Training in progress, step 5000

1_Pooling/config.json

@@ -1,7 +1,7 @@
 {
-    "word_embedding_dimension": 384,
-    "pooling_mode_cls_token": false,
-    "pooling_mode_mean_tokens": true,
+    "word_embedding_dimension": 512,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
     "pooling_mode_max_tokens": false,
     "pooling_mode_mean_sqrt_len_tokens": false,
     "pooling_mode_weightedmean_tokens": false,

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -1,3 +1,4 @@
 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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -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
 -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
 -1,-1,0.4545996860282574,0.6366718995290425,0.7228885400313971,0.8060910518053375,0.4545996860282574,0.2615469007664515,0.2807116692830978,0.40186584654388047,0.2218587127158556,0.4739396227677734,0.1520816326530612,0.5558179032632299,0.5629388627245769,0.49519347906125394,0.41529341414013715
+-1,-1,0.4914285714285715,0.6718367346938775,0.7428571428571428,0.8245525902668761,0.4914285714285715,0.29101290570653116,0.30013605442176866,0.44409159126937303,0.23019152276295135,0.509873722010538,0.15824489795918367,0.5791424793259958,0.5974918392265332,0.5309109052428821,0.45712865203428704

README.md

@@ -5,232 +5,51 @@ tags:
 - feature-extraction
 - dense
 - generated_from_trainer
-- dataset_size:359997
+- dataset_size:100000
 - loss:MultipleNegativesRankingLoss
-base_model: thenlper/gte-small
+base_model: prajjwal1/bert-small
 widget:
-- source_sentence: When do you use Ms. or Mrs.? Is one for a married woman and one
-    for one that's not married? Which one is for what?
+- source_sentence: How do I polish my English skills?
   sentences:
-  - When do you use Ms. or Mrs.? Is one for a married woman and one for one that's
-    not married? Which one is for what?
-  - Nations that do/does otherwise? Which one do I use?
-  - Why don't bikes have a gear indicator?
-- source_sentence: Which ointment is applied to the face of UFC fighters at the commencement
-    of a bout? What does it do?
+  - How can we polish English skills?
+  - Why should I move to Israel as a Jew?
+  - What are vitamins responsible for?
+- source_sentence: Can I use the Kozuka Gothic Pro font as a font-face on my web site?
   sentences:
-  - How can I save a Snapchat video that others posted?
-  - Which ointment is applied to the face of UFC fighters at the commencement of a
-    bout? What does it do?
-  - How do I get the body of a UFC Fighter?
-- source_sentence: Do you love the life you live?
+  - Can I use the Kozuka Gothic Pro font as a font-face on my web site?
+  - Why are Google, Facebook, YouTube and other social networking sites banned in
+    China?
+  - What font is used in Bloomberg Terminal?
+- source_sentence: Is Quora the best Q&A site?
   sentences:
-  - Can I do shoulder and triceps workout on same day? What other combinations like
-    this can I do?
-  - Do you love the life you're living?
-  - Where can you find an online TI-84 calculator?
-- source_sentence: Ordered food on Swiggy 3 days ago.After accepting my money, said
-    no more on Menu! When if ever will I atleast get refund in cr card a/c?
+  - What was the best Quora question ever?
+  - Is Quora the best inquiry site?
+  - Where do I buy Oway hair products online?
+- source_sentence: How can I customize my walking speed on Google Maps?
   sentences:
-  - Is getting to the Tel Aviv airport to catch a 5:30 AM flight very expensive?
-  - How do I die and make it look like an accident?
-  - Ordered food on Swiggy 3 days ago.After accepting my money, said no more on Menu!
-    When if ever will I atleast get refund in cr card a/c?
-- source_sentence: How do you earn money on Quora?
+  - How do I bring back Google maps icon in my home screen?
+  - How many pages are there in all the Harry Potter books combined?
+  - How can I customize my walking speed on Google Maps?
+- source_sentence: DId something exist before the Big Bang?
   sentences:
-  - What is a cheap healthy diet I can keep the same and eat every day?
-  - What are some things new employees should know going into their first day at Maximus?
-  - What is the best way to make money on Quora?
+  - How can I improve my memory problem?
+  - Where can I buy Fairy Tail Manga?
+  - Is there a scientific name for what existed before the Big Bang?
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
-metrics:
-- cosine_accuracy@1
-- cosine_accuracy@3
-- cosine_accuracy@5
-- cosine_accuracy@10
-- cosine_precision@1
-- cosine_precision@3
-- cosine_precision@5
-- cosine_precision@10
-- cosine_recall@1
-- cosine_recall@3
-- cosine_recall@5
-- cosine_recall@10
-- cosine_ndcg@10
-- cosine_mrr@10
-- cosine_map@100
-model-index:
-- name: SentenceTransformer based on thenlper/gte-small
-  results:
-  - task:
-      type: information-retrieval
-      name: Information Retrieval
-    dataset:
-      name: NanoMSMARCO
-      type: NanoMSMARCO
-    metrics:
-    - type: cosine_accuracy@1
-      value: 0.26
-      name: Cosine Accuracy@1
-    - type: cosine_accuracy@3
-      value: 0.56
-      name: Cosine Accuracy@3
-    - type: cosine_accuracy@5
-      value: 0.7
-      name: Cosine Accuracy@5
-    - type: cosine_accuracy@10
-      value: 0.78
-      name: Cosine Accuracy@10
-    - type: cosine_precision@1
-      value: 0.26
-      name: Cosine Precision@1
-    - type: cosine_precision@3
-      value: 0.18666666666666668
-      name: Cosine Precision@3
-    - type: cosine_precision@5
-      value: 0.14
-      name: Cosine Precision@5
-    - type: cosine_precision@10
-      value: 0.078
-      name: Cosine Precision@10
-    - type: cosine_recall@1
-      value: 0.26
-      name: Cosine Recall@1
-    - type: cosine_recall@3
-      value: 0.56
-      name: Cosine Recall@3
-    - type: cosine_recall@5
-      value: 0.7
-      name: Cosine Recall@5
-    - type: cosine_recall@10
-      value: 0.78
-      name: Cosine Recall@10
-    - type: cosine_ndcg@10
-      value: 0.5166057772867828
-      name: Cosine Ndcg@10
-    - type: cosine_mrr@10
-      value: 0.4317142857142857
-      name: Cosine Mrr@10
-    - type: cosine_map@100
-      value: 0.4425420850161611
-      name: Cosine Map@100
-  - task:
-      type: information-retrieval
-      name: Information Retrieval
-    dataset:
-      name: NanoNQ
-      type: NanoNQ
-    metrics:
-    - type: cosine_accuracy@1
-      value: 0.34
-      name: Cosine Accuracy@1
-    - type: cosine_accuracy@3
-      value: 0.56
-      name: Cosine Accuracy@3
-    - type: cosine_accuracy@5
-      value: 0.62
-      name: Cosine Accuracy@5
-    - type: cosine_accuracy@10
-      value: 0.68
-      name: Cosine Accuracy@10
-    - type: cosine_precision@1
-      value: 0.34
-      name: Cosine Precision@1
-    - type: cosine_precision@3
-      value: 0.19333333333333333
-      name: Cosine Precision@3
-    - type: cosine_precision@5
-      value: 0.128
-      name: Cosine Precision@5
-    - type: cosine_precision@10
-      value: 0.07200000000000001
-      name: Cosine Precision@10
-    - type: cosine_recall@1
-      value: 0.31
-      name: Cosine Recall@1
-    - type: cosine_recall@3
-      value: 0.53
-      name: Cosine Recall@3
-    - type: cosine_recall@5
-      value: 0.59
-      name: Cosine Recall@5
-    - type: cosine_recall@10
-      value: 0.66
-      name: Cosine Recall@10
-    - type: cosine_ndcg@10
-      value: 0.4981843146804683
-      name: Cosine Ndcg@10
-    - type: cosine_mrr@10
-      value: 0.46341269841269844
-      name: Cosine Mrr@10
-    - type: cosine_map@100
-      value: 0.4486372793651192
-      name: Cosine Map@100
-  - task:
-      type: nano-beir
-      name: Nano BEIR
-    dataset:
-      name: NanoBEIR mean
-      type: NanoBEIR_mean
-    metrics:
-    - type: cosine_accuracy@1
-      value: 0.30000000000000004
-      name: Cosine Accuracy@1
-    - type: cosine_accuracy@3
-      value: 0.56
-      name: Cosine Accuracy@3
-    - type: cosine_accuracy@5
-      value: 0.6599999999999999
-      name: Cosine Accuracy@5
-    - type: cosine_accuracy@10
-      value: 0.73
-      name: Cosine Accuracy@10
-    - type: cosine_precision@1
-      value: 0.30000000000000004
-      name: Cosine Precision@1
-    - type: cosine_precision@3
-      value: 0.19
-      name: Cosine Precision@3
-    - type: cosine_precision@5
-      value: 0.134
-      name: Cosine Precision@5
-    - type: cosine_precision@10
-      value: 0.07500000000000001
-      name: Cosine Precision@10
-    - type: cosine_recall@1
-      value: 0.28500000000000003
-      name: Cosine Recall@1
-    - type: cosine_recall@3
-      value: 0.545
-      name: Cosine Recall@3
-    - type: cosine_recall@5
-      value: 0.645
-      name: Cosine Recall@5
-    - type: cosine_recall@10
-      value: 0.72
-      name: Cosine Recall@10
-    - type: cosine_ndcg@10
-      value: 0.5073950459836256
-      name: Cosine Ndcg@10
-    - type: cosine_mrr@10
-      value: 0.4475634920634921
-      name: Cosine Mrr@10
-    - type: cosine_map@100
-      value: 0.44558968219064016
-      name: Cosine Map@100
 ---
 
