0.10% 718670: ~0.10% 724904: ~0.10% 725131: ~0.10% 730548: ~0.10% 739323: ~0.10% 739531: ~0.10% 752095: ~0.10% 752205: ~0.10% 756137: ~0.10% 778478: ~0.10% 778765: ~0.10% 781062: ~0.10% 792733: ~0.10% 792844: ~0.10% 793066: ~0.10% 793238: ~0.10% 793351: ~0.10% 793414: ~0.10% 794852: ~0.10% 836718: ~0.10% 836877: ~0.10% 837656: ~0.10% 838325: ~0.10% 838609: ~0.10% 840030: ~0.10% 840216: ~0.10% 840490: ~0.10% 840565: ~0.10% 840788: ~0.10% 841124: ~0.10% 841839: ~0.10% 842058: ~0.10% 842369: ~0.10% 842400: ~0.10% 843189: ~0.10% 844915: ~0.10% 845099: ~0.10% 845647: ~0.10% 845944: ~0.10% 846345: ~0.10% 846501: ~0.10% 846875: ~0.10% 847214: ~0.10% 847627: ~0.10% 850700: ~0.10% 851158: ~0.10% 851198: ~0.10% 851311: ~0.10% 852407: ~0.10% 853315: ~0.10% 853410: ~0.10% 853916: ~0.10% 854482: ~0.10% 855293: ~0.10% 855988: ~0.10% 856778: ~0.10% 858754: ~0.10% 859104: ~0.10% 861175: ~0.10% 861245: ~0.10% 861608: ~0.10% 861639: ~0.10% 902251: ~0.10% 902740: ~0.10% 906780: ~0.10% 907121: ~0.10% 908523: ~0.10% 915175: ~0.10% 915981: ~0.10% 917006: ~0.10% 917336: ~0.10% 920458: ~0.10% 922570: ~0.10% 923647: ~0.10% 923840: ~0.10% 929516: ~0.10% 929772: ~0.10% 930748: ~0.10% 932197: ~0.10% 938061: ~0.10% 939506: ~0.10% 942382: ~0.10% 943852: ~0.10% 944360: ~0.10% 950641: ~0.10% 955464: ~0.10% 957797: ~0.10% 959174: ~0.10% 961168: ~0.10% 964093: ~0.10% 964324: ~0.10% 964435: ~0.10% 968667: ~0.10% 968987: ~0.10% 986148: ~0.10% 986864: ~0.10% 997276: ~0.10% 1103518: ~0.10% 1105853: ~0.10% 1112921: ~0.10% 1124300: ~0.10% 1125443: ~0.10% 1127018: ~0.10% 1129625: ~0.10% 1137529: ~0.10% 1139042: ~0.10% 1139701: ~0.10% 1152631: ~0.10% 1153928: ~0.10% 1159946: ~0.10% 1167323: ~0.10% 1189596: ~0.10% 1237262: ~0.10% 1300673: ~0.10% 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~0.10% 2903357: ~0.10% 2903543: ~0.10% 2903900: ~0.10% 2905557: ~0.10% 2905692: ~0.10% 2907114: ~0.10% 2907294: ~0.10% 2907634: ~0.10% 2907857: ~0.10% 2907875: ~0.10% 2908122: ~0.10% 2908888: ~0.10% 2909571: ~0.10% 2909706: ~0.10% 2910103: ~0.10% 2910120: ~0.10% 2910160: ~0.10% 2912510: ~0.10% 2912559: ~0.10% 2913002: ~0.10% 2913802: ~0.10% 2914412: ~0.10% 2915051: ~0.10% 2915125: ~0.10% 2915177: ~0.10% 2915227: ~0.10% 2915572: ~0.10% 2915588: ~0.10% 2915810: ~0.10% 2916544: ~0.10% 2917036: ~0.10% 2917848: ~0.10% 2918060: ~0.10% 2918777: ~0.10% 2919603: ~0.10% 2919753: ~0.10% 2919832: ~0.10% 2920439: ~0.10% 2921509: ~0.10% 2921736: ~0.10% 2921769: ~0.10% 2923331: ~0.10% 2923572: ~0.10% 2923710: ~0.10% 2924304: ~0.10% 2925198: ~0.10% 2925471: ~0.10% 2925680: ~0.10% 2927642: ~0.10% 2928566: ~0.10% 2928935: ~0.10% 2928962: ~0.10% 2929770: ~0.10% 2931063: ~0.10% 2931079: ~0.10% 2931360: ~0.10% 2931782: ~0.10% 2931903: ~0.10% 2932735: ~0.10% 2932898: ~0.10% 2933072: ~0.10% 2933527: ~0.10% 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19047076: ~0.10% 19051444: ~0.10% 19054821: ~0.10% 19055217: ~0.10% 19056571: ~0.10% 19059056: ~0.10% 19060404: ~0.10% 19060423: ~0.10% 19065472: ~0.10% 19067087: ~0.30% 19069076: ~0.10% 19069090: ~0.10% 19069199: ~0.10% 19071966: ~0.10% 19072159: ~0.10% 19074212: ~0.10% 19074306: ~0.10% 19074845: ~0.10% 19075392: ~0.10% 19075984: ~0.10% 19077085: ~0.10% 19077548: ~0.10% 19077550: ~0.10% 19077574: ~0.10% 19077734: ~0.10% 19078344: ~0.10% 19078439: ~0.10% 19078461: ~0.10% 19078729: ~0.10% 19078829: ~0.10% 19079084: ~0.10% 19079155: ~0.10% 19079172: ~0.10% 19079462: ~0.10% 19079986: ~0.10% 19080040: ~0.10% 19080130: ~0.10% 19080847: ~0.10% 19082898: ~0.10% 19085107: ~0.10% 19088167: ~0.10% 19096198: ~0.10% 19096683: ~0.10% 19101672: ~0.10% 19101751: ~0.10% 19101819: ~0.10% 19102185: ~0.10% 19102189: ~0.10% 19102603: ~0.10% 19103646: ~0.10% 19104357: ~0.10% 19106521: ~0.10% 19108373: ~0.10% 19113619: ~0.10% 19114773: ~0.10% 19115193: ~0.10% 19121682: ~0.10% 19121933: ~0.10% 19124148: 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19116477: ~0.10% 19120285: ~0.10% 19120816: ~0.10% 19122473: ~0.10% 19124683: ~0.10% 19125634: ~0.10% 19133110: ~0.10% 19133140: ~0.10% 19133936: ~0.10% 19134957: ~0.10% 19135804: ~0.10% 21020742: ~0.10% 21023234: ~0.10% 21026273: ~0.10% 21026405: ~0.10% 21027609: ~0.10% 21030125: ~0.10% 21032136: ~0.10% 21032798: ~0.10% 21032971: ~0.10% 21037419: ~0.10% 21037526: ~0.10% 21043848: ~0.10% 21049986: ~0.10% 21051356: ~0.10% 21051924: ~0.10% 21051968: ~0.10% 21052317: ~0.10% 21055107: ~0.10% 21056534: ~0.10% 21060898: ~0.10% 21061590: ~0.10% 21064828: ~0.10% 21065993: ~0.10% 21074876: ~0.10% 21082691: ~0.10% 21084835: ~0.10% 21084851: ~0.10% 21089660: ~0.10% 21090091: ~0.10% 21090944: ~0.10% 21091358: ~0.10% 21091617: ~0.10% 21096942: ~0.10% 21097731: ~0.10% 21101380: ~0.10% 21104207: ~0.10% 21107094: ~0.10% 21108741: ~0.10% 21111312: ~0.10% 21111646: ~0.10% 21112106: ~0.10% 21112250: ~0.10% 21112741: ~0.10% 21114672: ~0.10% 21119478: ~0.10% 21120350: ~0.10% 21120507: ~0.10% 21121372: ~0.10% 21132524: ~0.10% 21133162: ~0.10% 21138516: ~0.10% 21139990: ~0.10% 21147652: ~0.10% 21152933: ~0.10% 21154477: ~0.10% 21158500: ~0.10% 21161737: ~0.10% 21161780: ~0.10% 21161912: ~0.10% 21165751: ~0.10% 21165916: ~0.10% 21166526: ~0.10% 21166988: ~0.10% 21167998: ~0.10% 21171525: ~0.10% 35135794: ~0.10% 35139750: ~0.10% 35150116: ~0.10% 35197897: ~0.10% 35200139: ~0.10% 35200267: ~0.10% 35407408: ~0.10% 35602991: ~0.10% 35603580: ~0.10% 35603661: ~0.10% 35603813: ~0.10% 35603927: ~0.10% 35603994: ~0.10% 35604253: ~0.10% 35604506: ~0.10% 35604729: ~0.10% 35605170: ~0.10% 35605804: ~0.10% 35605861: ~0.10% 35606475: ~0.10% 35745526: ~0.10% 35755354: ~0.10% 35777329: ~0.10% 35779098: ~0.10% 35784066: ~0.10% 35785514: ~0.10% 35848361: ~0.10% 35848449: ~0.10% 35850229: ~0.10% 35851355: ~0.10% 35852367: ~0.10% 35853049: ~0.10% 35854169: ~0.10% 35855695: ~0.10% 35855955: ~0.10% 35856215: ~0.10% 35856434: ~0.10% 35857026: ~0.10% 35857067: ~0.10% 35857232: ~0.10% 35857324: ~0.10% 35857618: ~0.10% 35858231: ~0.10% 35859178: ~0.10% 35860153: ~0.10% 35860257: ~0.10% 35860266: ~0.10% 35860327: ~0.10% 35860472: ~0.10% 35861076: ~0.10% 35861131: ~0.10% 35862043: ~0.10% 35862103: ~0.10% 35863462: ~0.10% 35864220: ~0.20% 35864443: ~0.10% 35865184: ~0.10% 35866132: ~0.10% 35866187: ~0.10% 35866712: ~0.10% 35866756: ~0.10% 35867023: ~0.10% 35867040: ~0.10% 35867189: ~0.10% 35869061: ~0.10% 35869224: ~0.10% 35870232: ~0.10% 35870392: ~0.10% 35871459: ~0.10% 35871777: ~0.10% 35873025: ~0.10% 35874285: ~0.10% 35874358: ~0.10% 35884374: ~0.10% 35884375: ~0.10% 35884419: ~0.10% 35885827: ~0.10% 35898101: ~0.10% 36026868: ~0.10% 36057161: ~0.10% 36060726: ~0.10% 36062085: ~0.10% 36064141: ~0.10% 36068681: ~0.10% 36074867: ~0.10% 36249935: ~0.10% 36259227: ~0.10% 36407530: ~0.10% 36410229: ~0.10% 36814659: ~0.10% 36852604: ~0.10% 36862275: ~0.10% 36879033: ~0.10% 36879164: ~0.10% 36882738: ~0.10% 36884649: ~0.10% 36886614: ~0.10% 36887728: ~0.10% 36889852: ~0.10% 36890149: ~0.10% 36890391: ~0.10% 36893616: ~0.10% 36893777: ~0.10% 36924525: ~0.10% 36925221: ~0.10% 36933313: ~0.10% 36944981: ~0.10% 36952476: ~0.10% 36956821: ~0.10% 37002763: ~0.10% 37003175: ~0.10% 37496558: ~0.10% 37498763: ~0.10% 40000285: ~0.10% 40000895: ~0.10% 40004026: ~0.10% 40005033: ~0.10% 40005682: ~0.10% 40005733: ~0.10% 40008931: ~0.20% 40009896: ~0.10% 40011717: ~0.10% 40018024: ~0.10% 40018375: ~0.10% 40018728: ~0.10% 40021844: ~0.10% 40021876: ~0.10% 40023499: ~0.10% 40028534: ~0.10% 40028890: ~0.10% 40029077: ~0.10% 40029100: ~0.10% 40029366: ~0.10% 40029802: ~0.10% 40033625: ~0.10% 40036207: ~0.10% 40037845: ~0.10% 40039439: ~0.20% 40040683: ~0.10% 40041698: ~0.10% 40042337: ~0.10% 40048793: ~0.10% 40052768: ~0.20% 40052913: ~0.10% 40054133: ~0.10% 40054144: ~0.10% 40056578: ~0.10% 40057367: ~0.10% 40057961: ~0.10% 40058138: ~0.10% 40058892: ~0.20% 40058894: ~0.10% 40058896: ~0.10% 40059178: ~0.10% 40059632: ~0.10% 40061328: ~0.10% 40061537: ~0.10% 40061834: ~0.20% 40063354: ~0.10% 40063501: ~0.10% 40063802: ~0.10% 40063847: ~0.10% 40064071: ~0.10% 40064205: ~0.10% 40064233: ~0.10% 40064847: ~0.20% 40065325: ~0.10% 40065403: ~0.10% 40066073: ~0.10% 40066417: ~0.30% 40066896: ~0.10% 40066981: ~0.10% 40068070: ~0.10% 40068357: ~0.10% 40069100: ~0.10% 40069655: ~0.10% 40070098: ~0.10% 40070192: ~0.10% 40070340: ~0.10% 40071614: ~0.10% 40072606: ~0.10% 40073531: ~0.10% 40073585: ~0.10% 40076568: ~0.10% 40077949: ~0.10% 40078545: ~0.10% 40078677: ~0.10% 40081782: ~0.10% 40084190: ~0.10% 40086368: ~0.10% 40087818: ~0.10% 40087827: ~0.10% 40088544: ~0.10% 40090281: ~0.10% 40090864: ~0.10% 40091264: ~0.10% 40092396: ~0.10% 40094574: ~0.10% 40097040: ~0.10% 40097236: ~0.10% 40097632: ~0.10% 40098114: ~0.20% 40098150: ~0.10% 40099317: ~0.10% 40099653: ~0.10% 40101923: ~0.10% 40102099: ~0.10% 40127049: ~0.10% 40131774: ~0.10% 40140313: ~0.10% 40154898: ~0.10% 40160930: ~0.10% 40161645: ~0.10% 40162326: ~0.10% 40162341: ~0.10% 40162700: ~0.10% 40162717: ~0.10% 40163057: ~0.10% 40163207: ~0.10% 40163312: ~0.10% 40164916: ~0.10% 40165244: ~0.10% 40165245: ~0.10% 40167259: ~0.10% 40168262: ~0.10% 40169689: ~0.10% 40170549: ~0.10% 40173567: ~0.10% 40173627: ~0.10% 40174494: ~0.10% 40177142: ~0.10% 40181642: ~0.10% 40213200: ~0.10% 40213205: ~0.10% 40213264: ~0.10% 40220371: ~0.10% 40220597: ~0.10% 40221142: ~0.10% 40221274: ~0.10% 40222311: ~0.10% 40222644: ~0.10% 40223464: ~0.10% 40223719: ~0.10% 40223757: ~0.10% 40223765: ~0.10% 40226994: ~0.10% 40227528: ~0.10% 40227989: ~0.10% 40228214: ~0.20% 40228537: ~0.10% 40229591: ~0.10% 40231373: ~0.10% 40231440: ~0.10% 40231532: ~0.10% 40232072: ~0.10% 40232438: ~0.10% 40235198: ~0.10% 40235211: ~0.10% 40236481: ~0.10% 40236491: ~0.10% 40236648: ~0.10% 40239095: ~0.10% 40243120: ~0.10% 40244478: ~0.10% 40703484: ~0.10% 40714022: ~0.10% 40714087: ~0.10% 40722431: ~0.10% 40723974: ~0.10% 40729513: ~0.10% 40731216: ~0.10% 40735980: ~0.10% 40738765: ~0.10% 40742808: ~0.10% 40743001: ~0.10% 40743420: ~0.10% 40745187: ~0.10% 40745372: ~0.10% 40747218: ~0.10% 40747576: ~0.10% 40751045: ~0.10% 40753859: ~0.10% 40799060: ~0.10% 40955510: ~0.10% 41013438: ~0.10% 41017375: ~0.10% 41054396: ~0.10% 41064024: ~0.10% 41076948: ~0.10% 41169880: ~0.10% 41242641: ~0.10% 41342607: ~0.10% 42479524: ~0.10% 42480272: ~0.10% 42480872: ~0.10% 42483299: ~0.10% 42628972: ~0.10% 42629470: ~0.10% 42629622: ~0.10% 42799452: ~0.10% 42799744: ~0.10% 42800246: ~0.10% 42801108: ~0.10% 42801181: ~0.10% 42801272: ~0.10% 42898363: ~0.10% 42898711: ~0.10% 42898880: ~0.10% 42899008: ~0.10% 42899155: ~0.10% 42900097: ~0.10% 42900206: ~0.10% 42900229: ~0.10% 42900256: ~0.10% 42900543: ~0.10% 42902295: ~0.10% 42902413: ~0.10% 42902544: ~0.10% 42903310: ~0.10% 42930629: ~0.10% 42931787: ~0.10% 42932089: ~0.10% 42935420: ~0.10% 42936781: ~0.10% 42939564: ~0.10% 42947181: ~0.10% 42959987: ~0.10% 42960507: ~0.10% 42963703: ~0.10% 42970229: ~0.10% 42973731: ~0.10% 43011459: ~0.10% 43012036: ~0.10% 43012238: ~0.10% 43012387: ~0.10% 43012577: ~0.10% 43013021: ~0.30% 43013634: ~0.10% 43013927: ~0.10% 43014081: ~0.10% 43014254: ~0.10% 43014281: ~0.10% 43167579: ~0.10% 43169324: ~0.10% 43190819: ~0.10% 43256148: ~0.10% 43257390: ~0.10% 43258778: ~0.10% 43260352: ~0.10% 43260944: ~0.10% 43265705: ~0.10% 43265876: ~0.10% 43266078: ~0.10% 43267313: ~0.10% 43268460: ~0.10% 43268651: ~0.10% 43269740: ~0.10% 43272104: ~0.10% 43273016: ~0.10% 43276976: ~0.10% 43278590: ~0.10% 43280847: ~0.10% 43281254: ~0.10% 43281864: ~0.10% 43285614: ~0.10% 43286303: ~0.10% 43288114: ~0.10% 43291720: ~0.10% 43292570: ~0.10% 43293118: ~0.10% 43294368: ~0.10% 43295921: ~0.10% 43296826: ~0.10% 43296946: ~0.10% 43297700: ~0.10% 43515450: ~0.10% 43527027: ~0.10% 43527206: ~0.10% 43532099: ~0.10% 43679574: ~0.10% 43702356: ~0.10% 43717139: ~0.10% 43769269: ~0.20% 43823338: ~0.10% 44082127: ~0.10% 44097108: ~0.10% 44103971: ~0.10% 44123627: ~0.10% 44189345: ~0.10% 44785099: ~0.10% 45775080: ~0.10% 45775550: ~0.10% 45775584: ~0.10% 45776537: ~0.10% 45776869: ~0.10% 46221435: ~0.10% 46234163: ~0.10% 46275432: ~0.10% 46276153: ~0.20% 46276276: ~0.10% 46287312: ~0.10% 46287316: ~0.10% 46287354: ~0.10% 46287511: ~0.10%	min: 5 tokens mean: 18.5 tokens max: 95 tokens	min: 5 tokens mean: 19.32 tokens max: 96 tokens	min: 5 tokens mean: 20.83 tokens max: 95 tokens

