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,-1,0.7551020408163265,0.8979591836734694,0.9387755102040817,1.0,0.7551020408163265,0.01681883497544576,0.6802721088435373,0.045273391320759554,0.6489795918367346,0.07198855467813525,0.563265306122449,0.12417651370073833,0.8328474246841594,0.6023501966395867,0.2434385293084787
--1,-1,0.7346938775510204,0.9387755102040817,0.9591836734693877,0.9795918367346939,0.7346938775510204,0.01628348794626864,0.6802721088435373,0.045158349206937544,0.6612244897959185,0.07322241057348641,0.5857142857142857,0.12952247914108056,0.8306122448979592,0.6211658380172899,0.24407616368208723
--1,-1,0.6530612244897959,0.9591836734693877,0.9591836734693877,0.9795918367346939,0.6530612244897959,0.01432104834093062,0.727891156462585,0.048281100280382724,0.6448979591836735,0.07113270115683268,0.5795918367346938,0.1277462197359846,0.7950680272108844,0.6075766364842123,0.25739036878474303

Information-Retrieval_evaluation_NanoArguAna_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,-1,0.18,0.5,0.66,0.74,0.18,0.18,0.16666666666666663,0.5,0.13200000000000003,0.66,0.07400000000000001,0.74,0.3599682539682539,0.45218312003145433,0.3658170202780539
--1,-1,0.14,0.38,0.5,0.66,0.14,0.14,0.12666666666666665,0.38,0.1,0.5,0.06600000000000002,0.66,0.300095238095238,0.38615266678375515,0.3088425567963239
--1,-1,0.22,0.54,0.58,0.74,0.22,0.22,0.18,0.54,0.11600000000000002,0.58,0.07400000000000001,0.74,0.3815793650793651,0.4674888162177975,0.3917367299367299

Information-Retrieval_evaluation_NanoClimateFEVER_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,-1,0.1,0.32,0.44,0.6,0.1,0.04333333333333333,0.11333333333333333,0.154,0.092,0.214,0.066,0.2723333333333333,0.23579365079365078,0.18832347198247595,0.13278630044723194
--1,-1,0.1,0.26,0.34,0.6,0.1,0.04666666666666666,0.08666666666666666,0.12399999999999999,0.07200000000000001,0.16899999999999998,0.066,0.2973333333333333,0.21938095238095237,0.1853895398720514,0.1267197025068282
--1,-1,0.22,0.36,0.44,0.68,0.22,0.10166666666666666,0.12,0.1433333333333333,0.09200000000000001,0.19666666666666666,0.08,0.32233333333333336,0.3309126984126983,0.24154521021050848,0.17419838412151278

Information-Retrieval_evaluation_NanoDBPedia_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,-1,0.58,0.82,0.86,0.88,0.58,0.05774032197349101,0.5266666666666666,0.13525154291594316,0.444,0.17429307751101658,0.36199999999999993,0.24557535689700863,0.7,0.46037876007701023,0.32047705412103555
--1,-1,0.68,0.8,0.86,0.88,0.68,0.08435515343632806,0.5066666666666666,0.13854094008435847,0.44800000000000006,0.17555319487559032,0.376,0.25077742902228617,0.7498333333333334,0.4897177201468777,0.34021634749539026
--1,-1,0.62,0.78,0.84,0.92,0.62,0.07067219113244924,0.5199999999999999,0.1473605766367288,0.452,0.18535916558236945,0.364,0.27484027728017424,0.7183571428571429,0.4817256707832707,0.3555155246867996

Information-Retrieval_evaluation_NanoFEVER_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
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--1,-1,0.6,0.76,0.86,0.88,0.6,0.5466666666666666,0.2533333333333333,0.7066666666666666,0.17199999999999996,0.7966666666666665,0.088,0.8166666666666665,0.6975555555555556,0.6976146491512496,0.6474697943858455
--1,-1,0.76,0.84,0.9,0.9,0.76,0.7066666666666666,0.29333333333333333,0.7933333333333333,0.18799999999999997,0.8533333333333333,0.09399999999999999,0.8533333333333333,0.805,0.7928392587586685,0.7629176534259828