-# SentenceTransformer based on thenlper/gte-small
+# SentenceTransformer based on prajjwal1/bert-small
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-small). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small). It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-- **Base model:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 17e1f347d17fe144873b1201da91788898c639cd -->
+- **Base model:** [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) <!-- at revision 0ec5f86f27c1a77d704439db5e01c307ea11b9d4 -->
 - **Maximum Sequence Length:** 128 tokens
-- **Output Dimensionality:** 384 dimensions
+- **Output Dimensionality:** 512 dimensions
 - **Similarity Function:** Cosine Similarity
 <!-- - **Training Dataset:** Unknown -->
 <!-- - **Language:** Unknown -->
@@ -247,8 +66,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [t
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'BertModel'})
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Normalize()
+  (1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
 )
 ```
 
@@ -267,23 +85,23 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("redis/model-a-baseline")
+model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    'How do you earn money on Quora?',
-    'What is the best way to make money on Quora?',
-    'What are some things new employees should know going into their first day at Maximus?',
+    'DId something exist before the Big Bang?',
+    'Is there a scientific name for what existed before the Big Bang?',
+    'Where can I buy Fairy Tail Manga?',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
-# [3, 384]
+# [3, 512]
 
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 0.9939, 0.0183],
-#         [0.9939, 1.0001, 0.0223],
-#         [0.0183, 0.0223, 1.0000]])
+# tensor([[ 1.0000,  0.7596, -0.0398],
+#         [ 0.7596,  1.0000, -0.0308],
+#         [-0.0398, -0.0308,  1.0000]])
 ```
 
 <!--
@@ -310,65 +128,6 @@ You can finetune this model on your own dataset.
 *List how the model may foreseeably be misused and address what users ought not to do with the model.*
 -->
 
-## Evaluation
-
-### Metrics
-
-#### Information Retrieval
-
-* Datasets: `NanoMSMARCO` and `NanoNQ`
-* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
-
-| Metric              | NanoMSMARCO | NanoNQ     |
-|:--------------------|:------------|:-----------|
-| cosine_accuracy@1   | 0.26        | 0.34       |
-| cosine_accuracy@3   | 0.56        | 0.56       |
-| cosine_accuracy@5   | 0.7         | 0.62       |
-| cosine_accuracy@10  | 0.78        | 0.68       |
-| cosine_precision@1  | 0.26        | 0.34       |
-| cosine_precision@3  | 0.1867      | 0.1933     |
-| cosine_precision@5  | 0.14        | 0.128      |
-| cosine_precision@10 | 0.078       | 0.072      |
-| cosine_recall@1     | 0.26        | 0.31       |
-| cosine_recall@3     | 0.56        | 0.53       |
-| cosine_recall@5     | 0.7         | 0.59       |
-| cosine_recall@10    | 0.78        | 0.66       |
-| **cosine_ndcg@10**  | **0.5166**  | **0.4982** |
-| cosine_mrr@10       | 0.4317      | 0.4634     |
-| cosine_map@100      | 0.4425      | 0.4486     |
-
-#### Nano BEIR
-
-* Dataset: `NanoBEIR_mean`
-* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters:
-  ```json
-  {
-      "dataset_names": [
-          "msmarco",
-          "nq"
-      ],
-      "dataset_id": "lightonai/NanoBEIR-en"
-  }
-  ```
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| cosine_accuracy@1   | 0.3        |
-| cosine_accuracy@3   | 0.56       |
-| cosine_accuracy@5   | 0.66       |
-| cosine_accuracy@10  | 0.73       |
-| cosine_precision@1  | 0.3        |
-| cosine_precision@3  | 0.19       |
-| cosine_precision@5  | 0.134      |
-| cosine_precision@10 | 0.075      |
-| cosine_recall@1     | 0.285      |
-| cosine_recall@3     | 0.545      |
-| cosine_recall@5     | 0.645      |
-| cosine_recall@10    | 0.72       |
-| **cosine_ndcg@10**  | **0.5074** |
-| cosine_mrr@10       | 0.4476     |
-| cosine_map@100      | 0.4456     |
-
 <!--
 ## Bias, Risks and Limitations
 
@@ -387,49 +146,23 @@ You can finetune this model on your own dataset.
 
 #### Unnamed Dataset
 
-* Size: 359,997 training samples
-* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | anchor                                                                            | positive                                                                          | negative                                                                           |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            | string                                                                             |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 15.46 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.52 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.99 tokens</li><li>max: 128 tokens</li></ul> |
-* Samples:
-  | anchor                                                                          | positive                                                                                             | negative                                                                                                                 |
-  |:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
-  | <code>Shall I upgrade my iPhone 5s to iOS 10 final version?</code>              | <code>Should I upgrade an iPhone 5s to iOS 10?</code>                                                | <code>Whether extension of CA-articleship is to be served at same firm/company?</code>                                   |
-  | <code>Is Donald Trump really going to be the president of United States?</code> | <code>Do you think Donald Trump could conceivably be the next President of the United States?</code> | <code>Since solid carbon dioxide is dry ice and incredibly cold, why doesn't it have an effect on global warming?</code> |
-  | <code>What are real tips to improve work life balance?</code>                   | <code>What are the best ways to create a work life balance?</code>                                   | <code>How do you open a briefcase combination lock without the combination?</code>                                       |
-* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
-  ```json
-  {
-      "scale": 7.0,
-      "similarity_fct": "cos_sim",
-      "gather_across_devices": false
-  }
-  ```
-
-### Evaluation Dataset
-
-#### Unnamed Dataset
-
-* Size: 40,000 evaluation samples
-* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Size: 100,000 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            | string                                                                            |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 15.71 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.79 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.97 tokens</li><li>max: 78 tokens</li></ul> |
+  |         | sentence_0                                                                        | sentence_1                                                                       | sentence_2                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                             |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 15.53 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.5 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.87 tokens</li><li>max: 128 tokens</li></ul> |
 * Samples:
-  | anchor                                                                                                            | positive                                                                                                          | negative                                                                                                                                  |
-  |:------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>Why were feathered dinosaur fossils only found in the last 20 years?</code>                                 | <code>Why were feathered dinosaur fossils only found in the last 20 years?</code>                                 | <code>Why are only few people aware that many dinosaurs had feathers?</code>                                                              |
-  | <code>If FOX News is the conservative news station, which cable news network is for liberals/progressives?</code> | <code>If FOX News is the conservative news station, which cable news network is for liberals/progressives?</code> | <code>How much did Fox News and conservative leaning media networks stoke the anger that contributed to Donald Trump's popularity?</code> |
-  | <code>How can guys last longer during sex?</code>                                                                 | <code>How do I last longer in sex?</code>                                                                         | <code>What is a permanent solution for rough and puffy hair?</code>                                                                       |
+  | sentence_0                                                                              | sentence_1                                                                              | sentence_2                                                             |
+  |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|
+  | <code>Is there visitor entry facility in Jaipur airport. How much is the ticket?</code> | <code>Is there visitor entry facility in Jaipur airport. How much is the ticket?</code> | <code>How much is the airport tax in bogota?</code>                    |
+  | <code>Which concept is more important: good planning or hard work?</code>               | <code>Which concept is more important: good planning or hard work?</code>               | <code>What is important in life: luck or hard work?</code>             |
+  | <code>What is the most efficient way to make money?</code>                              | <code>How can I make my money make money?</code>                                        | <code>What can one learn about Quantum Mechanics in 10 minutes?</code> |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
-      "scale": 7.0,
+      "scale": 20.0,
       "similarity_fct": "cos_sim",
       "gather_across_devices": false
   }
@@ -438,49 +171,36 @@ You can finetune this model on your own dataset.
 ### Training Hyperparameters
 #### Non-Default Hyperparameters
 
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 128
-- `per_device_eval_batch_size`: 128
-- `learning_rate`: 2e-05
-- `weight_decay`: 0.0001
-- `max_steps`: 5000
-- `warmup_ratio`: 0.1
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
 - `fp16`: True
-- `dataloader_drop_last`: True
-- `dataloader_num_workers`: 1
-- `dataloader_prefetch_factor`: 1
-- `load_best_model_at_end`: True
-- `optim`: adamw_torch
-- `ddp_find_unused_parameters`: False
-- `push_to_hub`: True
-- `hub_model_id`: redis/model-a-baseline
-- `eval_on_start`: True
+- `multi_dataset_batch_sampler`: round_robin
 