Samples:

query	positive	query_id	target_id	group_id	negative_0	negative_1	negative_2
`布洛芬片剂 [可普芬]`	`ibuprofen oral tablet [kepufen]`	`q36653233_0`	`35870422`	`1177480`	`ibuprofen topical ointment [vesicum]`	`ibuprofen extended release oral tablet [ibu 1a pharma]`	`ibuprofen oral tablet [jamp-ibuprofen]`
`avapro hct 300/12.5 tablet, 30`	`hydrochlorothiazide 12.5 mg / irbesartan 300 mg oral tablet [avapro hct] box of 30`	`q43362350_0`	`2936189`	`19081559`	`hydrochlorothiazide 25 mg / irbesartan 300 mg delayed release oral tablet [irbesartan hydrochlorothiazide teva] box of 28`	`hydrochlorothiazide 25 mg / irbesartan 300 mg oral tablet [irbesartan bz] box of 98`	`hydrochlorothiazide 25 mg / irbesartan 300 mg delayed release oral tablet [karvezide] box of 280`
`povidone-iodine (pfizer (perth)) 10% application, 500 ml, bottle`	`500 ml povidone-iodine 100 mg/ml transdermal system by pfizer`	`q43321701_0`	`2907294`	`19083433`	`30 ml povidone-iodine 100 mg/ml topical solution by pfizer`	`125 ml povidone-iodine 100 mg/ml topical solution [povidone iodee mylan] box of 1 by mylan`	`100 ml povidone-iodine 100 mg/ml topical solution by bittermedizin`

Loss: thirawat_trainer.training.colbert.KDLossWrapper

Evaluation Dataset

Unnamed Dataset

Size: 2,270 evaluation samples
Columns: query, positive, query_id, target_id, group_id, negative_0, negative_1, and negative_2

Approximate statistics based on the first 1000 samples:

	query	positive	query_id	target_id	group_id	negative_0	negative_1	negative_2
type	string	string	string	int	int	string	string	string
details	min: 5 tokens mean: 16.73 tokens max: 84 tokens	min: 5 tokens mean: 16.27 tokens max: 96 tokens	min: 8 tokens mean: 9.11 tokens max: 10 tokens	529411: ~0.10% 529716: ~0.10% 715458: ~1.20% 724394: ~0.10% 725172: ~0.10% 742183: ~0.10% 742189: ~0.10% 746843: ~0.10% 751282: ~0.50% 751889: ~0.10% 766844: ~0.10% 778353: ~0.50% 779185: ~0.10% 793309: ~0.10% 841119: ~0.10% 843465: ~0.10% 844619: ~0.10% 847968: ~0.10% 848546: ~0.20% 851288: ~0.40% 853204: ~0.10% 856802: ~0.10% 859336: ~0.10% 859815: ~0.10% 861178: ~0.10% 903753: ~0.50% 903904: ~0.20% 917339: ~0.90% 953118: ~0.20% 962398: ~0.10% 963355: ~0.90% 964326: ~0.10% 966454: ~0.10% 968639: ~0.30% 993770: ~0.20% 1036159: ~0.10% 1100303: ~0.10% 1103640: ~0.10% 1113672: ~2.40% 1115125: ~0.40% 1140258: ~0.10% 1145582: ~0.10% 1146810: ~0.10% 1150347: ~3.60% 1150870: ~0.10% 1150925: ~0.10% 1153428: ~0.30% 1163945: ~1.50% 1181809: ~0.10% 1201113: ~0.20% 1308877: ~0.70% 1350332: ~1.50% 1384051: ~0.10% 1387578: ~0.10% 1586834: ~0.10% 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19099736: ~0.10% 19101761: ~0.10% 19102080: ~0.10% 19102491: ~0.10% 19102526: ~0.30% 19105795: ~0.10% 19108452: ~0.10% 19109679: ~0.10% 19112655: ~0.10% 19112948: ~0.80% 19112951: ~0.20% 19123342: ~0.10% 19130496: ~0.20% 19132879: ~0.10% 19133859: ~0.10% 19136426: ~0.10% 19136626: ~0.20% 19136638: ~0.10% 19137312: ~0.50% 21032018: ~0.10% 21044613: ~0.10% 21050406: ~0.10% 21052676: ~0.20% 21053906: ~0.10% 21056256: ~0.10% 21067938: ~0.10% 21075193: ~0.10% 21084431: ~0.10% 21086101: ~0.10% 21086431: ~0.10% 21092635: ~0.10% 21092897: ~0.10% 21096120: ~0.10% 21109784: ~0.10% 21112596: ~0.10% 21114841: ~0.10% 21119204: ~0.10% 21119416: ~0.50% 21120543: ~0.10% 21121684: ~0.10% 21123205: ~0.10% 21144029: ~0.10% 21146069: ~0.10% 21150873: ~0.10% 21151796: ~0.10% 21153910: ~0.10% 21154890: ~0.10% 21155798: ~0.10% 21162438: ~0.10% 21163023: ~0.30% 35157015: ~0.10% 35200094: ~0.20% 35201112: ~0.10% 35602993: ~0.20% 35603717: ~0.20% 35605255: ~0.10% 35605598: ~0.40% 35605637: ~0.20% 35741965: ~0.10% 35745557: ~0.10% 35848701: ~0.10% 35849435: ~0.10% 35849752: ~0.20% 35849843: ~0.20% 35851095: ~0.10% 35851759: ~0.10% 35852008: ~0.10% 35852035: ~0.20% 35853042: ~0.10% 35853475: ~0.10% 35853764: ~0.10% 35853951: ~0.20% 35854063: ~0.10% 35855053: ~0.10% 35855539: ~0.10% 35856403: ~0.10% 35857140: ~0.20% 35857318: ~0.10% 35858011: ~0.10% 35858409: ~0.20% 35858879: ~0.10% 35859068: ~0.10% 35859694: ~0.10% 35859701: ~0.10% 35860653: ~0.40% 35861025: ~0.10% 35861327: ~0.10% 35862178: ~0.10% 35862496: ~0.10% 35863132: ~0.20% 35863370: ~0.20% 35863520: ~0.20% 35864974: ~0.20% 35867190: ~0.10% 35867650: ~0.30% 35867786: ~0.10% 35868957: ~0.10% 35869173: ~0.20% 35869475: ~0.10% 35869582: ~0.10% 35869668: ~0.10% 35870142: ~0.10% 35870491: ~0.10% 35870556: ~0.10% 35871033: ~0.10% 35871894: ~0.10% 35872986: ~0.10% 35873158: ~0.10% 35873803: ~0.20% 36053241: ~0.10% 36065031: ~0.10% 36120676: ~0.10% 36249242: ~0.10% 36269476: ~0.20% 36406680: ~0.20% 36407470: ~0.10% 36812109: ~0.10% 36930572: ~0.10% 36941007: ~0.10% 37002370: ~0.20% 37003255: ~0.10% 37497243: ~0.30% 37498745: ~0.30% 37499597: ~0.20% 40000191: ~0.10% 40003695: ~0.10% 40006525: ~0.10% 40007127: ~0.10% 40012752: ~0.20% 40014094: ~0.20% 40015281: ~0.20% 40020313: ~0.10% 40021303: ~0.10% 40023567: ~0.10% 40024030: ~0.10% 40027288: ~0.10% 40027652: ~0.10% 40029800: ~0.70% 40029802: ~0.20% 40041389: ~0.10% 40042239: ~0.10% 40045755: ~0.10% 40047199: ~0.90% 40047884: ~0.10% 40048088: ~0.20% 40048641: ~0.10% 40049782: ~0.10% 40051789: ~0.10% 40054144: ~0.10% 40055719: ~0.10% 40056528: ~0.10% 40057840: ~2.40% 40057961: ~0.10% 40058892: ~0.10% 40058894: ~0.10% 40059125: ~0.20% 40059833: ~0.10% 40061829: ~0.10% 40062549: ~0.30% 40064247: ~0.10% 40068160: ~0.20% 40072151: ~0.10% 40075134: ~0.10% 40076362: ~0.10% 40077664: ~0.10% 40080106: ~0.10% 40082136: ~0.10% 40084344: ~0.10% 40084352: ~0.10% 40086374: ~0.10% 40088069: ~0.10% 40091096: ~0.50% 40092781: ~0.50% 40095502: ~1.50% 40095878: ~0.10% 40096141: ~0.10% 40096233: ~0.10% 40097452: ~0.10% 40100588: ~0.30% 40100616: ~0.10% 40101922: ~0.30% 40102009: ~0.50% 40102099: ~0.10% 40102493: ~0.10% 40102767: ~0.20% 40109001: ~0.90% 40129549: ~0.10% 40131970: ~0.10% 40132149: ~0.70% 40133983: ~0.10% 40139114: ~0.50% 40143939: ~0.10% 40150732: ~0.10% 40161228: ~0.10% 40161497: ~0.10% 40163523: ~1.30% 40163540: ~0.10% 40164091: ~0.20% 40165142: ~0.10% 40165244: ~0.10% 40165400: ~0.10% 40166035: ~0.50% 40166901: ~0.30% 40168812: ~0.10% 40184411: ~0.10% 40185276: ~0.10% 40221135: ~0.10% 40221160: ~0.10% 40222018: ~0.10% 40222310: ~0.10% 40223074: ~0.10% 40224143: ~0.10% 40224436: ~0.70% 40224834: ~0.20% 40227524: ~0.20% 40227933: ~0.10% 40227972: ~0.10% 40228442: ~0.10% 40229691: ~0.20% 40232185: ~0.10% 40235565: ~0.10% 40237992: ~0.10% 40238027: ~0.20% 40240215: ~0.20% 40242283: ~0.10% 40242661: ~0.30% 40243635: ~0.20% 40243802: ~0.20% 40243866: ~0.40% 40244241: ~0.10% 40244481: ~0.10% 40716404: ~0.10% 40725206: ~0.10% 40726652: ~0.10% 40729508: ~0.10% 40729516: ~0.20% 40729839: ~0.10% 40732672: ~0.40% 40747576: ~0.10% 40750268: ~0.20% 40798773: ~0.10% 40892708: ~0.50% 40964169: ~0.10% 40986256: ~0.20% 41013472: ~0.30% 41014233: ~0.30% 41053478: ~0.10% 41086226: ~0.10% 41115763: ~0.20% 41127461: ~0.10% 41146132: ~0.10% 41147856: ~0.10% 41159556: ~0.10% 41298578: ~0.10% 42480872: ~0.10% 42482607: ~0.30% 42629048: ~0.10% 42705389: ~0.10% 42706501: ~0.10% 42706654: ~0.30% 42706886: ~0.10% 42800417: ~0.10% 42898699: ~0.10% 42901417: ~0.20% 42901939: ~0.20% 42903074: ~0.10% 42923967: ~0.10% 42925756: ~0.20% 42926223: ~0.10% 42929215: ~0.10% 42930072: ~0.10% 42932855: ~0.10% 42938162: ~0.10% 42944003: ~0.10% 42945990: ~0.10% 42948684: ~0.10% 42949030: ~0.10% 42953550: ~0.10% 42959173: ~0.10% 42960271: ~0.10% 42961143: ~0.10% 42962994: ~0.10% 42968504: ~0.10% 43009035: ~0.10% 43255108: ~0.30% 43255876: ~0.10% 43262076: ~0.10% 43274735: ~0.20% 43274977: ~0.10% 43277134: ~0.10% 43278404: ~0.10% 43280976: ~0.20% 43282310: ~0.10% 43283762: ~0.10% 43296574: ~0.10% 43296821: ~0.10% 43517541: ~0.10% 43526033: ~0.20% 43531905: ~0.10% 43532236: ~0.40% 43560166: ~0.10% 43640125: ~0.10% 43781650: ~0.10% 44040418: ~0.20% 44091314: ~0.20% 44107418: ~0.20% 44814411: ~0.10% 45775584: ~0.10% 46221237: ~0.10% 46233932: ~0.10% 46234260: ~0.10% 46234271: ~0.30% 46234522: ~0.50% 46275172: ~0.10% 46276153: ~0.10% 46287328: ~0.10%	min: 6 tokens mean: 18.39 tokens max: 96 tokens	min: 5 tokens mean: 19.68 tokens max: 96 tokens	min: 5 tokens mean: 20.73 tokens max: 96 tokens

Samples:

query	positive	query_id	target_id	group_id	negative_0	negative_1	negative_2
`fentanyl 100 microgram/hour patch, 5`	`fentanyl 0.1 mg/hr transdermal system`	`q43342123_0`	`42902560`	`1154029`	`fentanyl 0.158 mg/actuat nasal spray`	`fentanyl 0.05 mg/ml nasal spray`	`fentanyl 1.89 mg unknown combination pack medical aids`
`sodium benzoate 1.83g/5ml oral solution`	`benzoate 366 mg/ml oral solution`	`q21235913_0`	`21119205`	`21119204`	`sodium chloride 23.2 mg/ml oral solution`	`fluticasone 0.125 mg/actuat inhalation powder`	`chlorhexidine 0.00001 mg/ml oral solution`
`meloxicam - meloxicam tablet`	`meloxicam 15 mg oral tablet`	`q45623828_0`	`1150347`	`1150347`	`econazole 0.0314 mg/mg topical cream`	`nabumetone 400 mg oral tablet`	`domestic cow skin extract 50 mg/ml injectable solution`

Loss: thirawat_trainer.training.colbert.KDLossWrapper

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 64
gradient_accumulation_steps: 2
learning_rate: 2e-05
num_train_epochs: 2
warmup_ratio: 0.1
bf16: True
dataloader_num_workers: 4
remove_unused_columns: False