Information-Retrieval_evaluation_NanoFiQA2018_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,-1,0.24,0.38,0.42,0.52,0.24,0.13307936507936508,0.15333333333333332,0.21543650793650795,0.11600000000000002,0.2601031746031746,0.07600000000000001,0.3241587301587302,0.3178809523809524,0.2635447070179544,0.22615568818713977
--1,-1,0.26,0.42,0.46,0.58,0.26,0.11974603174603175,0.18,0.25293650793650796,0.124,0.28343650793650793,0.08399999999999999,0.37834920634920627,0.34807936507936504,0.289405411189281,0.2371668409805281
--1,-1,0.3,0.4,0.42,0.52,0.3,0.14474603174603173,0.1733333333333333,0.2201031746031746,0.124,0.2519365079365079,0.08,0.31682539682539684,0.35405555555555557,0.2761214091575939,0.23650883511668197

Information-Retrieval_evaluation_NanoHotpotQA_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,-1,0.5,0.58,0.62,0.68,0.5,0.25,0.2333333333333333,0.35,0.16399999999999998,0.41,0.094,0.47,0.5529999999999999,0.43627646197603637,0.37828612061221206
--1,-1,0.54,0.64,0.7,0.74,0.54,0.27,0.26666666666666666,0.4,0.184,0.46,0.106,0.53,0.606,0.48356059599535955,0.4167638398079239
--1,-1,0.7,0.76,0.78,0.8,0.7,0.35,0.3733333333333333,0.56,0.244,0.61,0.13399999999999998,0.67,0.7373333333333334,0.6362341710243232,0.579012255659608

Information-Retrieval_evaluation_NanoMSMARCO_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,-1,0.26,0.52,0.6,0.62,0.26,0.26,0.1733333333333333,0.52,0.12,0.6,0.062,0.62,0.40519047619047627,0.45904886208148177,0.4260102142025637
--1,-1,0.28,0.54,0.62,0.8,0.28,0.28,0.17999999999999997,0.54,0.124,0.62,0.08,0.8,0.43837301587301575,0.5241911345526384,0.4480618800320956
--1,-1,0.28,0.58,0.64,0.72,0.28,0.28,0.19333333333333333,0.58,0.128,0.64,0.07200000000000001,0.72,0.4386111111111111,0.5075011853031293,0.4533366047009664

Information-Retrieval_evaluation_NanoNFCorpus_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,-1,0.32,0.5,0.52,0.64,0.32,0.01260685895239504,0.31333333333333335,0.03463231741620742,0.27599999999999997,0.06850002262294216,0.24399999999999994,0.10851262864104039,0.4212380952380952,0.2703690747449406,0.10029196368651581
--1,-1,0.34,0.52,0.58,0.68,0.34,0.012133063569139098,0.33333333333333326,0.05465564949455657,0.29600000000000004,0.07192795043792813,0.25,0.10822085751351866,0.44152380952380954,0.2847553576589848,0.108499059841261
--1,-1,0.38,0.46,0.54,0.64,0.38,0.012479157217241355,0.3,0.04881894595681059,0.3,0.06922116223257517,0.276,0.10938910626227699,0.45196825396825396,0.30283246736353403,0.12231981928859673

Information-Retrieval_evaluation_NanoNQ_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,-1,0.32,0.5,0.6,0.62,0.32,0.3,0.1733333333333333,0.47,0.128,0.58,0.066,0.6,0.4272222222222222,0.4619884812398348,0.42411983365963474
--1,-1,0.36,0.56,0.6,0.62,0.36,0.35,0.19333333333333333,0.53,0.128,0.58,0.066,0.6,0.4625,0.490897686812855,0.46206363135240186
--1,-1,0.32,0.54,0.6,0.66,0.32,0.3,0.18666666666666665,0.51,0.128,0.58,0.07,0.64,0.4465,0.48687028758380874,0.4417143853257704

Information-Retrieval_evaluation_NanoQuoraRetrieval_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,-1,0.94,1.0,1.0,1.0,0.94,0.8273333333333334,0.4133333333333333,0.9653333333333333,0.25999999999999995,0.9793333333333334,0.13799999999999998,0.9966666666666666,0.9666666666666667,0.9697624312418531,0.9551233766233765
--1,-1,0.92,1.0,1.0,1.0,0.92,0.8073333333333332,0.4133333333333333,0.9653333333333333,0.264,0.986,0.13399999999999998,0.99,0.96,0.960129267031932,0.945489898989899
--1,-1,0.88,1.0,1.0,1.0,0.88,0.7773333333333332,0.41999999999999993,0.972,0.26799999999999996,0.9893333333333334,0.13599999999999998,0.9926666666666667,0.9366666666666665,0.9483612484877714,0.9296388888888888