 #### All Hyperparameters
 <details><summary>Click to expand</summary>
 
 - `overwrite_output_dir`: False
 - `do_predict`: False
-- `eval_strategy`: steps
+- `eval_strategy`: no
 - `prediction_loss_only`: True
-- `per_device_train_batch_size`: 128
-- `per_device_eval_batch_size`: 128
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
 - `per_gpu_train_batch_size`: None
 - `per_gpu_eval_batch_size`: None
 - `gradient_accumulation_steps`: 1
 - `eval_accumulation_steps`: None
 - `torch_empty_cache_steps`: None
-- `learning_rate`: 2e-05
-- `weight_decay`: 0.0001
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
 - `adam_beta1`: 0.9
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 3.0
-- `max_steps`: 5000
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `max_steps`: -1
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.1
+- `warmup_ratio`: 0.0
 - `warmup_steps`: 0
 - `log_level`: passive
 - `log_level_replica`: warning
@@ -508,14 +228,14 @@ You can finetune this model on your own dataset.
 - `tpu_num_cores`: None
 - `tpu_metrics_debug`: False
 - `debug`: []
-- `dataloader_drop_last`: True
-- `dataloader_num_workers`: 1
-- `dataloader_prefetch_factor`: 1
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
 - `past_index`: -1
 - `disable_tqdm`: False
 - `remove_unused_columns`: True
 - `label_names`: None
-- `load_best_model_at_end`: True
+- `load_best_model_at_end`: False
 - `ignore_data_skip`: False
 - `fsdp`: []
 - `fsdp_min_num_params`: 0
@@ -525,23 +245,23 @@ You can finetune this model on your own dataset.
 - `parallelism_config`: None
 - `deepspeed`: None
 - `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
+- `optim`: adamw_torch_fused
 - `optim_args`: None
 - `adafactor`: False
 - `group_by_length`: False
 - `length_column_name`: length
 - `project`: huggingface
 - `trackio_space_id`: trackio
-- `ddp_find_unused_parameters`: False
+- `ddp_find_unused_parameters`: None
 - `ddp_bucket_cap_mb`: None
 - `ddp_broadcast_buffers`: False
 - `dataloader_pin_memory`: True
 - `dataloader_persistent_workers`: False
 - `skip_memory_metrics`: True
 - `use_legacy_prediction_loop`: False
-- `push_to_hub`: True
+- `push_to_hub`: False
 - `resume_from_checkpoint`: None
-- `hub_model_id`: redis/model-a-baseline
+- `hub_model_id`: None
 - `hub_strategy`: every_save
 - `hub_private_repo`: None
 - `hub_always_push`: False
@@ -568,43 +288,31 @@ You can finetune this model on your own dataset.
 - `neftune_noise_alpha`: None
 - `optim_target_modules`: None
 - `batch_eval_metrics`: False
-- `eval_on_start`: True
+- `eval_on_start`: False
 - `use_liger_kernel`: False
 - `liger_kernel_config`: None
 - `eval_use_gather_object`: False
 - `average_tokens_across_devices`: True
 - `prompts`: None
 - `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: proportional
+- `multi_dataset_batch_sampler`: round_robin
 - `router_mapping`: {}
 - `learning_rate_mapping`: {}
 
 </details>
 
 ### Training Logs
-| Epoch  | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
-|:------:|:----:|:-------------:|:---------------:|:--------------------------:|:---------------------:|:----------------------------:|
-| 0      | 0    | -             | 3.6640          | 0.6259                     | 0.6583                | 0.6421                       |
-| 0.0889 | 250  | 2.5091        | 0.4913          | 0.5518                     | 0.5570                | 0.5544                       |
-| 0.1778 | 500  | 0.6206        | 0.4434          | 0.5891                     | 0.5412                | 0.5651                       |
-| 0.2667 | 750  | 0.5549        | 0.4244          | 0.5579                     | 0.5372                | 0.5475                       |
-| 0.3556 | 1000 | 0.5305        | 0.4144          | 0.5611                     | 0.5284                | 0.5447                       |
-| 0.4445 | 1250 | 0.5155        | 0.4080          | 0.5410                     | 0.5196                | 0.5303                       |
-| 0.5334 | 1500 | 0.5101        | 0.4031          | 0.5497                     | 0.5281                | 0.5389                       |
-| 0.6223 | 1750 | 0.5035        | 0.4002          | 0.5329                     | 0.5259                | 0.5294                       |
-| 0.7112 | 2000 | 0.4955        | 0.3965          | 0.5299                     | 0.5412                | 0.5356                       |
-| 0.8001 | 2250 | 0.4906        | 0.3941          | 0.5402                     | 0.5250                | 0.5326                       |
-| 0.8890 | 2500 | 0.4882        | 0.3919          | 0.5323                     | 0.5316                | 0.5320                       |
-| 0.9780 | 2750 | 0.4845        | 0.3904          | 0.5260                     | 0.5121                | 0.5190                       |
-| 1.0669 | 3000 | 0.4769        | 0.3885          | 0.5208                     | 0.5235                | 0.5221                       |
-| 1.1558 | 3250 | 0.4745        | 0.3876          | 0.5216                     | 0.5019                | 0.5117                       |
-| 1.2447 | 3500 | 0.4718        | 0.3867          | 0.5110                     | 0.5030                | 0.5070                       |
-| 1.3336 | 3750 | 0.47          | 0.3863          | 0.5122                     | 0.5008                | 0.5065                       |
-| 1.4225 | 4000 | 0.4681        | 0.3855          | 0.5108                     | 0.4988                | 0.5048                       |
-| 1.5114 | 4250 | 0.4668        | 0.3846          | 0.5096                     | 0.4937                | 0.5016                       |
-| 1.6003 | 4500 | 0.4669        | 0.3843          | 0.5082                     | 0.4982                | 0.5032                       |
-| 1.6892 | 4750 | 0.4661        | 0.3840          | 0.5166                     | 0.4982                | 0.5074                       |
-| 1.7781 | 5000 | 0.467         | 0.3838          | 0.5166                     | 0.4982                | 0.5074                       |
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.3199 | 500  | 0.2284        |
+| 0.6398 | 1000 | 0.0571        |
+| 0.9597 | 1500 | 0.0486        |
+| 1.2796 | 2000 | 0.0378        |
+| 1.5995 | 2500 | 0.0367        |
+| 1.9194 | 3000 | 0.0338        |
+| 2.2393 | 3500 | 0.0327        |
+| 2.5592 | 4000 | 0.0285        |
+| 2.8791 | 4500 | 0.0285        |
 
 
 ### Framework Versions
@@ -613,7 +321,7 @@ You can finetune this model on your own dataset.
 - Transformers: 4.57.3
 - PyTorch: 2.9.1+cu128
 - Accelerate: 1.12.0
-- Datasets: 2.21.0
+- Datasets: 4.4.2
 - Tokenizers: 0.22.1
 
 ## Citation

config.json

@@ -1,24 +1,45 @@
 {
   "architectures": [
-    "BertModel"
+    "ModernBertModel"
   ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
   "dtype": "float32",
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 384,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
   "initializer_range": 0.02,
-  "intermediate_size": 1536,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
   "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
   "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
   "transformers_version": "4.57.3",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
+  "vocab_size": 50368
 }