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-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: 2
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: 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: False
dataloader_num_workers: 4
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: False
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}
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: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss
0.0011	5	50.3236	-
0.0021	10	59.3515	-
0.0032	15	62.6096	-
0.0043	20	71.224	-
0.0053	25	58.249	-
0.0064	30	53.1647	-
0.0075	35	74.0864	-
0.0086	40	68.1396	-
0.0096	45	67.5764	-
0.0107	50	59.9784	-
0.0118	55	51.3131	-
0.0128	60	68.0457	-
0.0139	65	58.9018	-
0.0150	70	59.4911	-
0.0160	75	58.0199	-
0.0171	80	63.7679	-
0.0182	85	60.7314	-
0.0192	90	77.8096	-
0.0203	95	66.2088	-
0.0214	100	65.6054	-
0.0224	105	80.6449	-
0.0235	110	69.9855	-
0.0246	115	56.6307	-
0.0257	120	46.5356	-
0.0267	125	57.7131	-
0.0278	130	49.0737	-
0.0289	135	50.7495	-
0.0299	140	57.1029	-
0.0310	145	45.4298	-
0.0321	150	57.8409	-
0.0331	155	68.8388	-
0.0342	160	64.5809	-
0.0353	165	54.5407	-
0.0363	170	48.341	-
0.0374	175	44.3806	-
0.0385	180	59.123	-
0.0395	185	59.4863	-
0.0406	190	65.4498	-
0.0417	195	52.9526	-
0.0428	200	46.6744	-
0.0438	205	37.1728	-
0.0449	210	63.3973	-
0.0460	215	51.1136	-
0.0470	220	40.9712	-
0.0481	225	46.4765	-
0.0492	230	47.3894	-
0.0502	235	36.866	-
0.0513	240	32.3704	-
0.0524	245	36.0298	-
0.0534	250	43.7503	-
0.0545	255	33.9929	-
0.0556	260	38.4924	-
0.0566	265	30.8329	-
0.0577	270	29.0382	-
0.0588	275	25.2408	-
0.0599	280	30.9856	-
0.0609	285	25.987	-
0.0620	290	24.4493	-
0.0631	295	25.6893	-
0.0641	300	29.4356	-
0.0652	305	22.2	-
0.0663	310	24.3048	-
0.0673	315	18.5714	-
0.0684	320	17.4822	-
0.0695	325	15.8966	-
0.0705	330	14.8771	-
0.0716	335	16.0905	-
0.0727	340	11.9737	-
0.0737	345	12.1407	-
0.0748	350	11.5492	-
0.0759	355	13.0664	-
0.0770	360	10.0428	-
0.0780	365	9.2962	-
0.0791	370	8.6304	-
0.0802	375	5.7954	-
0.0812	380	6.0213	-
0.0823	385	6.8873	-
0.0834	390	5.6347	-
0.0844	395	5.7346	-
0.0855	400	4.2817	-
0.0866	405	3.9817	-
0.0876	410	3.1871	-
0.0887	415	3.2088	-
0.0898	420	3.5216	-
0.0909	425	2.3657	-
0.0919	430	2.3054	-
0.0930	435	2.174	-
0.0941	440	2.4017	-
0.0951	445	2.1189	-
0.0962	450	2.3965	-
0.0973	455	1.7638	-
0.0983	460	1.4081	-
0.0994	465	1.5501	-
0.1005	470	1.4015	-
0.1015	475	1.3726	-
0.1026	480	1.1044	-
0.1037	485	1.1651	-
0.1047	490	1.1674	-
0.1058	495	1.2274	-
0.1069	500	1.0606	-
0.1080	505	1.2207	-
0.1090	510	1.12	-
0.1101	515	0.9925	-
0.1112	520	0.9542	-
0.1122	525	1.1911	-
0.1133	530	1.2418	-
0.1144	535	1.0926	-
0.1154	540	0.9052	-
0.1165	545	0.9725	-
0.1176	550	1.0631	-
0.1186	555	1.0069	-
0.1197	560	0.9301	-
0.1208	565	0.7413	-
0.1218	570	1.0359	-
0.1229	575	0.9793	-
0.1240	580	0.7139	-
0.1251	585	1.0595	-
0.1261	590	0.8869	-
0.1272	595	0.8318	-
0.1283	600	0.7328	-
0.1293	605	0.7785	-
0.1304	610	0.8507	-
0.1315	615	1.0039	-
0.1325	620	0.7514	-
0.1336	625	0.6666	-
0.1347	630	0.6639	-
0.1357	635	0.77	-
0.1368	640	0.8079	-
0.1379	645	0.8061	-
0.1389	650	0.7666	-
0.1400	655	0.7835	-
0.1411	660	0.7319	-
0.1422	665	0.5945	-
0.1432	670	0.7008	-
0.1443	675	0.9728	-
0.1454	680	0.7346	-
0.1464	685	0.8463	-
0.1475	690	0.7251	-
0.1486	695	0.7444	-
0.1496	700	0.6496	-
0.1507	705	0.7623	-
0.1518	710	0.8285	-
0.1528	715	0.886	-
0.1539	720	0.7274	-
0.1550	725	0.8669	-
0.1560	730	0.6741	-
0.1571	735	0.7396	-
0.1582	740	0.7197	-
0.1593	745	0.9099	-
0.1603	750	0.7344	-
0.1614	755	0.6107	-
0.1625	760	0.6929	-
0.1635	765	0.7019	-
0.1646	770	0.5759	-
0.1657	775	0.5736	-
0.1667	780	0.5878	-
0.1678	785	0.7349	-
0.1689	790	0.7691	-
0.1699	795	0.6228	-
0.1710	800	0.6394	-
0.1721	805	0.624	-
0.1732	810	0.6487	-
0.1742	815	0.6001	-
0.1753	820	0.6685	-
0.1764	825	0.8176	-
0.1774	830	0.6417	-
0.1785	835	0.6353	-
0.1796	840	0.5946	-
0.1806	845	0.6122	-
0.1817	850	0.6499	-
0.1828	855	0.601	-
0.1838	860	0.5404	-
0.1849	865	0.6574	-
0.1860	870	0.5377	-
0.1870	875	0.5537	-
0.1881	880	0.7377	-
0.1892	885	0.8466	-
0.1903	890	0.4808	-
0.1913	895	0.5482	-
0.1924	900	0.6588	-
0.1935	905	0.6953	-
0.1945	910	0.4803	-
0.1956	915	0.6869	-
0.1967	920	0.5393	-
0.1977	925	0.4829	-
0.1988	930	0.5071	-
0.1999	935	0.6828	-
0.2009	940	0.6468	-
0.2020	945	0.6169	-
0.2031	950	0.5389	-
0.2041	955	0.4527	-
0.2052	960	0.5405	-
0.2063	965	0.6583	-
0.2074	970	0.7037	-
0.2084	975	0.564	-
0.2095	980	0.4531	-
0.2106	985	0.5454	-
0.2116	990	0.4836	-
0.2127	995	0.5458	-
0.2138	1000	0.5202	-
0.2148	1005	0.5532	-
0.2159	1010	0.63	-
0.2170	1015	0.6299	-
0.2180	1020	0.5405	-
0.2191	1025	0.4379	-
0.2202	1030	0.7446	-
0.2212	1035	0.6652	-
0.2223	1040	0.6873	-
0.2234	1045	0.4904	-
0.2245	1050	0.5763	-
0.2255	1055	0.5801	-
0.2266	1060	0.518	-
0.2277	1065	0.5928	-
0.2287	1070	0.5575	-
0.2298	1075	0.497	-
0.2309	1080	0.4607	-
0.2319	1085	0.5531	-
0.2330	1090	0.5372	-
0.2341	1095	0.4641	-
0.2351	1100	0.4724	-
0.2362	1105	0.4422	-
0.2373	1110	0.4571	-
0.2383	1115	0.5104	-
0.2394	1120	0.5217	-
0.2405	1125	0.4481	-
0.2416	1130	0.5244	-
0.2426	1135	0.4593	-
0.2437	1140	0.5566	-
0.2448	1145	0.5473	-
0.2458	1150	0.3649	-
0.2469	1155	0.5466	-
0.2480	1160	0.4687	-
0.2490	1165	0.521	-
0.2501	1170	0.4539	-
0.2512	1175	0.5975	-
0.2522	1180	0.4108	-
0.2533	1185	0.4052	-
0.2544	1190	0.3805	-
0.2555	1195	0.508	-
0.2565	1200	0.4517	-
0.2576	1205	0.5546	-
0.2587	1210	0.5427	-
0.2597	1215	0.4869	-
0.2608	1220	0.5849	-
0.2619	1225	0.4608	-
0.2629	1230	0.6211	-
0.2640	1235	0.5136	-
0.2651	1240	0.4788	-
0.2661	1245	0.4763	-
0.2672	1250	0.5398	-
0.2683	1255	0.5782	-
0.2693	1260	0.4568	-
0.2704	1265	0.3874	-
0.2715	1270	0.4604	-
0.2726	1275	0.4202	-
0.2736	1280	0.4467	-
0.2747	1285	0.3631	-
0.2758	1290	0.5917	-
0.2768	1295	0.4315	-
0.2779	1300	0.3898	-
0.2790	1305	0.5546	-
0.2800	1310	0.4766	-
0.2811	1315	0.5883	-
0.2822	1320	0.4837	-
0.2832	1325	0.4576	-
0.2843	1330	0.3894	-
0.2854	1335	0.5239	-
0.2864	1340	0.4255	-
0.2875	1345	0.3944	-
0.2886	1350	0.6219	-
0.2897	1355	0.4791	-
0.2907	1360	0.4101	-
0.2918	1365	0.4422	-
0.2929	1370	0.4426	-
0.2939	1375	0.4108	-
0.2950	1380	0.4734	-
0.2961	1385	0.4678	-
0.2971	1390	0.4334	-
0.2982	1395	0.4211	-
0.2993	1400	0.5807	-
0.3003	1405	0.5956	-
0.3014	1410	0.4175	-
0.3025	1415	0.4233	-
0.3035	1420	0.4118	-
0.3046	1425	0.3919	-
0.3057	1430	0.5152	-
0.3068	1435	0.393	-
0.3078	1440	0.3765	-
0.3089	1445	0.4476	-
0.3100	1450	0.5094	-
0.3110	1455	0.4496	-
0.3121	1460	0.4305	-
0.3132	1465	0.495	-
0.3142	1470	0.5561	-
0.3153	1475	0.4432	-
0.3164	1480	0.3382	-
0.3174	1485	0.4209	-
0.3185	1490	0.3776	-
0.3196	1495	0.4271	-
0.3206	1500	0.5628	-
0.3217	1505	0.4438	-
0.3228	1510	0.3863	-
0.3239	1515	0.5629	-
0.3249	1520	0.4851	-
0.3260	1525	0.3386	-
0.3271	1530	0.4546	-
0.3281	1535	0.4147	-
0.3292	1540	0.403	-
0.3303	1545	0.4155	-
0.3313	1550	0.4831	-
0.3324	1555	0.388	-
0.3335	1560	0.4184	-
0.3345	1565	0.3645	-
0.3356	1570	0.4653	-
0.3367	1575	0.5196	-
0.3378	1580	0.5252	-
0.3388	1585	0.6971	-
0.3399	1590	0.3881	-
0.3410	1595	0.4302	-
0.3420	1600	0.3701	-
0.3431	1605	0.3893	-
0.3442	1610	0.4246	-
0.3452	1615	0.3006	-
0.3463	1620	0.7078	-
0.3474	1625	0.375	-
0.3484	1630	0.4425	-
0.3495	1635	0.3841	-
0.3506	1640	0.4075	-
0.3516	1645	0.5383	-
0.3527	1650	0.4623	-
0.3538	1655	0.3986	-
0.3549	1660	0.4038	-
0.3559	1665	0.5615	-
0.3570	1670	0.5535	-
0.3581	1675	0.3316	-
0.3591	1680	0.3916	-
0.3602	1685	0.3723	-
0.3613	1690	0.5188	-
0.3623	1695	0.3817	-
0.3634	1700	0.4036	-
0.3645	1705	0.4224	-
0.3655	1710	0.4171	-
0.3666	1715	0.4122	-
0.3677	1720	0.4024	-
0.3687	1725	0.3882	-
0.3698	1730	0.3282	-
0.3709	1735	0.2961	-
0.3720	1740	0.4568	-
0.3730	1745	0.3544	-
0.3741	1750	0.4282	-
0.3752	1755	0.3798	-
0.3762	1760	0.4618	-
0.3773	1765	0.3876	-
0.3784	1770	0.3393	-
0.3794	1775	0.4276	-
0.3805	1780	0.4641	-
0.3816	1785	0.4248	-
0.3826	1790	0.3601	-
0.3837	1795	0.3384	-
0.3848	1800	0.3598	-
0.3858	1805	0.4363	-
0.3869	1810	0.335	-
0.3880	1815	0.3892	-
0.3891	1820	0.4103	-
0.3901	1825	0.3959	-
0.3912	1830	0.3593	-
0.3923	1835	0.4366	-
0.3933	1840	0.3633	-
0.3944	1845	0.4333	-
0.3955	1850	0.4825	-
0.3965	1855	0.3041	-
0.3976	1860	0.3416	-
0.3987	1865	0.4578	-