Information-Retrieval_evaluation_NanoSCIDOCS_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,-1,0.42,0.6,0.72,0.82,0.42,0.08866666666666667,0.33333333333333326,0.20866666666666664,0.272,0.2806666666666667,0.17999999999999997,0.3696666666666666,0.540047619047619,0.36082794471047336,0.2862806075456748
--1,-1,0.48,0.72,0.76,0.8,0.48,0.10166666666666666,0.3666666666666666,0.22766666666666666,0.28400000000000003,0.29266666666666663,0.19199999999999995,0.3946666666666666,0.5951904761904762,0.3895503827770311,0.3082897117657901
--1,-1,0.46,0.68,0.84,0.9,0.46,0.09666666666666666,0.3533333333333333,0.21766666666666665,0.304,0.31266666666666665,0.18999999999999997,0.3896666666666666,0.6114444444444443,0.3893008993021786,0.3091964898288773

Information-Retrieval_evaluation_NanoSciFact_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,-1,0.38,0.46,0.5,0.58,0.38,0.345,0.15999999999999998,0.43,0.10800000000000003,0.475,0.068,0.58,0.44026984126984126,0.46384622999765257,0.43257979600699104
--1,-1,0.38,0.54,0.58,0.7,0.38,0.345,0.19999999999999996,0.525,0.12800000000000003,0.565,0.08,0.7,0.4820793650793651,0.5292195947118973,0.47730440170572996
--1,-1,0.58,0.68,0.7,0.72,0.58,0.545,0.2533333333333333,0.665,0.156,0.685,0.08199999999999999,0.71,0.6375,0.6523742480687815,0.6370166989443306

Information-Retrieval_evaluation_NanoTouche2020_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,-1,0.46938775510204084,0.8367346938775511,0.9387755102040817,1.0,0.46938775510204084,0.032657982947973084,0.44897959183673464,0.09621881460341672,0.42040816326530606,0.1425551052100505,0.3346938775510204,0.22061476067159091,0.6573129251700679,0.3807140713282222,0.2698119698398041
--1,-1,0.5102040816326531,0.8163265306122449,0.8571428571428571,0.9591836734693877,0.5102040816326531,0.04030730530317779,0.45578231292517,0.10027039527564566,0.4040816326530612,0.14754618693234572,0.3428571428571428,0.2268233238254859,0.6711613216715256,0.3942611497955867,0.28013001290517386
--1,-1,0.4489795918367347,0.7755102040816326,0.8979591836734694,0.9795918367346939,0.4489795918367347,0.03398816288797225,0.41496598639455773,0.09117793391499442,0.4326530612244897,0.15321422858378142,0.3591836734693878,0.24000922572748823,0.6308309037900873,0.3959230964031327,0.3023791135389433

NanoBEIR_evaluation_mean_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,-1,0.4053375196232339,0.601287284144427,0.6737519623233909,0.7369230769230769,0.4053375196232339,0.23516034838101724,0.26838304552590264,0.37175942432349296,0.20895447409733128,0.43675267025234743,0.1428226059654631,0.4908354981821823,0.5156608233036803,0.45145275407225244,0.3820046351353431
--1,-1,0.4300156985871272,0.6120251177394034,0.6705494505494506,0.7614756671899527,0.4300156985871272,0.24183652979907766,0.2740345368916797,0.3803900122659796,0.20985243328100472,0.4344459364242849,0.14852747252747253,0.5194490371828588,0.536290187137126,0.46960347357534615,0.39284751373578397
--1,-1,0.47453689167974883,0.6458084772370486,0.7059968602825746,0.7830455259026687,0.47453689167974883,0.2799399135628483,0.290894819466248,0.42221492034192626,0.22558869701726847,0.4697485434104026,0.15470643642072213,0.536851077391949,0.5754430365552815,0.5060859975895767,0.4381147218048991

eval/Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -20,3 +20,16 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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+0.5747126436781609,500,0.36,0.48,0.52,0.72,0.36,0.36,0.15999999999999998,0.48,0.10400000000000001,0.52,0.07200000000000001,0.72,0.44824603174603167,0.5105706870011885,0.4614621165572778
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eval/NanoBEIR_evaluation_mean_results.csv

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final_metrics.json

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