eval/Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -83,3 +83,24 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 1.600284495021337,4500,0.26,0.56,0.7,0.76,0.26,0.26,0.18666666666666668,0.56,0.14,0.7,0.07600000000000001,0.76,0.4263809523809524,0.5082058856889958,0.4389900767590395
 1.689189189189189,4750,0.26,0.56,0.7,0.78,0.26,0.26,0.18666666666666668,0.56,0.14,0.7,0.078,0.78,0.4317142857142857,0.5166057772867828,0.4425420850161611
 1.7780938833570412,5000,0.26,0.56,0.7,0.78,0.26,0.26,0.18666666666666668,0.56,0.14,0.7,0.078,0.78,0.4317142857142857,0.5166057772867828,0.4425420850161611
+0,0,0.44,0.72,0.76,0.86,0.44,0.44,0.24,0.72,0.15200000000000002,0.76,0.08599999999999998,0.86,0.5872222222222222,0.6530251712549636,0.5942230199326107
+0.08890469416785206,250,0.4,0.66,0.78,0.84,0.4,0.4,0.21999999999999997,0.66,0.15600000000000003,0.78,0.08399999999999999,0.84,0.5537222222222221,0.6233489066081196,0.5635062977957714
+0.17780938833570412,500,0.36,0.64,0.76,0.86,0.36,0.36,0.21333333333333332,0.64,0.15200000000000002,0.76,0.08599999999999998,0.86,0.5344126984126983,0.6136581255652764,0.5411879592416676
+0.26671408250355616,750,0.4,0.66,0.68,0.84,0.4,0.4,0.21999999999999997,0.66,0.136,0.68,0.08399999999999999,0.84,0.5555714285714286,0.6243145435136173,0.5640618250272982
+0.35561877667140823,1000,0.38,0.68,0.74,0.86,0.38,0.38,0.22666666666666668,0.68,0.14800000000000002,0.74,0.08599999999999998,0.86,0.5495793650793651,0.6250033046333645,0.557006253006253
+0.4445234708392603,1250,0.38,0.66,0.7,0.86,0.38,0.38,0.21999999999999997,0.66,0.14,0.7,0.08599999999999998,0.86,0.5403809523809523,0.6170462223747559,0.5470477611334389
+0.5334281650071123,1500,0.38,0.62,0.68,0.84,0.38,0.38,0.20666666666666667,0.62,0.136,0.68,0.08399999999999999,0.84,0.5305793650793651,0.6048908769916042,0.538908464901112
+0.6223328591749644,1750,0.34,0.62,0.74,0.82,0.34,0.34,0.20666666666666667,0.62,0.14800000000000002,0.74,0.08199999999999999,0.82,0.5072698412698412,0.583457909676193,0.5173745553962945
+0.7112375533428165,2000,0.36,0.62,0.72,0.84,0.36,0.36,0.20666666666666664,0.62,0.14400000000000002,0.72,0.08399999999999999,0.84,0.5175238095238095,0.5955397543812248,0.5268191253191253
+0.8001422475106685,2250,0.38,0.64,0.72,0.82,0.38,0.38,0.21333333333333332,0.64,0.14400000000000002,0.72,0.08199999999999999,0.82,0.5345238095238095,0.603696422404652,0.5446621125230965
+0.8890469416785206,2500,0.36,0.66,0.72,0.8,0.36,0.36,0.22,0.66,0.14400000000000002,0.72,0.08,0.8,0.5193571428571429,0.5883743208686202,0.531178052701582
+0.9779516358463727,2750,0.34,0.66,0.72,0.8,0.34,0.34,0.22,0.66,0.14400000000000002,0.72,0.08,0.8,0.509238095238095,0.5808928079955077,0.5217201394797302
+1.0668563300142249,3000,0.36,0.62,0.72,0.78,0.36,0.36,0.20666666666666667,0.62,0.14400000000000002,0.72,0.07800000000000001,0.78,0.5021904761904762,0.5700934532257512,0.5138738116598179
+1.1557610241820768,3250,0.36,0.62,0.72,0.76,0.36,0.36,0.20666666666666667,0.62,0.14400000000000002,0.72,0.07600000000000001,0.76,0.5068571428571428,0.5690721446643242,0.5200125698829765
+1.2446657183499288,3500,0.38,0.58,0.72,0.76,0.38,0.38,0.19333333333333333,0.58,0.14400000000000002,0.72,0.07600000000000001,0.76,0.5078333333333334,0.569428899857823,0.5212677107789235
+1.333570412517781,3750,0.36,0.62,0.72,0.78,0.36,0.36,0.20666666666666667,0.62,0.14400000000000002,0.72,0.078,0.78,0.5070238095238095,0.5736393221453866,0.5175857474826515
+1.422475106685633,4000,0.38,0.6,0.72,0.78,0.38,0.38,0.2,0.6,0.14400000000000002,0.72,0.078,0.78,0.5172698412698412,0.5810965757911744,0.5283554988447523
+1.5113798008534851,4250,0.32,0.64,0.7,0.76,0.32,0.32,0.21333333333333332,0.64,0.14,0.7,0.07600000000000001,0.76,0.4843809523809523,0.5524731912952869,0.4971737256100805
+1.600284495021337,4500,0.38,0.62,0.72,0.78,0.38,0.38,0.20666666666666667,0.62,0.14400000000000002,0.72,0.078,0.78,0.5160238095238094,0.5801442520571805,0.5268918517084115
+1.689189189189189,4750,0.38,0.62,0.72,0.78,0.38,0.38,0.20666666666666667,0.62,0.14400000000000002,0.72,0.078,0.78,0.5157460317460317,0.5798555544347456,0.5268006584975625
+1.7780938833570412,5000,0.38,0.62,0.72,0.78,0.38,0.38,0.20666666666666667,0.62,0.14400000000000002,0.72,0.078,0.78,0.5150238095238094,0.5792677770404034,0.5260186479155519

eval/Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -83,3 +83,24 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 1.600284495021337,4500,0.34,0.56,0.62,0.68,0.34,0.31,0.19333333333333333,0.53,0.128,0.59,0.07200000000000001,0.66,0.46341269841269844,0.4981843146804683,0.44850926901873406
 1.689189189189189,4750,0.34,0.56,0.62,0.68,0.34,0.31,0.19333333333333333,0.53,0.128,0.59,0.07200000000000001,0.66,0.46341269841269844,0.4981843146804683,0.4485243115129495
 1.7780938833570412,5000,0.34,0.56,0.62,0.68,0.34,0.31,0.19333333333333333,0.53,0.128,0.59,0.07200000000000001,0.66,0.46341269841269844,0.4981843146804683,0.4486372793651192
+0,0,0.52,0.7,0.78,0.84,0.52,0.49,0.2333333333333333,0.65,0.15600000000000003,0.73,0.08999999999999998,0.8,0.6295238095238095,0.655180139603386,0.6050874426120327
+0.08890469416785206,250,0.52,0.7,0.74,0.82,0.52,0.48,0.23333333333333336,0.65,0.15600000000000003,0.71,0.08999999999999998,0.8,0.6152460317460318,0.6439323830674052,0.590834612167892
+0.17780938833570412,500,0.52,0.68,0.72,0.8,0.52,0.47,0.23333333333333328,0.64,0.15200000000000002,0.68,0.08799999999999997,0.78,0.6139682539682539,0.6371903799776186,0.5880927716257193
+0.26671408250355616,750,0.5,0.66,0.74,0.8,0.5,0.45,0.22666666666666666,0.62,0.156,0.7,0.08599999999999998,0.77,0.604079365079365,0.6258743003534032,0.5760735054262106
+0.35561877667140823,1000,0.42,0.66,0.72,0.8,0.42,0.38,0.2333333333333333,0.63,0.156,0.69,0.08599999999999998,0.77,0.5576031746031745,0.5981468723038755,0.5429824862085043
+0.4445234708392603,1250,0.46,0.66,0.72,0.8,0.46,0.42,0.22666666666666666,0.62,0.156,0.69,0.08599999999999998,0.77,0.5702380952380952,0.6045200178625042,0.550527864737212
+0.5334281650071123,1500,0.42,0.62,0.72,0.78,0.42,0.39,0.21333333333333332,0.59,0.156,0.69,0.08399999999999999,0.75,0.5405714285714285,0.5821127911484091,0.5295401715300265
+0.6223328591749644,1750,0.4,0.58,0.68,0.78,0.4,0.35,0.20666666666666664,0.55,0.14800000000000002,0.65,0.08399999999999999,0.75,0.5228571428571428,0.5619256762628706,0.5001688995702154
+0.7112375533428165,2000,0.4,0.58,0.7,0.76,0.4,0.36,0.21333333333333332,0.56,0.15200000000000002,0.67,0.08199999999999999,0.73,0.5179126984126984,0.5575556261393345,0.5047886285359457
+0.8001422475106685,2250,0.46,0.58,0.7,0.76,0.46,0.42,0.2,0.55,0.14800000000000002,0.66,0.08199999999999999,0.73,0.5478333333333333,0.5785671069657581,0.5320591074477433
+0.8890469416785206,2500,0.42,0.58,0.72,0.78,0.42,0.38,0.2,0.55,0.156,0.69,0.08399999999999999,0.75,0.5283571428571429,0.569127931822496,0.5105739762955421
+0.9779516358463727,2750,0.4,0.58,0.7,0.78,0.4,0.36,0.2,0.55,0.15200000000000002,0.68,0.08399999999999999,0.75,0.5191666666666667,0.5627046734671732,0.5011601577654209
+1.0668563300142249,3000,0.42,0.58,0.68,0.76,0.42,0.38,0.2,0.54,0.14400000000000002,0.64,0.08199999999999999,0.73,0.5250238095238094,0.5599080804311154,0.5049299021908467
+1.1557610241820768,3250,0.38,0.54,0.7,0.74,0.38,0.36,0.18666666666666665,0.5,0.14800000000000002,0.66,0.08,0.71,0.49938095238095237,0.5418779926276425,0.48958588825244265
+1.2446657183499288,3500,0.42,0.56,0.68,0.74,0.42,0.39,0.2,0.54,0.14400000000000002,0.64,0.078,0.7,0.5208571428571428,0.5533867165556,0.509604859647127
+1.333570412517781,3750,0.4,0.58,0.68,0.74,0.4,0.38,0.2,0.54,0.14400000000000002,0.64,0.08,0.71,0.512047619047619,0.5519481375051689,0.5032554612510217
+1.422475106685633,4000,0.38,0.58,0.68,0.74,0.38,0.36,0.2,0.54,0.14400000000000002,0.64,0.08,0.71,0.49835714285714283,0.5415907683742168,0.4895576780176279
+1.5113798008534851,4250,0.4,0.58,0.66,0.74,0.4,0.38,0.2,0.54,0.14,0.63,0.08,0.71,0.5143571428571428,0.5538335579266234,0.5059206767449402
+1.600284495021337,4500,0.38,0.56,0.66,0.74,0.38,0.35,0.19333333333333333,0.53,0.14,0.62,0.078,0.7,0.5015714285714286,0.5379880507233898,0.48801468381441493
+1.689189189189189,4750,0.4,0.58,0.66,0.74,0.4,0.37,0.2,0.54,0.14,0.63,0.078,0.7,0.5143571428571428,0.5474332549729899,0.5004754288333266
+1.7780938833570412,5000,0.38,0.58,0.66,0.74,0.38,0.36,0.2,0.54,0.14,0.62,0.078,0.7,0.5033571428571428,0.5417937853620868,0.4942594774374801