0.3997	1870	0.5441	-
0.4008	1875	0.5124	-
0.4019	1880	0.4026	-
0.4029	1885	0.4537	-
0.4040	1890	0.3983	-
0.4051	1895	0.3894	-
0.4062	1900	0.4747	-
0.4072	1905	0.4042	-
0.4083	1910	0.3477	-
0.4094	1915	0.4512	-
0.4104	1920	0.3451	-
0.4115	1925	0.3983	-
0.4126	1930	0.62	-
0.4136	1935	0.4632	-
0.4147	1940	0.3757	-
0.4158	1945	0.4752	-
0.4168	1950	0.453	-
0.4179	1955	0.4348	-
0.4190	1960	0.3944	-
0.4201	1965	0.3702	-
0.4211	1970	0.3888	-
0.4222	1975	0.3968	-
0.4233	1980	0.3609	-
0.4243	1985	0.4328	-
0.4254	1990	0.516	-
0.4265	1995	0.3605	-
0.4275	2000	0.3094	-
0.4286	2005	0.3866	-
0.4297	2010	0.3943	-
0.4307	2015	0.3931	-
0.4318	2020	0.3728	-
0.4329	2025	0.2953	-
0.4339	2030	0.3547	-
0.4350	2035	0.4275	-
0.4361	2040	0.2959	-
0.4372	2045	0.3245	-
0.4382	2050	0.4148	-
0.4393	2055	0.3542	-
0.4404	2060	0.4493	-
0.4414	2065	0.376	-
0.4425	2070	0.322	-
0.4436	2075	0.3227	-
0.4446	2080	0.3046	-
0.4457	2085	0.3064	-
0.4468	2090	0.3199	-
0.4478	2095	0.3774	-
0.4489	2100	0.3212	-
0.4500	2105	0.3302	-
0.4510	2110	0.3205	-
0.4521	2115	0.3673	-
0.4532	2120	0.3752	-
0.4543	2125	0.3192	-
0.4553	2130	0.3372	-
0.4564	2135	0.321	-
0.4575	2140	0.4086	-
0.4585	2145	0.4092	-
0.4596	2150	0.4591	-
0.4607	2155	0.503	-
0.4617	2160	0.4767	-
0.4628	2165	0.3425	-
0.4639	2170	0.372	-
0.4649	2175	0.4671	-
0.4660	2180	0.3767	-
0.4671	2185	0.3096	-
0.4681	2190	0.3654	-
0.4692	2195	0.4243	-
0.4703	2200	0.4411	-
0.4714	2205	0.5001	-
0.4724	2210	0.3485	-
0.4735	2215	0.3546	-
0.4746	2220	0.4037	-
0.4756	2225	0.3114	-
0.4767	2230	0.3422	-
0.4778	2235	0.4241	-
0.4788	2240	0.3912	-
0.4799	2245	0.3748	-
0.4810	2250	0.3746	-
0.4820	2255	0.3544	-
0.4831	2260	0.3246	-
0.4842	2265	0.3739	-
0.4853	2270	0.3606	-
0.4863	2275	0.3891	-
0.4874	2280	0.2855	-
0.4885	2285	0.4829	-
0.4895	2290	0.4529	-
0.4906	2295	0.4819	-
0.4917	2300	0.3453	-
0.4927	2305	0.3425	-
0.4938	2310	0.2884	-
0.4949	2315	0.3184	-
0.4959	2320	0.3043	-
0.4970	2325	0.3746	-
0.4981	2330	0.4023	-
0.4991	2335	0.2994	-
0.5002	2340	0.3858	-
0.5013	2345	0.3488	-
0.5024	2350	0.4467	-
0.5034	2355	0.3199	-
0.5045	2360	0.3124	-
0.5056	2365	0.3229	-
0.5066	2370	0.3416	-
0.5077	2375	0.3555	-
0.5088	2380	0.3078	-
0.5098	2385	0.3105	-
0.5109	2390	0.3565	-
0.5120	2395	0.3113	-
0.5130	2400	0.4326	-
0.5141	2405	0.4395	-
0.5152	2410	0.3549	-
0.5162	2415	0.4271	-
0.5173	2420	0.3136	-
0.5184	2425	0.3235	-
0.5195	2430	0.2909	-
0.5205	2435	0.3296	-
0.5216	2440	0.4124	-
0.5227	2445	0.4444	-
0.5237	2450	0.3649	-
0.5248	2455	0.3897	-
0.5259	2460	0.36	-
0.5269	2465	0.3899	-
0.5280	2470	0.3303	-
0.5291	2475	0.3115	-
0.5301	2480	0.4637	-
0.5312	2485	0.4402	-
0.5323	2490	0.2825	-
0.5333	2495	0.3389	-
0.5344	2500	0.3513	-
0.5355	2505	0.4301	-
0.5366	2510	0.3733	-
0.5376	2515	0.3241	-
0.5387	2520	0.3457	-
0.5398	2525	0.4533	-
0.5408	2530	0.3397	-
0.5419	2535	0.4097	-
0.5430	2540	0.3083	-
0.5440	2545	0.3199	-
0.5451	2550	0.3971	-
0.5462	2555	0.5229	-
0.5472	2560	0.3765	-
0.5483	2565	0.3727	-
0.5494	2570	0.3252	-
0.5504	2575	0.2897	-
0.5515	2580	0.2867	-
0.5526	2585	0.3058	-
0.5537	2590	0.461	-
0.5547	2595	0.2341	-
0.5558	2600	0.3446	-
0.5569	2605	0.2844	-
0.5579	2610	0.3414	-
0.5590	2615	0.3575	-
0.5601	2620	0.3799	-
0.5611	2625	0.3553	-
0.5622	2630	0.3938	-
0.5633	2635	0.4941	-
0.5643	2640	0.3454	-
0.5654	2645	0.3009	-
0.5665	2650	0.4625	-
0.5676	2655	0.3426	-
0.5686	2660	0.3403	-
0.5697	2665	0.3571	-
0.5708	2670	0.235	-
0.5718	2675	0.3939	-
0.5729	2680	0.3041	-
0.5740	2685	0.398	-
0.5750	2690	0.4327	-
0.5761	2695	0.3622	-
0.5772	2700	0.3461	-
0.5782	2705	0.409	-
0.5793	2710	0.3561	-
0.5804	2715	0.3608	-
0.5814	2720	0.3824	-
0.5825	2725	0.3403	-
0.5836	2730	0.4133	-
0.5847	2735	0.4104	-
0.5857	2740	0.2704	-
0.5868	2745	0.275	-
0.5879	2750	0.3642	-
0.5889	2755	0.3882	-
0.5900	2760	0.445	-
0.5911	2765	0.275	-
0.5921	2770	0.3727	-
0.5932	2775	0.3458	-
0.5943	2780	0.3775	-
0.5953	2785	0.3731	-
0.5964	2790	0.3073	-
0.5975	2795	0.3113	-
0.5985	2800	0.3666	-
0.5996	2805	0.3769	-
0.6007	2810	0.3267	-
0.6018	2815	0.2513	-
0.6028	2820	0.273	-
0.6039	2825	0.3712	-
0.6050	2830	0.4728	-
0.6060	2835	0.3608	-
0.6071	2840	0.3765	-
0.6082	2845	0.3285	-
0.6092	2850	0.4822	-
0.6103	2855	0.3185	-
0.6114	2860	0.3027	-
0.6124	2865	0.3819	-
0.6135	2870	0.2819	-
0.6146	2875	0.3369	-
0.6156	2880	0.331	-
0.6167	2885	0.3662	-
0.6178	2890	0.3472	-
0.6189	2895	0.387	-
0.6199	2900	0.3017	-
0.6210	2905	0.2628	-
0.6221	2910	0.2683	-
0.6231	2915	0.3189	-
0.6242	2920	0.3389	-
0.6253	2925	0.3552	-
0.6263	2930	0.2784	-
0.6274	2935	0.3416	-
0.6285	2940	0.3805	-
0.6295	2945	0.4616	-
0.6306	2950	0.3584	-
0.6317	2955	0.3439	-
0.6327	2960	0.3569	-
0.6338	2965	0.2775	-
0.6349	2970	0.3758	-
0.6360	2975	0.4178	-
0.6370	2980	0.2725	-
0.6381	2985	0.4838	-
0.6392	2990	0.3184	-
0.6402	2995	0.3265	-
0.6413	3000	0.3344	-
0.6424	3005	0.284	-
0.6434	3010	0.3337	-
0.6445	3015	0.3085	-
0.6456	3020	0.3685	-
0.6466	3025	0.4621	-
0.6477	3030	0.2958	-
0.6488	3035	0.4165	-
0.6499	3040	0.336	-
0.6509	3045	0.328	-
0.6520	3050	0.3256	-
0.6531	3055	0.2877	-
0.6541	3060	0.3468	-
0.6552	3065	0.3836	-
0.6563	3070	0.3084	-
0.6573	3075	0.2879	-
0.6584	3080	0.2836	-
0.6595	3085	0.3049	-
0.6605	3090	0.3972	-
0.6616	3095	0.3189	-
0.6627	3100	0.207	-
0.6637	3105	0.2844	-
0.6648	3110	0.3583	-
0.6659	3115	0.2681	-
0.6670	3120	0.3788	-
0.6680	3125	0.3335	-
0.6691	3130	0.2958	-
0.6702	3135	0.343	-
0.6712	3140	0.3026	-
0.6723	3145	0.3575	-
0.6734	3150	0.3962	-
0.6744	3155	0.292	-
0.6755	3160	0.4226	-
0.6766	3165	0.3999	-
0.6776	3170	0.3949	-
0.6787	3175	0.2844	-
0.6798	3180	0.3003	-
0.6808	3185	0.2636	-
0.6819	3190	0.2334	-
0.6830	3195	0.2574	-
0.6841	3200	0.3476	-
0.6851	3205	0.3763	-
0.6862	3210	0.2873	-
0.6873	3215	0.2859	-
0.6883	3220	0.3335	-
0.6894	3225	0.3712	-
0.6905	3230	0.4072	-
0.6915	3235	0.3442	-
0.6926	3240	0.32	-
0.6937	3245	0.3076	-
0.6947	3250	0.3134	-
0.6958	3255	0.3155	-
0.6969	3260	0.3097	-
0.6979	3265	0.2727	-
0.6990	3270	0.3666	-
0.7001	3275	0.3049	-
0.7012	3280	0.3506	-
0.7022	3285	0.3027	-
0.7033	3290	0.2706	-
0.7044	3295	0.3165	-
0.7054	3300	0.3567	-
0.7065	3305	0.2795	-
0.7076	3310	0.2905	-
0.7086	3315	0.2946	-
0.7097	3320	0.298	-
0.7108	3325	0.289	-
0.7118	3330	0.3194	-
0.7129	3335	0.2719	-
0.7140	3340	0.3079	-
0.7150	3345	0.3373	-
0.7161	3350	0.3103	-
0.7172	3355	0.3384	-
0.7183	3360	0.3785	-
0.7193	3365	0.2583	-
0.7204	3370	0.2992	-
0.7215	3375	0.2196	-
0.7225	3380	0.3438	-
0.7236	3385	0.333	-
0.7247	3390	0.4422	-
0.7257	3395	0.458	-
0.7268	3400	0.2526	-
0.7279	3405	0.2443	-
0.7289	3410	0.4024	-
0.7300	3415	0.3057	-
0.7311	3420	0.3417	-
0.7322	3425	0.3535	-
0.7332	3430	0.3814	-
0.7343	3435	0.3196	-
0.7354	3440	0.2738	-
0.7364	3445	0.2127	-
0.7375	3450	0.2415	-
0.7386	3455	0.368	-
0.7396	3460	0.2965	-
0.7407	3465	0.3676	-
0.7418	3470	0.4793	-
0.7428	3475	0.2924	-
0.7439	3480	0.3051	-
0.7450	3485	0.3206	-
0.7460	3490	0.3062	-
0.7471	3495	0.3099	-
0.7482	3500	0.3528	-
0.7493	3505	0.3075	-
0.7503	3510	0.3132	-
0.7514	3515	0.3789	-
0.7525	3520	0.3316	-
0.7535	3525	0.3113	-
0.7546	3530	0.2481	-
0.7557	3535	0.3174	-
0.7567	3540	0.3116	-
0.7578	3545	0.29	-
0.7589	3550	0.33	-
0.7599	3555	0.2002	-
0.7610	3560	0.3566	-
0.7621	3565	0.2618	-
0.7631	3570	0.316	-
0.7642	3575	0.2997	-
0.7653	3580	0.2809	-
0.7664	3585	0.2023	-
0.7674	3590	0.2726	-
0.7685	3595	0.2878	-
0.7696	3600	0.4026	-
0.7706	3605	0.3205	-
0.7717	3610	0.3092	-
0.7728	3615	0.3249	-
0.7738	3620	0.2898	-
0.7749	3625	0.3464	-
0.7760	3630	0.3755	-
0.7770	3635	0.3236	-
0.7781	3640	0.3298	-
0.7792	3645	0.3301	-
0.7802	3650	0.2428	-
0.7813	3655	0.2789	-
0.7824	3660	0.4398	-
0.7835	3665	0.3211	-
0.7845	3670	0.3079	-
0.7856	3675	0.2765	-
0.7867	3680	0.2414	-
0.7877	3685	0.3142	-
0.7888	3690	0.4895	-
0.7899	3695	0.3058	-
0.7909	3700	0.2568	-
0.7920	3705	0.2505	-
0.7931	3710	0.3416	-
0.7941	3715	0.2867	-
0.7952	3720	0.2696	-
0.7963	3725	0.293	-
0.7973	3730	0.2741	-
0.7984	3735	0.3276	-
0.7995	3740	0.2688	-
0.8006	3745	0.2408	-
0.8016	3750	0.2926	-
0.8027	3755	0.3119	-
0.8038	3760	0.3018	-
0.8048	3765	0.3448	-
0.8059	3770	0.3539	-
0.8070	3775	0.2776	-
0.8080	3780	0.2255	-
0.8091	3785	0.2507	-
0.8102	3790	0.3463	-
0.8112	3795	0.2849	-
0.8123	3800	0.2678	-
0.8134	3805	0.3446	-
0.8145	3810	0.344	-
0.8155	3815	0.3305	-
