Training in progress, step 3000

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -1,4 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.7142857142857143,0.8979591836734694,0.9387755102040817,1.0,0.7142857142857143,0.015925713751801022,0.6870748299319728,0.045760276621368244,0.6326530612244898,0.06999790748190285,0.553061224489796,0.12237196517849,0.8126660187884677,0.590781089071972,0.25216721309824497
--1,-1,0.7346938775510204,0.8775510204081632,0.9591836734693877,0.9795918367346939,0.7346938775510204,0.01624309573792563,0.6734693877551019,0.04452074460941084,0.6367346938775511,0.07034165898860735,0.5510204081632654,0.12170213074025006,0.8187074829931973,0.5930193473989395,0.2518399254976325
--1,-1,0.673469387755102,0.8571428571428571,0.9387755102040817,0.9591836734693877,0.673469387755102,0.014854954185034354,0.6258503401360543,0.041323822718693934,0.6122448979591837,0.06744310800628797,0.5816326530612245,0.12790902925353234,0.7784256559766765,0.6000606062963675,0.2654061071722756

Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.2,0.56,0.74,0.76,0.2,0.2,0.18666666666666668,0.56,0.14800000000000002,0.74,0.07600000000000001,0.76,0.40222222222222215,0.49058314613975507,0.4109932426184554
--1,-1,0.2,0.56,0.74,0.76,0.2,0.2,0.18666666666666668,0.56,0.14800000000000002,0.74,0.07600000000000001,0.76,0.40222222222222215,0.49058314613975507,0.4109932426184554
--1,-1,0.22,0.5,0.66,0.8,0.22,0.22,0.16666666666666663,0.5,0.132,0.66,0.08,0.8,0.3926269841269841,0.48983349748002636,0.40347638549721887
--1,-1,0.22,0.56,0.72,0.84,0.22,0.22,0.18666666666666668,0.56,0.14400000000000002,0.72,0.08399999999999999,0.84,0.417611111111111,0.5195582784941274,0.4242292757682783

Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.14,0.32,0.48,0.62,0.14,0.05833333333333333,0.12,0.155,0.10400000000000002,0.22066666666666668,0.07400000000000001,0.30733333333333335,0.27213492063492056,0.215125793679731,0.15431110143807805
--1,-1,0.14,0.32,0.48,0.62,0.14,0.05833333333333333,0.12,0.155,0.10400000000000002,0.22066666666666668,0.07400000000000001,0.30733333333333335,0.27213492063492056,0.215125793679731,0.15431110143807805
--1,-1,0.12,0.28,0.46,0.66,0.12,0.03833333333333333,0.09999999999999998,0.135,0.1,0.23633333333333334,0.07600000000000001,0.32466666666666666,0.2561746031746031,0.20845131843009276,0.14245832634750027
--1,-1,0.28,0.5,0.52,0.72,0.28,0.12,0.16666666666666669,0.22166666666666665,0.10800000000000001,0.2483333333333333,0.08399999999999999,0.36733333333333335,0.39877777777777773,0.28399247526326993,0.21216679851781925

Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.62,0.84,0.88,0.88,0.62,0.05039842070870112,0.4933333333333333,0.13002690694209756,0.44,0.18830365543570443,0.37199999999999994,0.2679047211992138,0.7323333333333334,0.46809379506385207,0.33243413363446367
--1,-1,0.62,0.84,0.88,0.88,0.62,0.05039842070870112,0.4933333333333333,0.13002690694209756,0.44,0.18830365543570443,0.37199999999999994,0.2679047211992138,0.7323333333333334,0.46809379506385207,0.33243413363446367
--1,-1,0.66,0.76,0.86,0.88,0.66,0.08255861663979128,0.4733333333333334,0.1250415886989628,0.44800000000000006,0.17452224602058436,0.34800000000000003,0.25437495331636306,0.7288333333333334,0.4636652485066659,0.32939375725902226
--1,-1,0.66,0.78,0.8,0.92,0.66,0.07324232876100338,0.5,0.1458081980942187,0.4160000000000001,0.18062353317681695,0.354,0.2682740597758614,0.7401904761904763,0.4702867365632332,0.35022041415987587

Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.62,0.88,0.94,0.96,0.62,0.5966666666666667,0.30666666666666664,0.8433333333333333,0.19599999999999995,0.8933333333333333,0.09999999999999998,0.9133333333333333,0.753,0.7821095700854137,0.7330432132878941
--1,-1,0.62,0.88,0.94,0.96,0.62,0.5966666666666667,0.30666666666666664,0.8433333333333333,0.19599999999999995,0.8933333333333333,0.09999999999999998,0.9133333333333333,0.753,0.7821095700854137,0.7330432132878941
--1,-1,0.7,0.86,0.9,0.98,0.7,0.6466666666666666,0.2866666666666667,0.8066666666666665,0.184,0.8566666666666666,0.09999999999999998,0.9266666666666667,0.784190476190476,0.7923317127841635,0.7390417679680837
--1,-1,0.76,0.92,1.0,1.0,0.76,0.7066666666666666,0.32,0.8733333333333333,0.20799999999999996,0.9533333333333333,0.10399999999999998,0.9533333333333333,0.8390000000000001,0.8454367717882709,0.7968940242763771

Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.22,0.38,0.54,0.6,0.22,0.11752380952380952,0.15999999999999998,0.21912698412698414,0.14400000000000002,0.34296031746031747,0.088,0.3807380952380952,0.33804761904761904,0.2959832185054632,0.24139316426365195
--1,-1,0.22,0.38,0.54,0.6,0.22,0.11752380952380952,0.15999999999999998,0.21912698412698414,0.14400000000000002,0.34296031746031747,0.088,0.3807380952380952,0.33804761904761904,0.2959832185054632,0.24139316426365195
--1,-1,0.28,0.38,0.52,0.6,0.28,0.12974603174603175,0.16666666666666663,0.20312698412698413,0.14400000000000002,0.2956269841269841,0.08999999999999998,0.3792936507936508,0.3612698412698412,0.29381486022787706,0.24011197414286956
--1,-1,0.28,0.38,0.44,0.56,0.28,0.12474603174603174,0.16,0.19496031746031747,0.14,0.28234920634920635,0.088,0.3491825396825397,0.34983333333333333,0.28171892775593277,0.23002318958083748

Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.6,0.74,0.78,0.86,0.6,0.3,0.32666666666666666,0.49,0.21599999999999994,0.54,0.12599999999999997,0.63,0.6806666666666666,0.5588160498147219,0.47611256957303766
--1,-1,0.6,0.74,0.78,0.86,0.6,0.3,0.32666666666666666,0.49,0.21599999999999994,0.54,0.12599999999999997,0.63,0.6806666666666666,0.5588160498147219,0.47611256957303766
--1,-1,0.64,0.74,0.78,0.84,0.64,0.32,0.3,0.45,0.19599999999999998,0.49,0.11199999999999999,0.56,0.704079365079365,0.5341052902954041,0.46245563144445145
--1,-1,0.74,0.84,0.9,0.9,0.74,0.37,0.40666666666666657,0.61,0.27599999999999997,0.69,0.154,0.77,0.8006666666666667,0.7043467589883098,0.6277244926048727

Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.22,0.5,0.62,0.74,0.22,0.22,0.16666666666666663,0.5,0.124,0.62,0.07400000000000001,0.74,0.39240476190476187,0.47667177266958005,0.406991563991564
--1,-1,0.22,0.5,0.62,0.74,0.22,0.22,0.16666666666666663,0.5,0.124,0.62,0.07400000000000001,0.74,0.39240476190476187,0.47667177266958005,0.406991563991564
--1,-1,0.3,0.56,0.66,0.8,0.3,0.3,0.18666666666666665,0.56,0.132,0.66,0.08,0.8,0.4522142857142857,0.5355647548788993,0.4614012040294735
--1,-1,0.26,0.56,0.7,0.78,0.26,0.26,0.18666666666666668,0.56,0.14,0.7,0.078,0.78,0.4317142857142857,0.5166057772867828,0.4425420850161611

Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.34,0.5,0.58,0.6,0.34,0.01200107748257525,0.3133333333333333,0.042785241025884206,0.292,0.08113445148474485,0.25,0.10803989338634405,0.4345555555555555,0.2802878906182637,0.10597358556555182
--1,-1,0.34,0.5,0.58,0.6,0.34,0.01200107748257525,0.3133333333333333,0.042785241025884206,0.292,0.08113445148474485,0.25,0.10803989338634405,0.4345555555555555,0.2802878906182637,0.10597358556555182
--1,-1,0.36,0.54,0.58,0.66,0.36,0.013678895813410474,0.34,0.05640197371329614,0.308,0.07379464684205841,0.242,0.09966914120321839,0.45522222222222225,0.2773671235823583,0.10548993594921903
--1,-1,0.38,0.46,0.52,0.66,0.38,0.03212466721866998,0.3,0.04815148484556365,0.288,0.0674537100479343,0.262,0.12106237128917721,0.43976984126984114,0.2990794029698064,0.13069447368464218

Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.28,0.46,0.56,0.64,0.28,0.27,0.15999999999999998,0.45,0.11600000000000002,0.54,0.066,0.61,0.39785714285714285,0.4442430372694745,0.39869586832265574
--1,-1,0.28,0.46,0.56,0.64,0.28,0.27,0.15999999999999998,0.45,0.11600000000000002,0.54,0.066,0.61,0.39785714285714285,0.4442430372694745,0.39869586832265574
--1,-1,0.3,0.54,0.62,0.7,0.3,0.29,0.18,0.51,0.132,0.6,0.07400000000000001,0.68,0.43224603174603166,0.48789548101925573,0.4304090341200182
--1,-1,0.34,0.56,0.62,0.68,0.34,0.31,0.19333333333333333,0.53,0.128,0.59,0.07200000000000001,0.66,0.46341269841269844,0.4981843146804683,0.4486372793651192

Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.96,1.0,1.0,1.0,0.96,0.8373333333333334,0.4133333333333333,0.9653333333333333,0.264,0.986,0.13999999999999999,1.0,0.9733333333333334,0.9736013358388067,0.958547619047619
--1,-1,0.96,1.0,1.0,1.0,0.96,0.8373333333333334,0.4133333333333333,0.9653333333333333,0.264,0.986,0.13999999999999999,1.0,0.9733333333333334,0.9736013358388067,0.958547619047619
--1,-1,0.96,0.98,1.0,1.0,0.96,0.8473333333333334,0.4133333333333333,0.9520000000000001,0.264,0.986,0.13999999999999999,1.0,0.975,0.9790267083021519,0.9683809523809523
--1,-1,0.94,0.98,1.0,1.0,0.94,0.8273333333333334,0.4133333333333333,0.9520000000000001,0.264,0.986,0.13999999999999999,1.0,0.9640000000000001,0.9693718101249,0.9546825396825398

Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.46,0.64,0.78,0.82,0.46,0.09766666666666668,0.3533333333333333,0.21966666666666665,0.3,0.30966666666666665,0.18799999999999997,0.38666666666666655,0.5706666666666667,0.3818424009361081,0.30532272577213904
--1,-1,0.46,0.64,0.78,0.82,0.46,0.09766666666666668,0.3533333333333333,0.21966666666666665,0.3,0.30966666666666665,0.18799999999999997,0.38666666666666655,0.5706666666666667,0.3818424009361081,0.30532272577213904
--1,-1,0.46,0.66,0.76,0.82,0.46,0.09766666666666668,0.34,0.21166666666666667,0.27599999999999997,0.2846666666666666,0.19399999999999998,0.3986666666666666,0.5684444444444444,0.38054185058113466,0.29482712989551213
--1,-1,0.44,0.72,0.8,0.9,0.44,0.09266666666666666,0.36666666666666664,0.22666666666666666,0.284,0.29166666666666663,0.18799999999999997,0.3856666666666666,0.5949444444444444,0.3848988062074523,0.3093365232956528

Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.48,0.6,0.64,0.8,0.48,0.435,0.22666666666666668,0.585,0.148,0.63,0.09,0.79,0.5592777777777777,0.6050538780432089,0.5513100730514523
--1,-1,0.48,0.6,0.64,0.8,0.48,0.435,0.22666666666666668,0.585,0.148,0.63,0.09,0.79,0.5592777777777777,0.6050538780432089,0.5513100730514523
--1,-1,0.42,0.64,0.72,0.78,0.42,0.375,0.2333333333333333,0.615,0.16,0.7,0.088,0.77,0.5500238095238096,0.5949205162858369,0.5379955882174889
--1,-1,0.66,0.78,0.78,0.8,0.66,0.615,0.28,0.76,0.17199999999999996,0.765,0.08999999999999998,0.79,0.7228571428571428,0.733200404338037,0.7167485636699191

Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
 -1,-1,0.4489795918367347,0.7142857142857143,0.8367346938775511,0.9795918367346939,0.4489795918367347,0.03145284890764548,0.3877551020408163,0.08052290820807267,0.37959183673469393,0.12752705262749714,0.3285714285714286,0.21259838452857663,0.6169663103336572,0.36562572315623365,0.2636080363851069
--1,-1,0.4489795918367347,0.7142857142857143,0.8367346938775511,0.9795918367346939,0.4489795918367347,0.03145284890764548,0.3877551020408163,0.08052290820807267,0.37959183673469393,0.12752705262749714,0.3285714285714286,0.21259838452857663,0.6169663103336572,0.36562572315623365,0.2636080363851069
--1,-1,0.4897959183673469,0.8367346938775511,0.8775510204081632,0.9591836734693877,0.4897959183673469,0.0391261657646358,0.46258503401360546,0.09935212519786965,0.4081632653061225,0.14360455232476052,0.35306122448979593,0.23229499710875565,0.6578798185941044,0.39999686542243473,0.28337269656997294
--1,-1,0.42857142857142855,0.6938775510204082,0.8571428571428571,0.9591836734693877,0.42857142857142855,0.03138807979253311,0.42176870748299317,0.09060401943508274,0.4244897959183673,0.1535986032297028,0.35918367346938773,0.24399992715703397,0.6046161321671525,0.39516130369687524,0.2987728168236368

NanoBEIR_evaluation_mean_results.csv

@@ -1,5 +1,2 @@
 epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100
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--1,-1,0.42992150706436427,0.6257142857142857,0.7212872841444271,0.7891993720565149,0.42992150706436427,0.24818278127867163,0.27803244374672936,0.4031381056643362,0.22089167974882265,0.47843016489807155,0.15173626373626373,0.5466626482835049,0.5479589469487428,0.4875413547554317,0.4106720689962823
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eval/Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -20,3 +20,21 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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eval/Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -20,3 +20,21 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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eval/NanoBEIR_evaluation_mean_results.csv

@@ -20,3 +20,21 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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final_metrics.json

@@ -1,231 +1,231 @@
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