eval/NanoBEIR_evaluation_mean_results.csv

@@ -83,3 +83,24 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 1.600284495021337,4500,0.30000000000000004,0.56,0.6599999999999999,0.72,0.30000000000000004,0.28500000000000003,0.19,0.545,0.134,0.645,0.07400000000000001,0.71,0.4448968253968254,0.5031951001847321,0.4437496728888868
 1.689189189189189,4750,0.30000000000000004,0.56,0.6599999999999999,0.73,0.30000000000000004,0.28500000000000003,0.19,0.545,0.134,0.645,0.07500000000000001,0.72,0.4475634920634921,0.5073950459836256,0.4455331982645553
 1.7780938833570412,5000,0.30000000000000004,0.56,0.6599999999999999,0.73,0.30000000000000004,0.28500000000000003,0.19,0.545,0.134,0.645,0.07500000000000001,0.72,0.4475634920634921,0.5073950459836256,0.44558968219064016
+0,0,0.48,0.71,0.77,0.85,0.48,0.46499999999999997,0.23666666666666664,0.685,0.15400000000000003,0.745,0.08799999999999998,0.8300000000000001,0.6083730158730158,0.6541026554291748,0.5996552312723218
+0.08890469416785206,250,0.46,0.6799999999999999,0.76,0.83,0.46,0.44,0.22666666666666668,0.655,0.15600000000000003,0.745,0.087,0.8200000000000001,0.584484126984127,0.6336406448377624,0.5771704549818317
+0.17780938833570412,500,0.44,0.66,0.74,0.8300000000000001,0.44,0.415,0.2233333333333333,0.64,0.15200000000000002,0.72,0.08699999999999997,0.8200000000000001,0.5741904761904761,0.6254242527714475,0.5646403654336934
+0.26671408250355616,750,0.45,0.66,0.71,0.8200000000000001,0.45,0.42500000000000004,0.22333333333333333,0.64,0.14600000000000002,0.69,0.08499999999999999,0.8049999999999999,0.5798253968253968,0.6250944219335102,0.5700676652267544
+0.35561877667140823,1000,0.4,0.67,0.73,0.8300000000000001,0.4,0.38,0.22999999999999998,0.655,0.15200000000000002,0.715,0.08599999999999998,0.815,0.5535912698412698,0.61157508846862,0.5499943696073786
+0.4445234708392603,1250,0.42000000000000004,0.66,0.71,0.8300000000000001,0.42000000000000004,0.4,0.22333333333333333,0.64,0.14800000000000002,0.695,0.08599999999999998,0.815,0.5553095238095238,0.61078312011863,0.5487878129353254
+0.5334281650071123,1500,0.4,0.62,0.7,0.81,0.4,0.385,0.21,0.605,0.14600000000000002,0.685,0.08399999999999999,0.7949999999999999,0.5355753968253968,0.5935018340700067,0.5342243182155693
+0.6223328591749644,1750,0.37,0.6,0.71,0.8,0.37,0.345,0.20666666666666667,0.585,0.14800000000000002,0.6950000000000001,0.08299999999999999,0.7849999999999999,0.515063492063492,0.5726917929695319,0.5087717274832549
+0.7112375533428165,2000,0.38,0.6,0.71,0.8,0.38,0.36,0.20999999999999996,0.5900000000000001,0.14800000000000002,0.6950000000000001,0.08299999999999999,0.7849999999999999,0.5177182539682539,0.5765476902602796,0.5158038769275355
+0.8001422475106685,2250,0.42000000000000004,0.61,0.71,0.79,0.42000000000000004,0.4,0.20666666666666667,0.595,0.14600000000000002,0.69,0.08199999999999999,0.7749999999999999,0.5411785714285714,0.591131764685205,0.5383606099854199
+0.8890469416785206,2500,0.39,0.62,0.72,0.79,0.39,0.37,0.21000000000000002,0.605,0.15000000000000002,0.705,0.08199999999999999,0.775,0.5238571428571428,0.578751126345558,0.520876014498562
+0.9779516358463727,2750,0.37,0.62,0.71,0.79,0.37,0.35,0.21000000000000002,0.605,0.14800000000000002,0.7,0.08199999999999999,0.775,0.5142023809523808,0.5717987407313405,0.5114401486225755
+1.0668563300142249,3000,0.39,0.6,0.7,0.77,0.39,0.37,0.20333333333333334,0.5800000000000001,0.14400000000000002,0.6799999999999999,0.08,0.755,0.5136071428571428,0.5650007668284334,0.5094018569253322
+1.1557610241820768,3250,0.37,0.5800000000000001,0.71,0.75,0.37,0.36,0.19666666666666666,0.56,0.14600000000000002,0.69,0.07800000000000001,0.735,0.5031190476190476,0.5554750686459833,0.5047992290677096
+1.2446657183499288,3500,0.4,0.5700000000000001,0.7,0.75,0.4,0.385,0.19666666666666666,0.56,0.14400000000000002,0.6799999999999999,0.07700000000000001,0.73,0.514345238095238,0.5614078082067115,0.5154362852130252
+1.333570412517781,3750,0.38,0.6,0.7,0.76,0.38,0.37,0.20333333333333334,0.5800000000000001,0.14400000000000002,0.6799999999999999,0.079,0.745,0.5095357142857142,0.5627937298252778,0.5104206043668367
+1.422475106685633,4000,0.38,0.59,0.7,0.76,0.38,0.37,0.2,0.5700000000000001,0.14400000000000002,0.6799999999999999,0.079,0.745,0.507813492063492,0.5613436720826956,0.5089565884311901
+1.5113798008534851,4250,0.36,0.61,0.6799999999999999,0.75,0.36,0.35,0.20666666666666667,0.5900000000000001,0.14,0.665,0.07800000000000001,0.735,0.49936904761904755,0.5531533746109552,0.5015472011775104
+1.600284495021337,4500,0.38,0.5900000000000001,0.69,0.76,0.38,0.365,0.2,0.575,0.14200000000000002,0.6699999999999999,0.078,0.74,0.508797619047619,0.5590661513902851,0.5074532677614132
+1.689189189189189,4750,0.39,0.6,0.69,0.76,0.39,0.375,0.20333333333333334,0.5800000000000001,0.14200000000000002,0.675,0.078,0.74,0.5150515873015873,0.5636444047038678,0.5136380436654446
+1.7780938833570412,5000,0.38,0.6,0.69,0.76,0.38,0.37,0.20333333333333334,0.5800000000000001,0.14200000000000002,0.6699999999999999,0.078,0.74,0.5091904761904762,0.5605307812012451,0.510139062676516