0.8166	3820	0.2728	-
0.8177	3825	0.4181	-
0.8187	3830	0.3351	-
0.8198	3835	0.2646	-
0.8209	3840	0.2628	-
0.8219	3845	0.2747	-
0.8230	3850	0.2253	-
0.8241	3855	0.2741	-
0.8251	3860	0.3982	-
0.8262	3865	0.3014	-
0.8273	3870	0.2696	-
0.8283	3875	0.2607	-
0.8294	3880	0.3154	-
0.8305	3885	0.3275	-
0.8316	3890	0.274	-
0.8326	3895	0.3042	-
0.8337	3900	0.2692	-
0.8348	3905	0.2505	-
0.8358	3910	0.3762	-
0.8369	3915	0.2467	-
0.8380	3920	0.2227	-
0.8390	3925	0.2513	-
0.8401	3930	0.313	-
0.8412	3935	0.265	-
0.8422	3940	0.2839	-
0.8433	3945	0.3432	-
0.8444	3950	0.2168	-
0.8454	3955	0.3083	-
0.8465	3960	0.3362	-
0.8476	3965	0.2897	-
0.8487	3970	0.299	-
0.8497	3975	0.3421	-
0.8508	3980	0.2886	-
0.8519	3985	0.3563	-
0.8529	3990	0.3204	-
0.8540	3995	0.3025	-
0.8551	4000	0.2813	-
0.8561	4005	0.2787	-
0.8572	4010	0.2643	-
0.8583	4015	0.291	-
0.8593	4020	0.2467	-
0.8604	4025	0.3489	-
0.8615	4030	0.2613	-
0.8625	4035	0.3007	-
0.8636	4040	0.2685	-
0.8647	4045	0.2984	-
0.8658	4050	0.2321	-
0.8668	4055	0.2417	-
0.8679	4060	0.3164	-
0.8690	4065	0.3769	-
0.8700	4070	0.3385	-
0.8711	4075	0.3135	-
0.8722	4080	0.3295	-
0.8732	4085	0.2992	-
0.8743	4090	0.2723	-
0.8754	4095	0.2805	-
0.8764	4100	0.2171	-
0.8775	4105	0.2618	-
0.8786	4110	0.2994	-
0.8796	4115	0.2866	-
0.8807	4120	0.2091	-
0.8818	4125	0.3285	-
0.8829	4130	0.2854	-
0.8839	4135	0.3004	-
0.8850	4140	0.2335	-
0.8861	4145	0.2919	-
0.8871	4150	0.2786	-
0.8882	4155	0.3312	-
0.8893	4160	0.2677	-
0.8903	4165	0.2774	-
0.8914	4170	0.2649	-
0.8925	4175	0.3119	-
0.8935	4180	0.243	-
0.8946	4185	0.292	-
0.8957	4190	0.352	-
0.8968	4195	0.2422	-
0.8978	4200	0.2427	-
0.8989	4205	0.2246	-
0.9000	4210	0.2275	-
0.9010	4215	0.3766	-
0.9021	4220	0.2748	-
0.9032	4225	0.2941	-
0.9042	4230	0.2693	-
0.9053	4235	0.3058	-
0.9064	4240	0.2167	-
0.9074	4245	0.2371	-
0.9085	4250	0.2199	-
0.9096	4255	0.2803	-
0.9106	4260	0.2765	-
0.9117	4265	0.2734	-
0.9128	4270	0.3345	-
0.9139	4275	0.2873	-
0.9149	4280	0.2506	-
0.9160	4285	0.2335	-
0.9171	4290	0.3218	-
0.9181	4295	0.2971	-
0.9192	4300	0.2296	-
0.9203	4305	0.3087	-
0.9213	4310	0.3353	-
0.9224	4315	0.3046	-
0.9235	4320	0.3431	-
0.9245	4325	0.2707	-
0.9256	4330	0.284	-
0.9267	4335	0.2714	-
0.9277	4340	0.2751	-
0.9288	4345	0.3047	-
0.9299	4350	0.2867	-
0.9310	4355	0.2895	-
0.9320	4360	0.2749	-
0.9331	4365	0.2549	-
0.9342	4370	0.2648	-
0.9352	4375	0.3001	-
0.9363	4380	0.3198	-
0.9374	4385	0.2583	-
0.9384	4390	0.2894	-
0.9395	4395	0.3017	-
0.9406	4400	0.2754	-
0.9416	4405	0.3289	-
0.9427	4410	0.2787	-
0.9438	4415	0.2406	-
0.9448	4420	0.3144	-
0.9459	4425	0.3237	-
0.9470	4430	0.2641	-
0.9481	4435	0.2875	-
0.9491	4440	0.3211	-
0.9502	4445	0.2403	-
0.9513	4450	0.3018	-
0.9523	4455	0.2531	-
0.9534	4460	0.349	-
0.9545	4465	0.238	-
0.9555	4470	0.2768	-
0.9566	4475	0.2944	-
0.9577	4480	0.3043	-
0.9587	4485	0.2434	-
0.9598	4490	0.2451	-
0.9609	4495	0.213	-
0.9619	4500	0.26	-
0.9630	4505	0.3173	-
0.9641	4510	0.2745	-
0.9652	4515	0.2824	-
0.9662	4520	0.2769	-
0.9673	4525	0.2464	-
0.9684	4530	0.3213	-
0.9694	4535	0.3606	-
0.9705	4540	0.2183	-
0.9716	4545	0.2567	-
0.9726	4550	0.2521	-
0.9737	4555	0.2696	-
0.9748	4560	0.3409	-
0.9758	4565	0.2732	-
0.9769	4570	0.3003	-
0.9780	4575	0.3426	-
0.9791	4580	0.2873	-
0.9801	4585	0.3	-
0.9812	4590	0.2074	-
0.9823	4595	0.2521	-
0.9833	4600	0.3123	-
0.9844	4605	0.2492	-
0.9855	4610	0.3012	-
0.9865	4615	0.2516	-
0.9876	4620	0.3629	-
0.9887	4625	0.2484	-
0.9897	4630	0.2749	-
0.9908	4635	0.2064	-
0.9919	4640	0.3089	-
0.9929	4645	0.3145	-
0.9940	4650	0.2357	-
0.9951	4655	0.2803	-
0.9962	4660	0.2437	-
0.9972	4665	0.2708	-
0.9983	4670	0.253	-
0.9994	4675	0.2219	-
1.0	4678	-	0.1271
1.0004	4680	0.2621	-
1.0015	4685	0.382	-
1.0026	4690	0.3026	-
1.0036	4695	0.3643	-
1.0047	4700	0.382	-
1.0058	4705	0.3082	-
1.0068	4710	0.3411	-
1.0079	4715	0.317	-
1.0090	4720	0.4396	-
1.0100	4725	0.4034	-
1.0111	4730	0.2875	-
1.0122	4735	0.4919	-
1.0133	4740	0.3118	-
1.0143	4745	0.3976	-
1.0154	4750	0.2919	-
1.0165	4755	0.5247	-
1.0175	4760	0.2126	-
1.0186	4765	0.3455	-
1.0197	4770	0.3498	-
1.0207	4775	0.29	-
1.0218	4780	0.3684	-
1.0229	4785	0.3033	-
1.0239	4790	0.2747	-
1.0250	4795	0.2813	-
1.0261	4800	0.2945	-
1.0271	4805	0.421	-
1.0282	4810	0.3365	-
1.0293	4815	0.3708	-
1.0304	4820	0.4345	-
1.0314	4825	0.3728	-
1.0325	4830	0.3173	-
1.0336	4835	0.2574	-
1.0346	4840	0.3128	-
1.0357	4845	0.2542	-
1.0368	4850	0.1649	-
1.0378	4855	0.4091	-
1.0389	4860	0.3553	-
1.0400	4865	0.5474	-
1.0410	4870	0.3586	-
1.0421	4875	0.2589	-
1.0432	4880	0.3143	-
1.0442	4885	0.3248	-
1.0453	4890	0.4631	-
1.0464	4895	0.2977	-
1.0475	4900	0.2487	-
1.0485	4905	0.3554	-
1.0496	4910	0.3579	-
1.0507	4915	0.3562	-
1.0517	4920	0.4625	-
1.0528	4925	0.2834	-
1.0539	4930	0.4072	-
1.0549	4935	0.2831	-
1.0560	4940	0.2642	-
1.0571	4945	0.2887	-
1.0581	4950	0.4394	-
1.0592	4955	0.2957	-
1.0603	4960	0.298	-
1.0614	4965	0.2643	-
1.0624	4970	0.3147	-
1.0635	4975	0.3433	-
1.0646	4980	0.2797	-
1.0656	4985	0.2958	-
1.0667	4990	0.3972	-
1.0678	4995	0.3361	-
1.0688	5000	0.3174	-
1.0699	5005	0.3346	-
1.0710	5010	0.326	-
1.0720	5015	0.2975	-
1.0731	5020	0.3303	-
1.0742	5025	0.2285	-
1.0752	5030	0.3798	-
1.0763	5035	0.2882	-
1.0774	5040	0.4249	-
1.0785	5045	0.4139	-
1.0795	5050	0.4502	-
1.0806	5055	0.3521	-
1.0817	5060	0.342	-
1.0827	5065	0.294	-
1.0838	5070	0.2994	-
1.0849	5075	0.3467	-
1.0859	5080	0.443	-
1.0870	5085	0.2759	-
1.0881	5090	0.2614	-
1.0891	5095	0.3649	-
1.0902	5100	0.3699	-
1.0913	5105	0.3785	-
1.0923	5110	0.325	-
1.0934	5115	0.3954	-
1.0945	5120	0.2887	-
1.0956	5125	0.2565	-
1.0966	5130	0.2661	-
1.0977	5135	0.286	-
1.0988	5140	0.3969	-
1.0998	5145	0.2451	-
1.1009	5150	0.2472	-
1.1020	5155	0.286	-
1.1030	5160	0.3109	-
1.1041	5165	0.441	-
1.1052	5170	0.3494	-
1.1062	5175	0.345	-
1.1073	5180	0.2503	-
1.1084	5185	0.3922	-
1.1094	5190	0.3429	-
1.1105	5195	0.3437	-
1.1116	5200	0.2751	-
1.1127	5205	0.3634	-
1.1137	5210	0.2685	-
1.1148	5215	0.2121	-
1.1159	5220	0.4083	-
1.1169	5225	0.2497	-
1.1180	5230	0.3521	-
1.1191	5235	0.3048	-
1.1201	5240	0.2622	-
1.1212	5245	0.2651	-
1.1223	5250	0.2971	-
1.1233	5255	0.3186	-
1.1244	5260	0.3277	-
1.1255	5265	0.2521	-
1.1265	5270	0.3114	-
1.1276	5275	0.3582	-
1.1287	5280	0.3617	-
1.1298	5285	0.328	-
1.1308	5290	0.2627	-
1.1319	5295	0.3054	-
1.1330	5300	0.5342	-
1.1340	5305	0.2956	-
1.1351	5310	0.3008	-
1.1362	5315	0.4844	-
1.1372	5320	0.2941	-
1.1383	5325	0.3928	-
1.1394	5330	0.2804	-
1.1404	5335	0.2605	-
1.1415	5340	0.2871	-
1.1426	5345	0.2106	-
1.1437	5350	0.3053	-
1.1447	5355	0.3719	-
1.1458	5360	0.396	-
1.1469	5365	0.4105	-
1.1479	5370	0.3882	-
1.1490	5375	0.2815	-
1.1501	5380	0.2477	-
1.1511	5385	0.2798	-
1.1522	5390	0.2758	-
1.1533	5395	0.2938	-
1.1543	5400	0.3946	-
1.1554	5405	0.2348	-
1.1565	5410	0.2701	-
1.1575	5415	0.3154	-
1.1586	5420	0.2617	-
1.1597	5425	0.3529	-
1.1608	5430	0.2858	-
1.1618	5435	0.42	-
1.1629	5440	0.3894	-
1.1640	5445	0.3073	-
1.1650	5450	0.2824	-
1.1661	5455	0.3139	-
1.1672	5460	0.4651	-
1.1682	5465	0.2611	-
1.1693	5470	0.2663	-
1.1704	5475	0.252	-
1.1714	5480	0.4996	-
1.1725	5485	0.2786	-
1.1736	5490	0.2628	-
1.1746	5495	0.3326	-
1.1757	5500	0.3294	-
1.1768	5505	0.341	-
1.1779	5510	0.2878	-
1.1789	5515	0.354	-
1.1800	5520	0.2667	-
1.1811	5525	0.2932	-
1.1821	5530	0.2608	-
1.1832	5535	0.3078	-
1.1843	5540	0.4028	-
1.1853	5545	0.3429	-
1.1864	5550	0.3481	-
1.1875	5555	0.2931	-
1.1885	5560	0.2719	-
1.1896	5565	0.3968	-
1.1907	5570	0.4653	-
1.1917	5575	0.2727	-
1.1928	5580	0.4521	-
1.1939	5585	0.3042	-
1.1950	5590	0.2687	-
1.1960	5595	0.2892	-
1.1971	5600	0.2515	-
1.1982	5605	0.3022	-
1.1992	5610	0.4018	-
1.2003	5615	0.2532	-
1.2014	5620	0.2285	-
1.2024	5625	0.3307	-
1.2035	5630	0.2353	-
1.2046	5635	0.3137	-
1.2056	5640	0.2549	-
1.2067	5645	0.2901	-
1.2078	5650	0.4423	-
1.2088	5655	0.2959	-
1.2099	5660	0.2523	-
1.2110	5665	0.1746	-
1.2121	5670	0.3541	-
1.2131	5675	0.2801	-
1.2142	5680	0.2846	-
1.2153	5685	0.3503	-
1.2163	5690	0.2831	-
1.2174	5695	0.2708	-
1.2185	5700	0.2846	-
1.2195	5705	0.3136	-
1.2206	5710	0.3377	-
1.2217	5715	0.4648	-
1.2227	5720	0.371	-
1.2238	5725	0.3419	-
1.2249	5730	0.3034	-
1.2260	5735	0.2912	-
1.2270	5740	0.3251	-
1.2281	5745	0.3989	-
1.2292	5750	0.3193	-
1.2302	5755	0.3586	-
1.2313	5760	0.2832	-