final_metrics.json

@@ -1,231 +1,231 @@
 {
   "nano_beir": {
-    "NanoClimateFEVER_cosine_accuracy@1": 0.12,
-    "NanoClimateFEVER_cosine_accuracy@3": 0.28,
-    "NanoClimateFEVER_cosine_accuracy@5": 0.46,
-    "NanoClimateFEVER_cosine_accuracy@10": 0.66,
-    "NanoClimateFEVER_cosine_precision@1": 0.12,
-    "NanoClimateFEVER_cosine_precision@3": 0.09999999999999998,
-    "NanoClimateFEVER_cosine_precision@5": 0.1,
-    "NanoClimateFEVER_cosine_precision@10": 0.07600000000000001,
-    "NanoClimateFEVER_cosine_recall@1": 0.03833333333333333,
-    "NanoClimateFEVER_cosine_recall@3": 0.135,
-    "NanoClimateFEVER_cosine_recall@5": 0.23633333333333334,
-    "NanoClimateFEVER_cosine_recall@10": 0.32466666666666666,
-    "NanoClimateFEVER_cosine_ndcg@10": 0.20845131843009276,
-    "NanoClimateFEVER_cosine_mrr@10": 0.2561746031746031,
-    "NanoClimateFEVER_cosine_map@100": 0.14245832634750027,
+    "NanoClimateFEVER_cosine_accuracy@1": 0.28,
+    "NanoClimateFEVER_cosine_accuracy@3": 0.5,
+    "NanoClimateFEVER_cosine_accuracy@5": 0.52,
+    "NanoClimateFEVER_cosine_accuracy@10": 0.72,
+    "NanoClimateFEVER_cosine_precision@1": 0.28,
+    "NanoClimateFEVER_cosine_precision@3": 0.16666666666666669,
+    "NanoClimateFEVER_cosine_precision@5": 0.10800000000000001,
+    "NanoClimateFEVER_cosine_precision@10": 0.08399999999999999,
+    "NanoClimateFEVER_cosine_recall@1": 0.12,
+    "NanoClimateFEVER_cosine_recall@3": 0.22166666666666665,
+    "NanoClimateFEVER_cosine_recall@5": 0.2483333333333333,
+    "NanoClimateFEVER_cosine_recall@10": 0.36733333333333335,
+    "NanoClimateFEVER_cosine_ndcg@10": 0.28399247526326993,
+    "NanoClimateFEVER_cosine_mrr@10": 0.39877777777777773,
+    "NanoClimateFEVER_cosine_map@100": 0.21216679851781925,
     "NanoDBPedia_cosine_accuracy@1": 0.66,
-    "NanoDBPedia_cosine_accuracy@3": 0.76,
-    "NanoDBPedia_cosine_accuracy@5": 0.86,
-    "NanoDBPedia_cosine_accuracy@10": 0.88,
+    "NanoDBPedia_cosine_accuracy@3": 0.78,
+    "NanoDBPedia_cosine_accuracy@5": 0.8,
+    "NanoDBPedia_cosine_accuracy@10": 0.92,
     "NanoDBPedia_cosine_precision@1": 0.66,
-    "NanoDBPedia_cosine_precision@3": 0.4733333333333334,
-    "NanoDBPedia_cosine_precision@5": 0.44800000000000006,
-    "NanoDBPedia_cosine_precision@10": 0.34800000000000003,
-    "NanoDBPedia_cosine_recall@1": 0.08255861663979128,
-    "NanoDBPedia_cosine_recall@3": 0.1250415886989628,
-    "NanoDBPedia_cosine_recall@5": 0.17452224602058436,
-    "NanoDBPedia_cosine_recall@10": 0.25437495331636306,
-    "NanoDBPedia_cosine_ndcg@10": 0.4636652485066659,
-    "NanoDBPedia_cosine_mrr@10": 0.7288333333333334,
-    "NanoDBPedia_cosine_map@100": 0.32939375725902226,
-    "NanoFEVER_cosine_accuracy@1": 0.7,
-    "NanoFEVER_cosine_accuracy@3": 0.86,
-    "NanoFEVER_cosine_accuracy@5": 0.9,
-    "NanoFEVER_cosine_accuracy@10": 0.98,
-    "NanoFEVER_cosine_precision@1": 0.7,
-    "NanoFEVER_cosine_precision@3": 0.2866666666666667,
-    "NanoFEVER_cosine_precision@5": 0.184,
-    "NanoFEVER_cosine_precision@10": 0.09999999999999998,
-    "NanoFEVER_cosine_recall@1": 0.6466666666666666,
-    "NanoFEVER_cosine_recall@3": 0.8066666666666665,
-    "NanoFEVER_cosine_recall@5": 0.8566666666666666,
-    "NanoFEVER_cosine_recall@10": 0.9266666666666667,
-    "NanoFEVER_cosine_ndcg@10": 0.7923317127841635,
-    "NanoFEVER_cosine_mrr@10": 0.784190476190476,
-    "NanoFEVER_cosine_map@100": 0.7390417679680837,
+    "NanoDBPedia_cosine_precision@3": 0.5,
+    "NanoDBPedia_cosine_precision@5": 0.4160000000000001,
+    "NanoDBPedia_cosine_precision@10": 0.354,
+    "NanoDBPedia_cosine_recall@1": 0.07324232876100338,
+    "NanoDBPedia_cosine_recall@3": 0.1458081980942187,
+    "NanoDBPedia_cosine_recall@5": 0.18062353317681695,
+    "NanoDBPedia_cosine_recall@10": 0.2682740597758614,
+    "NanoDBPedia_cosine_ndcg@10": 0.4702867365632332,
+    "NanoDBPedia_cosine_mrr@10": 0.7401904761904763,
+    "NanoDBPedia_cosine_map@100": 0.35022041415987587,
+    "NanoFEVER_cosine_accuracy@1": 0.76,
+    "NanoFEVER_cosine_accuracy@3": 0.92,
+    "NanoFEVER_cosine_accuracy@5": 1.0,
+    "NanoFEVER_cosine_accuracy@10": 1.0,
+    "NanoFEVER_cosine_precision@1": 0.76,
+    "NanoFEVER_cosine_precision@3": 0.32,
+    "NanoFEVER_cosine_precision@5": 0.20799999999999996,
+    "NanoFEVER_cosine_precision@10": 0.10399999999999998,
+    "NanoFEVER_cosine_recall@1": 0.7066666666666666,
+    "NanoFEVER_cosine_recall@3": 0.8733333333333333,
+    "NanoFEVER_cosine_recall@5": 0.9533333333333333,
+    "NanoFEVER_cosine_recall@10": 0.9533333333333333,
+    "NanoFEVER_cosine_ndcg@10": 0.8454367717882709,
+    "NanoFEVER_cosine_mrr@10": 0.8390000000000001,
+    "NanoFEVER_cosine_map@100": 0.7968940242763771,
     "NanoFiQA2018_cosine_accuracy@1": 0.28,
     "NanoFiQA2018_cosine_accuracy@3": 0.38,
-    "NanoFiQA2018_cosine_accuracy@5": 0.52,
-    "NanoFiQA2018_cosine_accuracy@10": 0.6,
+    "NanoFiQA2018_cosine_accuracy@5": 0.44,
+    "NanoFiQA2018_cosine_accuracy@10": 0.56,
     "NanoFiQA2018_cosine_precision@1": 0.28,
-    "NanoFiQA2018_cosine_precision@3": 0.16666666666666663,
-    "NanoFiQA2018_cosine_precision@5": 0.14400000000000002,
-    "NanoFiQA2018_cosine_precision@10": 0.08999999999999998,
-    "NanoFiQA2018_cosine_recall@1": 0.12974603174603175,
-    "NanoFiQA2018_cosine_recall@3": 0.20312698412698413,
-    "NanoFiQA2018_cosine_recall@5": 0.2956269841269841,
-    "NanoFiQA2018_cosine_recall@10": 0.3792936507936508,
-    "NanoFiQA2018_cosine_ndcg@10": 0.29381486022787706,
-    "NanoFiQA2018_cosine_mrr@10": 0.3612698412698412,
-    "NanoFiQA2018_cosine_map@100": 0.24011197414286956,
-    "NanoHotpotQA_cosine_accuracy@1": 0.64,
-    "NanoHotpotQA_cosine_accuracy@3": 0.74,
-    "NanoHotpotQA_cosine_accuracy@5": 0.78,
-    "NanoHotpotQA_cosine_accuracy@10": 0.84,
-    "NanoHotpotQA_cosine_precision@1": 0.64,
-    "NanoHotpotQA_cosine_precision@3": 0.3,
-    "NanoHotpotQA_cosine_precision@5": 0.19599999999999998,
-    "NanoHotpotQA_cosine_precision@10": 0.11199999999999999,
-    "NanoHotpotQA_cosine_recall@1": 0.32,
-    "NanoHotpotQA_cosine_recall@3": 0.45,
-    "NanoHotpotQA_cosine_recall@5": 0.49,
-    "NanoHotpotQA_cosine_recall@10": 0.56,
-    "NanoHotpotQA_cosine_ndcg@10": 0.5341052902954041,
-    "NanoHotpotQA_cosine_mrr@10": 0.704079365079365,
-    "NanoHotpotQA_cosine_map@100": 0.46245563144445145,
-    "NanoMSMARCO_cosine_accuracy@1": 0.3,
+    "NanoFiQA2018_cosine_precision@3": 0.16,
+    "NanoFiQA2018_cosine_precision@5": 0.14,
+    "NanoFiQA2018_cosine_precision@10": 0.088,
+    "NanoFiQA2018_cosine_recall@1": 0.12474603174603174,
+    "NanoFiQA2018_cosine_recall@3": 0.19496031746031747,
+    "NanoFiQA2018_cosine_recall@5": 0.28234920634920635,
+    "NanoFiQA2018_cosine_recall@10": 0.3491825396825397,
+    "NanoFiQA2018_cosine_ndcg@10": 0.28171892775593277,
+    "NanoFiQA2018_cosine_mrr@10": 0.34983333333333333,
+    "NanoFiQA2018_cosine_map@100": 0.23002318958083748,
+    "NanoHotpotQA_cosine_accuracy@1": 0.74,
+    "NanoHotpotQA_cosine_accuracy@3": 0.84,
+    "NanoHotpotQA_cosine_accuracy@5": 0.9,
+    "NanoHotpotQA_cosine_accuracy@10": 0.9,
+    "NanoHotpotQA_cosine_precision@1": 0.74,
+    "NanoHotpotQA_cosine_precision@3": 0.40666666666666657,
+    "NanoHotpotQA_cosine_precision@5": 0.27599999999999997,
+    "NanoHotpotQA_cosine_precision@10": 0.154,
+    "NanoHotpotQA_cosine_recall@1": 0.37,
+    "NanoHotpotQA_cosine_recall@3": 0.61,
+    "NanoHotpotQA_cosine_recall@5": 0.69,
+    "NanoHotpotQA_cosine_recall@10": 0.77,
+    "NanoHotpotQA_cosine_ndcg@10": 0.7043467589883098,
+    "NanoHotpotQA_cosine_mrr@10": 0.8006666666666667,
+    "NanoHotpotQA_cosine_map@100": 0.6277244926048727,
+    "NanoMSMARCO_cosine_accuracy@1": 0.26,
     "NanoMSMARCO_cosine_accuracy@3": 0.56,
-    "NanoMSMARCO_cosine_accuracy@5": 0.66,
-    "NanoMSMARCO_cosine_accuracy@10": 0.8,
-    "NanoMSMARCO_cosine_precision@1": 0.3,
-    "NanoMSMARCO_cosine_precision@3": 0.18666666666666665,
-    "NanoMSMARCO_cosine_precision@5": 0.132,
-    "NanoMSMARCO_cosine_precision@10": 0.08,
-    "NanoMSMARCO_cosine_recall@1": 0.3,
+    "NanoMSMARCO_cosine_accuracy@5": 0.7,
+    "NanoMSMARCO_cosine_accuracy@10": 0.78,
+    "NanoMSMARCO_cosine_precision@1": 0.26,
+    "NanoMSMARCO_cosine_precision@3": 0.18666666666666668,
+    "NanoMSMARCO_cosine_precision@5": 0.14,
+    "NanoMSMARCO_cosine_precision@10": 0.078,
+    "NanoMSMARCO_cosine_recall@1": 0.26,
     "NanoMSMARCO_cosine_recall@3": 0.56,
-    "NanoMSMARCO_cosine_recall@5": 0.66,
-    "NanoMSMARCO_cosine_recall@10": 0.8,
-    "NanoMSMARCO_cosine_ndcg@10": 0.5355647548788993,
-    "NanoMSMARCO_cosine_mrr@10": 0.4522142857142857,
-    "NanoMSMARCO_cosine_map@100": 0.4614012040294735,
-    "NanoNFCorpus_cosine_accuracy@1": 0.36,
-    "NanoNFCorpus_cosine_accuracy@3": 0.54,
-    "NanoNFCorpus_cosine_accuracy@5": 0.58,
+    "NanoMSMARCO_cosine_recall@5": 0.7,
+    "NanoMSMARCO_cosine_recall@10": 0.78,
+    "NanoMSMARCO_cosine_ndcg@10": 0.5166057772867828,
+    "NanoMSMARCO_cosine_mrr@10": 0.4317142857142857,
+    "NanoMSMARCO_cosine_map@100": 0.4425420850161611,
+    "NanoNFCorpus_cosine_accuracy@1": 0.38,
+    "NanoNFCorpus_cosine_accuracy@3": 0.46,
+    "NanoNFCorpus_cosine_accuracy@5": 0.52,
     "NanoNFCorpus_cosine_accuracy@10": 0.66,
-    "NanoNFCorpus_cosine_precision@1": 0.36,
-    "NanoNFCorpus_cosine_precision@3": 0.34,
-    "NanoNFCorpus_cosine_precision@5": 0.308,
-    "NanoNFCorpus_cosine_precision@10": 0.242,
-    "NanoNFCorpus_cosine_recall@1": 0.013678895813410474,
-    "NanoNFCorpus_cosine_recall@3": 0.05640197371329614,
-    "NanoNFCorpus_cosine_recall@5": 0.07379464684205841,
-    "NanoNFCorpus_cosine_recall@10": 0.09966914120321839,
-    "NanoNFCorpus_cosine_ndcg@10": 0.2773671235823583,
-    "NanoNFCorpus_cosine_mrr@10": 0.45522222222222225,
-    "NanoNFCorpus_cosine_map@100": 0.10548993594921903,
-    "NanoNQ_cosine_accuracy@1": 0.3,
-    "NanoNQ_cosine_accuracy@3": 0.54,
+    "NanoNFCorpus_cosine_precision@1": 0.38,
+    "NanoNFCorpus_cosine_precision@3": 0.3,
+    "NanoNFCorpus_cosine_precision@5": 0.288,
+    "NanoNFCorpus_cosine_precision@10": 0.262,
+    "NanoNFCorpus_cosine_recall@1": 0.03212466721866998,
+    "NanoNFCorpus_cosine_recall@3": 0.04815148484556365,
+    "NanoNFCorpus_cosine_recall@5": 0.0674537100479343,
+    "NanoNFCorpus_cosine_recall@10": 0.12106237128917721,
+    "NanoNFCorpus_cosine_ndcg@10": 0.2990794029698064,
+    "NanoNFCorpus_cosine_mrr@10": 0.43976984126984114,
+    "NanoNFCorpus_cosine_map@100": 0.13069447368464218,
+    "NanoNQ_cosine_accuracy@1": 0.34,
+    "NanoNQ_cosine_accuracy@3": 0.56,
     "NanoNQ_cosine_accuracy@5": 0.62,
-    "NanoNQ_cosine_accuracy@10": 0.7,
-    "NanoNQ_cosine_precision@1": 0.3,
-    "NanoNQ_cosine_precision@3": 0.18,
-    "NanoNQ_cosine_precision@5": 0.132,
-    "NanoNQ_cosine_precision@10": 0.07400000000000001,
-    "NanoNQ_cosine_recall@1": 0.29,
-    "NanoNQ_cosine_recall@3": 0.51,
-    "NanoNQ_cosine_recall@5": 0.6,
-    "NanoNQ_cosine_recall@10": 0.68,
-    "NanoNQ_cosine_ndcg@10": 0.48789548101925573,
-    "NanoNQ_cosine_mrr@10": 0.43224603174603166,
-    "NanoNQ_cosine_map@100": 0.4304090341200182,
-    "NanoQuoraRetrieval_cosine_accuracy@1": 0.96,
+    "NanoNQ_cosine_accuracy@10": 0.68,
+    "NanoNQ_cosine_precision@1": 0.34,
+    "NanoNQ_cosine_precision@3": 0.19333333333333333,
+    "NanoNQ_cosine_precision@5": 0.128,
+    "NanoNQ_cosine_precision@10": 0.07200000000000001,
+    "NanoNQ_cosine_recall@1": 0.31,
+    "NanoNQ_cosine_recall@3": 0.53,
+    "NanoNQ_cosine_recall@5": 0.59,
+    "NanoNQ_cosine_recall@10": 0.66,
+    "NanoNQ_cosine_ndcg@10": 0.4981843146804683,
+    "NanoNQ_cosine_mrr@10": 0.46341269841269844,
+    "NanoNQ_cosine_map@100": 0.4486372793651192,
+    "NanoQuoraRetrieval_cosine_accuracy@1": 0.94,
     "NanoQuoraRetrieval_cosine_accuracy@3": 0.98,
     "NanoQuoraRetrieval_cosine_accuracy@5": 1.0,
     "NanoQuoraRetrieval_cosine_accuracy@10": 1.0,
-    "NanoQuoraRetrieval_cosine_precision@1": 0.96,
+    "NanoQuoraRetrieval_cosine_precision@1": 0.94,
     "NanoQuoraRetrieval_cosine_precision@3": 0.4133333333333333,
     "NanoQuoraRetrieval_cosine_precision@5": 0.264,
     "NanoQuoraRetrieval_cosine_precision@10": 0.13999999999999999,
-    "NanoQuoraRetrieval_cosine_recall@1": 0.8473333333333334,
+    "NanoQuoraRetrieval_cosine_recall@1": 0.8273333333333334,
     "NanoQuoraRetrieval_cosine_recall@3": 0.9520000000000001,
     "NanoQuoraRetrieval_cosine_recall@5": 0.986,
     "NanoQuoraRetrieval_cosine_recall@10": 1.0,
-    "NanoQuoraRetrieval_cosine_ndcg@10": 0.9790267083021519,
-    "NanoQuoraRetrieval_cosine_mrr@10": 0.975,
-    "NanoQuoraRetrieval_cosine_map@100": 0.9683809523809523,
-    "NanoSCIDOCS_cosine_accuracy@1": 0.46,
-    "NanoSCIDOCS_cosine_accuracy@3": 0.66,
-    "NanoSCIDOCS_cosine_accuracy@5": 0.76,
-    "NanoSCIDOCS_cosine_accuracy@10": 0.82,
-    "NanoSCIDOCS_cosine_precision@1": 0.46,
-    "NanoSCIDOCS_cosine_precision@3": 0.34,
-    "NanoSCIDOCS_cosine_precision@5": 0.27599999999999997,
-    "NanoSCIDOCS_cosine_precision@10": 0.19399999999999998,
-    "NanoSCIDOCS_cosine_recall@1": 0.09766666666666668,
-    "NanoSCIDOCS_cosine_recall@3": 0.21166666666666667,
-    "NanoSCIDOCS_cosine_recall@5": 0.2846666666666666,
-    "NanoSCIDOCS_cosine_recall@10": 0.3986666666666666,
-    "NanoSCIDOCS_cosine_ndcg@10": 0.38054185058113466,
-    "NanoSCIDOCS_cosine_mrr@10": 0.5684444444444444,
-    "NanoSCIDOCS_cosine_map@100": 0.29482712989551213,
+    "NanoQuoraRetrieval_cosine_ndcg@10": 0.9693718101249,
+    "NanoQuoraRetrieval_cosine_mrr@10": 0.9640000000000001,
+    "NanoQuoraRetrieval_cosine_map@100": 0.9546825396825398,
+    "NanoSCIDOCS_cosine_accuracy@1": 0.44,
+    "NanoSCIDOCS_cosine_accuracy@3": 0.72,
+    "NanoSCIDOCS_cosine_accuracy@5": 0.8,
+    "NanoSCIDOCS_cosine_accuracy@10": 0.9,