1.2324	5765	0.389	-
1.2334	5770	0.3889	-
1.2345	5775	0.2243	-
1.2356	5780	0.2416	-
1.2366	5785	0.3066	-
1.2377	5790	0.3227	-
1.2388	5795	0.3194	-
1.2398	5800	0.3851	-
1.2409	5805	0.2793	-
1.2420	5810	0.2482	-
1.2431	5815	0.3311	-
1.2441	5820	0.2456	-
1.2452	5825	0.3267	-
1.2463	5830	0.2496	-
1.2473	5835	0.2896	-
1.2484	5840	0.2717	-
1.2495	5845	0.344	-
1.2505	5850	0.1997	-
1.2516	5855	0.2454	-
1.2527	5860	0.2923	-
1.2537	5865	0.268	-
1.2548	5870	0.2469	-
1.2559	5875	0.2488	-
1.2569	5880	0.2809	-
1.2580	5885	0.2967	-
1.2591	5890	0.3366	-
1.2602	5895	0.2968	-
1.2612	5900	0.2	-
1.2623	5905	0.2761	-
1.2634	5910	0.2177	-
1.2644	5915	0.2721	-
1.2655	5920	0.2592	-
1.2666	5925	0.453	-
1.2676	5930	0.1952	-
1.2687	5935	0.3269	-
1.2698	5940	0.2776	-
1.2708	5945	0.2627	-
1.2719	5950	0.2524	-
1.2730	5955	0.2713	-
1.2740	5960	0.2618	-
1.2751	5965	0.2735	-
1.2762	5970	0.327	-
1.2773	5975	0.3294	-
1.2783	5980	0.3299	-
1.2794	5985	0.2328	-
1.2805	5990	0.2554	-
1.2815	5995	0.3285	-
1.2826	6000	0.2884	-
1.2837	6005	0.3201	-
1.2847	6010	0.3529	-
1.2858	6015	0.3113	-
1.2869	6020	0.386	-
1.2879	6025	0.2453	-
1.2890	6030	0.3845	-
1.2901	6035	0.3311	-
1.2912	6040	0.3556	-
1.2922	6045	0.2491	-
1.2933	6050	0.3478	-
1.2944	6055	0.175	-
1.2954	6060	0.2475	-
1.2965	6065	0.2793	-
1.2976	6070	0.3745	-
1.2986	6075	0.2619	-
1.2997	6080	0.2882	-
1.3008	6085	0.2651	-
1.3018	6090	0.3257	-
1.3029	6095	0.2626	-
1.3040	6100	0.3391	-
1.3050	6105	0.2868	-
1.3061	6110	0.2358	-
1.3072	6115	0.3505	-
1.3083	6120	0.3643	-
1.3093	6125	0.2729	-
1.3104	6130	0.2363	-
1.3115	6135	0.3173	-
1.3125	6140	0.2797	-
1.3136	6145	0.3054	-
1.3147	6150	0.2596	-
1.3157	6155	0.3871	-
1.3168	6160	0.2798	-
1.3179	6165	0.2532	-
1.3189	6170	0.2649	-
1.3200	6175	0.3457	-
1.3211	6180	0.4062	-
1.3221	6185	0.4318	-
1.3232	6190	0.2305	-
1.3243	6195	0.218	-
1.3254	6200	0.2738	-
1.3264	6205	0.3043	-
1.3275	6210	0.2055	-
1.3286	6215	0.2528	-
1.3296	6220	0.3514	-
1.3307	6225	0.3199	-
1.3318	6230	0.2453	-
1.3328	6235	0.3237	-
1.3339	6240	0.3409	-
1.3350	6245	0.268	-
1.3360	6250	0.4072	-
1.3371	6255	0.3601	-
1.3382	6260	0.3997	-
1.3392	6265	0.2062	-
1.3403	6270	0.2703	-
1.3414	6275	0.2498	-
1.3425	6280	0.3418	-
1.3435	6285	0.2046	-
1.3446	6290	0.3267	-
1.3457	6295	0.2756	-
1.3467	6300	0.2808	-
1.3478	6305	0.3037	-
1.3489	6310	0.3837	-
1.3499	6315	0.2496	-
1.3510	6320	0.3131	-
1.3521	6325	0.3038	-
1.3531	6330	0.2872	-
1.3542	6335	0.3548	-
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1.3563	6345	0.2383	-
1.3574	6350	0.3412	-
1.3585	6355	0.2392	-
1.3596	6360	0.2946	-
1.3606	6365	0.3075	-
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1.3649	6385	0.3048	-
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1.3681	6400	0.3156	-
1.3692	6405	0.2827	-
1.3702	6410	0.2337	-
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1.3756	6435	0.2549	-
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1.3788	6450	0.3722	-
1.3799	6455	0.453	-
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1.3820	6465	0.2605	-
1.3831	6470	0.3834	-
1.3841	6475	0.2683	-
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1.3873	6490	0.2754	-
1.3884	6495	0.3701	-
1.3895	6500	0.2762	-
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1.3927	6515	0.2754	-
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1.3948	6525	0.2974	-
1.3959	6530	0.3158	-
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1.3980	6540	0.2586	-
1.3991	6545	0.2071	-
1.4002	6550	0.3308	-
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1.4023	6560	0.234	-
1.4034	6565	0.3025	-
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1.4055	6575	0.2521	-
1.4066	6580	0.3113	-
1.4077	6585	0.3537	-
1.4087	6590	0.2386	-
1.4098	6595	0.3081	-
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1.4119	6605	0.2643	-
1.4130	6610	0.2206	-
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1.4162	6625	0.2711	-
1.4173	6630	0.3304	-
1.4183	6635	0.321	-
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1.4205	6645	0.2363	-
1.4215	6650	0.2319	-
1.4226	6655	0.3526	-
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1.4248	6665	0.2846	-
1.4258	6670	0.3077	-
1.4269	6675	0.3709	-
1.4280	6680	0.3438	-
1.4290	6685	0.4159	-
1.4301	6690	0.3511	-
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1.4322	6700	0.2694	-
1.4333	6705	0.3412	-
1.4344	6710	0.2631	-
1.4354	6715	0.3879	-
1.4365	6720	0.2942	-
1.4376	6725	0.3901	-
1.4386	6730	0.3192	-
1.4397	6735	0.2269	-
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1.4419	6745	0.2467	-
1.4429	6750	0.2313	-
1.4440	6755	0.3538	-
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1.4461	6765	0.3049	-
1.4472	6770	0.2796	-
1.4483	6775	0.2128	-
1.4493	6780	0.3407	-
1.4504	6785	0.2196	-
1.4515	6790	0.2472	-
1.4525	6795	0.2788	-
1.4536	6800	0.305	-
1.4547	6805	0.3072	-
1.4558	6810	0.2879	-
1.4568	6815	0.4406	-
1.4579	6820	0.3371	-
1.4590	6825	0.2775	-
1.4600	6830	0.2872	-
1.4611	6835	0.3056	-
1.4622	6840	0.3183	-
1.4632	6845	0.2801	-
1.4643	6850	0.2641	-
1.4654	6855	0.2903	-
1.4664	6860	0.4097	-
1.4675	6865	0.2133	-
1.4686	6870	0.3064	-
1.4696	6875	0.2629	-
1.4707	6880	0.1846	-
1.4718	6885	0.2552	-
1.4729	6890	0.2812	-
1.4739	6895	0.2796	-
1.4750	6900	0.3157	-
1.4761	6905	0.271	-
1.4771	6910	0.2352	-
1.4782	6915	0.3752	-
1.4793	6920	0.2811	-
1.4803	6925	0.3303	-
1.4814	6930	0.3382	-
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1.4857	6950	0.3061	-
1.4867	6955	0.3059	-
1.4878	6960	0.2692	-
1.4889	6965	0.2853	-
1.4900	6970	0.238	-
1.4910	6975	0.3086	-
1.4921	6980	0.3016	-
1.4932	6985	0.3272	-
1.4942	6990	0.3125	-
1.4953	6995	0.2777	-
1.4964	7000	0.3026	-
1.4974	7005	0.2522	-
1.4985	7010	0.2487	-
1.4996	7015	0.2316	-
1.5006	7020	0.2484	-
1.5017	7025	0.2852	-
1.5028	7030	0.2944	-
1.5038	7035	0.3133	-
1.5049	7040	0.2557	-
1.5060	7045	0.232	-
1.5071	7050	0.2635	-
1.5081	7055	0.3033	-
1.5092	7060	0.2477	-
1.5103	7065	0.2443	-
1.5113	7070	0.4329	-
1.5124	7075	0.2682	-
1.5135	7080	0.3149	-
1.5145	7085	0.353	-
1.5156	7090	0.2247	-
1.5167	7095	0.2862	-
1.5177	7100	0.287	-
1.5188	7105	0.3141	-
1.5199	7110	0.4446	-
1.5209	7115	0.2853	-
1.5220	7120	0.2662	-
1.5231	7125	0.2729	-
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1.5263	7140	0.302	-
1.5274	7145	0.2346	-
1.5284	7150	0.4036	-
1.5295	7155	0.2902	-
1.5306	7160	0.4254	-
1.5316	7165	0.2863	-
1.5327	7170	0.2618	-
1.5338	7175	0.3614	-
1.5348	7180	0.3084	-
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1.5370	7190	0.2843	-
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1.5402	7205	0.3137	-
1.5413	7210	0.3365	-
1.5423	7215	0.1981	-
1.5434	7220	0.2338	-
1.5445	7225	0.2692	-
1.5455	7230	0.2129	-
1.5466	7235	0.2395	-
1.5477	7240	0.485	-
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1.5498	7250	0.2799	-
1.5509	7255	0.2856	-
1.5519	7260	0.272	-
1.5530	7265	0.299	-
1.5541	7270	0.3309	-
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1.5562	7280	0.2351	-
1.5573	7285	0.2177	-
1.5584	7290	0.2606	-
1.5594	7295	0.3972	-
1.5605	7300	0.2594	-
1.5616	7305	0.2934	-
1.5626	7310	0.4105	-
1.5637	7315	0.1971	-
1.5648	7320	0.2107	-
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1.5669	7330	0.2413	-
1.5680	7335	0.2619	-
1.5690	7340	0.3098	-
1.5701	7345	0.3024	-
1.5712	7350	0.2548	-
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1.5733	7360	0.2245	-
1.5744	7365	0.2612	-
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1.5765	7375	0.2457	-
1.5776	7380	0.214	-
1.5787	7385	0.2761	-
1.5797	7390	0.278	-
1.5808	7395	0.4008	-
1.5819	7400	0.3222	-
1.5829	7405	0.2645	-
1.5840	7410	0.2121	-
1.5851	7415	0.2679	-
1.5861	7420	0.342	-
1.5872	7425	0.2312	-
1.5883	7430	0.3332	-
1.5894	7435	0.2628	-
1.5904	7440	0.2824	-
1.5915	7445	0.3249	-
1.5926	7450	0.3738	-
1.5936	7455	0.3357	-
1.5947	7460	0.2563	-
1.5958	7465	0.2091	-
1.5968	7470	0.2425	-
1.5979	7475	0.2699	-
1.5990	7480	0.3409	-
1.6000	7485	0.2233	-
1.6011	7490	0.3269	-
1.6022	7495	0.3029	-
1.6032	7500	0.2547	-
1.6043	7505	0.2855	-
1.6054	7510	0.3106	-
1.6065	7515	0.262	-
1.6075	7520	0.3016	-
1.6086	7525	0.2852	-
1.6097	7530	0.3126	-
1.6107	7535	0.2779	-
1.6118	7540	0.2811	-
1.6129	7545	0.259	-
1.6139	7550	0.2602	-
1.6150	7555	0.2481	-
1.6161	7560	0.2022	-
1.6171	7565	0.2943	-
1.6182	7570	0.2144	-
1.6193	7575	0.2589	-
1.6204	7580	0.1999	-
1.6214	7585	0.3035	-
1.6225	7590	0.2322	-
1.6236	7595	0.237	-
1.6246	7600	0.2815	-
1.6257	7605	0.4136	-
1.6268	7610	0.2474	-
1.6278	7615	0.3143	-
1.6289	7620	0.244	-
1.6300	7625	0.3134	-
1.6310	7630	0.2516	-
1.6321	7635	0.3159	-
1.6332	7640	0.2519	-
1.6342	7645	0.2785	-
1.6353	7650	0.2627	-
1.6364	7655	0.2677	-
1.6375	7660	0.1962	-
1.6385	7665	0.3779	-
1.6396	7670	0.2731	-
1.6407	7675	0.2806	-
1.6417	7680	0.2582	-
1.6428	7685	0.2064	-
1.6439	7690	0.263	-
1.6449	7695	0.3126	-
1.6460	7700	0.2889	-
1.6471	7705	0.2372	-
1.6481	7710	0.2062	-
1.6492	7715	0.3085	-
1.6503	7720	0.22	-
1.6513	7725	0.258	-
1.6524	7730	0.2496	-
1.6535	7735	0.2975	-
1.6546	7740	0.2744	-
1.6556	7745	0.2255	-
1.6567	7750	0.2387	-
1.6578	7755	0.2724	-
1.6588	7760	0.282	-
1.6599	7765	0.166	-
1.6610	7770	0.2653	-
1.6620	7775	0.2368	-
1.6631	7780	0.2235	-
1.6642	7785	0.309	-
1.6652	7790	0.3402	-
1.6663	7795	0.2741	-
1.6674	7800	0.2282	-
1.6684	7805	0.3233	-
1.6695	7810	0.23	-
1.6706	7815	0.22	-
1.6717	7820	0.216	-
1.6727	7825	0.3479	-
1.6738	7830	0.2256	-
1.6749	7835	0.3205	-
1.6759	7840	0.28	-
1.6770	7845	0.255	-
1.6781	7850	0.2584	-
1.6791	7855	0.254	-
1.6802	7860	0.2051	-
1.6813	7865	0.2653	-
1.6823	7870	0.2882	-
1.6834	7875	0.2685	-
1.6845	7880	0.2606	-
1.6855	7885	0.2266	-
1.6866	7890	0.2112	-
1.6877	7895	0.245	-
1.6888	7900	0.2144	-
1.6898	7905	0.3334	-
1.6909	7910	0.2754	-
1.6920	7915	0.3048	-
1.6930	7920	0.2319	-
1.6941	7925	0.3505	-
1.6952	7930	0.3171	-
1.6962	7935	0.2359	-
1.6973	7940	0.259	-
1.6984	7945	0.2345	-
1.6994	7950	0.2364	-
1.7005	7955	0.3125	-
1.7016	7960	0.3855	-
1.7027	7965	0.3085	-
1.7037	7970	0.2558	-
1.7048	7975	0.2863	-
1.7059	7980	0.2763	-
1.7069	7985	0.2307	-
1.7080	7990	0.2604	-
1.7091	7995	0.3524	-
1.7101	8000	0.2591	-
1.7112	8005	0.2945	-
1.7123	8010	0.2698	-
1.7133	8015	0.2091	-
1.7144	8020	0.2313	-
1.7155	8025	0.2935	-
1.7165	8030	0.2358	-
1.7176	8035	0.2842	-
1.7187	8040	0.26	-
1.7198	8045	0.312	-
1.7208	8050	0.2776	-
1.7219	8055	0.1612	-
1.7230	8060	0.1803	-
1.7240	8065	0.2677	-
1.7251	8070	0.2315	-
1.7262	8075	0.2583	-
1.7272	8080	0.2488	-
1.7283	8085	0.2379	-
1.7294	8090	0.2637	-
1.7304	8095	0.2101	-
1.7315	8100	0.283	-
1.7326	8105	0.2163	-
1.7336	8110	0.2474	-
1.7347	8115	0.3346	-
1.7358	8120	0.2031	-
1.7369	8125	0.2301	-
1.7379	8130	0.3413	-
1.7390	8135	0.3121	-
1.7401	8140	0.2696	-
1.7411	8145	0.1871	-
1.7422	8150	0.2427	-
1.7433	8155	0.215	-
1.7443	8160	0.2603	-
1.7454	8165	0.2149	-
1.7465	8170	0.2961	-
1.7475	8175	0.2363	-
1.7486	8180	0.2238	-
1.7497	8185	0.2522	-
1.7507	8190	0.2314	-
1.7518	8195	0.34	-
1.7529	8200	0.3113	-
1.7540	8205	0.2022	-
1.7550	8210	0.2244	-
1.7561	8215	0.1794	-
1.7572	8220	0.3058	-
1.7582	8225	0.2495	-
1.7593	8230	0.3586	-
1.7604	8235	0.2726	-
1.7614	8240	0.2774	-
1.7625	8245	0.3009	-
1.7636	8250	0.2813	-
1.7646	8255	0.2942	-
1.7657	8260	0.2463	-
1.7668	8265	0.2399	-
1.7678	8270	0.2725	-
1.7689	8275	0.2171	-
1.7700	8280	0.3037	-
1.7711	8285	0.1921	-
1.7721	8290	0.2519	-
1.7732	8295	0.2644	-
1.7743	8300	0.3707	-
1.7753	8305	0.1496	-
1.7764	8310	0.2251	-
1.7775	8315	0.2426	-
1.7785	8320	0.2853	-
1.7796	8325	0.2156	-
1.7807	8330	0.2293	-
1.7817	8335	0.2516	-
1.7828	8340	0.2272	-
1.7839	8345	0.2995	-
1.7850	8350	0.2825	-
1.7860	8355	0.2604	-
1.7871	8360	0.2216	-
1.7882	8365	0.3034	-
1.7892	8370	0.229	-
1.7903	8375	0.271	-
1.7914	8380	0.2371	-
1.7924	8385	0.2592	-
1.7935	8390	0.206	-
1.7946	8395	0.2167	-
1.7956	8400	0.2521	-
1.7967	8405	0.2283	-
1.7978	8410	0.2161	-
1.7988	8415	0.2233	-
1.7999	8420	0.2799	-
1.8010	8425	0.2558	-
1.8021	8430	0.3093	-
1.8031	8435	0.2751	-
1.8042	8440	0.268	-
1.8053	8445	0.2473	-
1.8063	8450	0.3218	-
1.8074	8455	0.2602	-
1.8085	8460	0.2118	-
1.8095	8465	0.2526	-
1.8106	8470	0.2797	-
1.8117	8475	0.2177	-
1.8127	8480	0.2264	-
1.8138	8485	0.253	-
1.8149	8490	0.199	-
1.8159	8495	0.2484	-
1.8170	8500	0.2666	-
1.8181	8505	0.2423	-
1.8192	8510	0.3206	-
1.8202	8515	0.1885	-
1.8213	8520	0.3103	-
1.8224	8525	0.2204	-
1.8234	8530	0.2269	-
1.8245	8535	0.3602	-
1.8256	8540	0.1735	-
1.8266	8545	0.3107	-
1.8277	8550	0.2346	-
1.8288	8555	0.2372	-
1.8298	8560	0.2665	-
1.8309	8565	0.2722	-
1.8320	8570	0.1879	-
1.8330	8575	0.2434	-
1.8341	8580	0.3408	-
1.8352	8585	0.2819	-
1.8363	8590	0.2828	-
1.8373	8595	0.1821	-
1.8384	8600	0.2193	-
1.8395	8605	0.3155	-
1.8405	8610	0.2129	-
1.8416	8615	0.2321	-
1.8427	8620	0.1748	-
1.8437	8625	0.2355	-
1.8448	8630	0.2226	-
1.8459	8635	0.2812	-
1.8469	8640	0.2542	-
1.8480	8645	0.214	-
1.8491	8650	0.2157	-
1.8501	8655	0.2336	-
1.8512	8660	0.3409	-
1.8523	8665	0.2902	-
1.8534	8670	0.2329	-
1.8544	8675	0.1849	-
1.8555	8680	0.2919	-
1.8566	8685	0.2157	-
1.8576	8690	0.2286	-
1.8587	8695	0.1957	-
1.8598	8700	0.2102	-
1.8608	8705	0.3172	-
1.8619	8710	0.2619	-
1.8630	8715	0.2898	-
1.8640	8720	0.2523	-
1.8651	8725	0.2534	-
1.8662	8730	0.2546	-
1.8673	8735	0.2171	-
1.8683	8740	0.2617	-
1.8694	8745	0.1917	-
1.8705	8750	0.2256	-
1.8715	8755	0.2319	-
1.8726	8760	0.2783	-
1.8737	8765	0.2885	-
1.8747	8770	0.2586	-
1.8758	8775	0.2009	-
1.8769	8780	0.273	-
1.8779	8785	0.2502	-
1.8790	8790	0.2174	-
1.8801	8795	0.2723	-
1.8811	8800	0.2633	-
1.8822	8805	0.2794	-
1.8833	8810	0.2961	-
1.8844	8815	0.2598	-
1.8854	8820	0.2794	-
1.8865	8825	0.2447	-
1.8876	8830	0.2741	-
1.8886	8835	0.2192	-
1.8897	8840	0.3188	-
1.8908	8845	0.294	-
1.8918	8850	0.2961	-
1.8929	8855	0.2288	-
1.8940	8860	0.2796	-
1.8950	8865	0.2464	-
1.8961	8870	0.2435	-
1.8972	8875	0.2993	-
1.8982	8880	0.2049	-
1.8993	8885	0.2588	-
1.9004	8890	0.2559	-
1.9015	8895	0.261	-
1.9025	8900	0.2132	-
1.9036	8905	0.2184	-
1.9047	8910	0.2551	-
1.9057	8915	0.2302	-
1.9068	8920	0.2441	-
1.9079	8925	0.2176	-
1.9089	8930	0.3041	-
1.9100	8935	0.2325	-
1.9111	8940	0.2463	-
1.9121	8945	0.2997	-
1.9132	8950	0.2202	-
1.9143	8955	0.2745	-
1.9153	8960	0.2642	-
1.9164	8965	0.2782	-
1.9175	8970	0.2435	-
1.9186	8975	0.2492	-
1.9196	8980	0.2161	-
1.9207	8985	0.2549	-
1.9218	8990	0.2217	-
1.9228	8995	0.266	-
1.9239	9000	0.2909	-
1.9250	9005	0.2149	-
1.9260	9010	0.3099	-
1.9271	9015	0.1929	-
1.9282	9020	0.2854	-
1.9292	9025	0.1857	-
1.9303	9030	0.2445	-
1.9314	9035	0.226	-
1.9324	9040	0.2822	-
1.9335	9045	0.2541	-
1.9346	9050	0.2442	-
1.9357	9055	0.2504	-
1.9367	9060	0.2073	-
1.9378	9065	0.2245	-
1.9389	9070	0.2442	-
1.9399	9075	0.2382	-
1.9410	9080	0.2482	-
1.9421	9085	0.2849	-
1.9431	9090	0.2666	-
1.9442	9095	0.224	-
1.9453	9100	0.2961	-
1.9463	9105	0.217	-
1.9474	9110	0.2384	-
1.9485	9115	0.2205	-
1.9496	9120	0.2807	-
1.9506	9125	0.2577	-
1.9517	9130	0.3212	-
1.9528	9135	0.239	-
1.9538	9140	0.2283	-
1.9549	9145	0.2606	-
1.9560	9150	0.2684	-
1.9570	9155	0.2857	-
1.9581	9160	0.2962	-
1.9592	9165	0.2639	-
1.9602	9170	0.2549	-
1.9613	9175	0.2561	-
1.9624	9180	0.2324	-
1.9634	9185	0.3142	-
1.9645	9190	0.2778	-
1.9656	9195	0.2603	-
1.9667	9200	0.2191	-
1.9677	9205	0.2658	-
1.9688	9210	0.2701	-
1.9699	9215	0.3	-
1.9709	9220	0.2728	-
1.9720	9225	0.2724	-
1.9731	9230	0.1735	-
1.9741	9235	0.25	-
1.9752	9240	0.276	-
1.9763	9245	0.2581	-
1.9773	9250	0.2545	-
1.9784	9255	0.2251	-
1.9795	9260	0.269	-
1.9805	9265	0.2026	-
1.9816	9270	0.2297	-
1.9827	9275	0.2414	-
1.9838	9280	0.2724	-
1.9848	9285	0.2338	-
1.9859	9290	0.2606	-
1.9870	9295	0.2747	-
1.9880	9300	0.1792	-
1.9891	9305	0.2479	-
1.9902	9310	0.2554	-
1.9912	9315	0.25	-
1.9923	9320	0.2814	-
1.9934	9325	0.2102	-
1.9944	9330	0.2466	-
1.9955	9335	0.2547	-
1.9966	9340	0.2216	-
1.9976	9345	0.2521	-
1.9987	9350	0.2558	-
1.9998	9355	0.2345	-
2.0	9356	-	0.1219

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.1.1
PyLate: 1.3.4
Transformers: 4.56.2
PyTorch: 2.8.0+cu128
Accelerate: 1.12.0
Datasets: 4.4.1
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"
}

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}
}

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Collection including sidataplus/THIRAWAT-SapBERT

THIRAWAT

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Terminology Harmonization using Late-Interaction Reranker With Alignment-tuned Transformers • 3 items • Updated 12 days ago

Paper for sidataplus/THIRAWAT-SapBERT

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 10