+    "NanoSCIDOCS_cosine_precision@1": 0.44,
+    "NanoSCIDOCS_cosine_precision@3": 0.36666666666666664,
+    "NanoSCIDOCS_cosine_precision@5": 0.284,
+    "NanoSCIDOCS_cosine_precision@10": 0.18799999999999997,
+    "NanoSCIDOCS_cosine_recall@1": 0.09266666666666666,
+    "NanoSCIDOCS_cosine_recall@3": 0.22666666666666666,
+    "NanoSCIDOCS_cosine_recall@5": 0.29166666666666663,
+    "NanoSCIDOCS_cosine_recall@10": 0.3856666666666666,
+    "NanoSCIDOCS_cosine_ndcg@10": 0.3848988062074523,
+    "NanoSCIDOCS_cosine_mrr@10": 0.5949444444444444,
+    "NanoSCIDOCS_cosine_map@100": 0.3093365232956528,
     "NanoArguAna_cosine_accuracy@1": 0.22,
-    "NanoArguAna_cosine_accuracy@3": 0.5,
-    "NanoArguAna_cosine_accuracy@5": 0.66,
-    "NanoArguAna_cosine_accuracy@10": 0.8,
+    "NanoArguAna_cosine_accuracy@3": 0.56,
+    "NanoArguAna_cosine_accuracy@5": 0.72,
+    "NanoArguAna_cosine_accuracy@10": 0.84,
     "NanoArguAna_cosine_precision@1": 0.22,
-    "NanoArguAna_cosine_precision@3": 0.16666666666666663,
-    "NanoArguAna_cosine_precision@5": 0.132,
-    "NanoArguAna_cosine_precision@10": 0.08,
+    "NanoArguAna_cosine_precision@3": 0.18666666666666668,
+    "NanoArguAna_cosine_precision@5": 0.14400000000000002,
+    "NanoArguAna_cosine_precision@10": 0.08399999999999999,
     "NanoArguAna_cosine_recall@1": 0.22,
-    "NanoArguAna_cosine_recall@3": 0.5,
-    "NanoArguAna_cosine_recall@5": 0.66,
-    "NanoArguAna_cosine_recall@10": 0.8,
-    "NanoArguAna_cosine_ndcg@10": 0.48983349748002636,
-    "NanoArguAna_cosine_mrr@10": 0.3926269841269841,
-    "NanoArguAna_cosine_map@100": 0.40347638549721887,
-    "NanoSciFact_cosine_accuracy@1": 0.42,
-    "NanoSciFact_cosine_accuracy@3": 0.64,
-    "NanoSciFact_cosine_accuracy@5": 0.72,
-    "NanoSciFact_cosine_accuracy@10": 0.78,
-    "NanoSciFact_cosine_precision@1": 0.42,
-    "NanoSciFact_cosine_precision@3": 0.2333333333333333,
-    "NanoSciFact_cosine_precision@5": 0.16,
-    "NanoSciFact_cosine_precision@10": 0.088,
-    "NanoSciFact_cosine_recall@1": 0.375,
-    "NanoSciFact_cosine_recall@3": 0.615,
-    "NanoSciFact_cosine_recall@5": 0.7,
-    "NanoSciFact_cosine_recall@10": 0.77,
-    "NanoSciFact_cosine_ndcg@10": 0.5949205162858369,
-    "NanoSciFact_cosine_mrr@10": 0.5500238095238096,
-    "NanoSciFact_cosine_map@100": 0.5379955882174889,
-    "NanoTouche2020_cosine_accuracy@1": 0.4897959183673469,
-    "NanoTouche2020_cosine_accuracy@3": 0.8367346938775511,
-    "NanoTouche2020_cosine_accuracy@5": 0.8775510204081632,
+    "NanoArguAna_cosine_recall@3": 0.56,
+    "NanoArguAna_cosine_recall@5": 0.72,
+    "NanoArguAna_cosine_recall@10": 0.84,
+    "NanoArguAna_cosine_ndcg@10": 0.5195582784941274,
+    "NanoArguAna_cosine_mrr@10": 0.417611111111111,
+    "NanoArguAna_cosine_map@100": 0.4242292757682783,
+    "NanoSciFact_cosine_accuracy@1": 0.66,
+    "NanoSciFact_cosine_accuracy@3": 0.78,
+    "NanoSciFact_cosine_accuracy@5": 0.78,
+    "NanoSciFact_cosine_accuracy@10": 0.8,
+    "NanoSciFact_cosine_precision@1": 0.66,
+    "NanoSciFact_cosine_precision@3": 0.28,
+    "NanoSciFact_cosine_precision@5": 0.17199999999999996,
+    "NanoSciFact_cosine_precision@10": 0.08999999999999998,
+    "NanoSciFact_cosine_recall@1": 0.615,
+    "NanoSciFact_cosine_recall@3": 0.76,
+    "NanoSciFact_cosine_recall@5": 0.765,
+    "NanoSciFact_cosine_recall@10": 0.79,
+    "NanoSciFact_cosine_ndcg@10": 0.733200404338037,
+    "NanoSciFact_cosine_mrr@10": 0.7228571428571428,
+    "NanoSciFact_cosine_map@100": 0.7167485636699191,
+    "NanoTouche2020_cosine_accuracy@1": 0.42857142857142855,
+    "NanoTouche2020_cosine_accuracy@3": 0.6938775510204082,
+    "NanoTouche2020_cosine_accuracy@5": 0.8571428571428571,
     "NanoTouche2020_cosine_accuracy@10": 0.9591836734693877,
-    "NanoTouche2020_cosine_precision@1": 0.4897959183673469,
-    "NanoTouche2020_cosine_precision@3": 0.46258503401360546,
-    "NanoTouche2020_cosine_precision@5": 0.4081632653061225,
-    "NanoTouche2020_cosine_precision@10": 0.35306122448979593,
-    "NanoTouche2020_cosine_recall@1": 0.0391261657646358,
-    "NanoTouche2020_cosine_recall@3": 0.09935212519786965,
-    "NanoTouche2020_cosine_recall@5": 0.14360455232476052,
-    "NanoTouche2020_cosine_recall@10": 0.23229499710875565,
-    "NanoTouche2020_cosine_ndcg@10": 0.39999686542243473,
-    "NanoTouche2020_cosine_mrr@10": 0.6578798185941044,
-    "NanoTouche2020_cosine_map@100": 0.28337269656997294,
-    "NanoBEIR_mean_cosine_accuracy@1": 0.4545996860282574,
-    "NanoBEIR_mean_cosine_accuracy@3": 0.6366718995290425,
-    "NanoBEIR_mean_cosine_accuracy@5": 0.7228885400313971,
-    "NanoBEIR_mean_cosine_accuracy@10": 0.8060910518053375,
-    "NanoBEIR_mean_cosine_precision@1": 0.4545996860282574,
-    "NanoBEIR_mean_cosine_precision@3": 0.2807116692830978,
-    "NanoBEIR_mean_cosine_precision@5": 0.2218587127158556,
-    "NanoBEIR_mean_cosine_precision@10": 0.1520816326530612,
-    "NanoBEIR_mean_cosine_recall@1": 0.2615469007664515,
-    "NanoBEIR_mean_cosine_recall@3": 0.40186584654388047,
-    "NanoBEIR_mean_cosine_recall@5": 0.4739396227677734,
-    "NanoBEIR_mean_cosine_recall@10": 0.5558179032632299,
-    "NanoBEIR_mean_cosine_ndcg@10": 0.49519347906125394,
-    "NanoBEIR_mean_cosine_mrr@10": 0.5629388627245769,
-    "NanoBEIR_mean_cosine_map@100": 0.41529341414013715
+    "NanoTouche2020_cosine_precision@1": 0.42857142857142855,
+    "NanoTouche2020_cosine_precision@3": 0.42176870748299317,
+    "NanoTouche2020_cosine_precision@5": 0.4244897959183673,
+    "NanoTouche2020_cosine_precision@10": 0.35918367346938773,
+    "NanoTouche2020_cosine_recall@1": 0.03138807979253311,
+    "NanoTouche2020_cosine_recall@3": 0.09060401943508274,
+    "NanoTouche2020_cosine_recall@5": 0.1535986032297028,
+    "NanoTouche2020_cosine_recall@10": 0.24399992715703397,
+    "NanoTouche2020_cosine_ndcg@10": 0.39516130369687524,
+    "NanoTouche2020_cosine_mrr@10": 0.6046161321671525,
+    "NanoTouche2020_cosine_map@100": 0.2987728168236368,
+    "NanoBEIR_mean_cosine_accuracy@1": 0.4914285714285715,
+    "NanoBEIR_mean_cosine_accuracy@3": 0.6718367346938775,
+    "NanoBEIR_mean_cosine_accuracy@5": 0.7428571428571428,
+    "NanoBEIR_mean_cosine_accuracy@10": 0.8245525902668761,
+    "NanoBEIR_mean_cosine_precision@1": 0.4914285714285715,
+    "NanoBEIR_mean_cosine_precision@3": 0.30013605442176866,
+    "NanoBEIR_mean_cosine_precision@5": 0.23019152276295135,
+    "NanoBEIR_mean_cosine_precision@10": 0.15824489795918367,
+    "NanoBEIR_mean_cosine_recall@1": 0.29101290570653116,
+    "NanoBEIR_mean_cosine_recall@3": 0.44409159126937303,
+    "NanoBEIR_mean_cosine_recall@5": 0.509873722010538,
+    "NanoBEIR_mean_cosine_recall@10": 0.5791424793259958,
+    "NanoBEIR_mean_cosine_ndcg@10": 0.5309109052428821,
+    "NanoBEIR_mean_cosine_mrr@10": 0.5974918392265332,
+    "NanoBEIR_mean_cosine_map@100": 0.45712865203428704
   },
   "beir_touche2020": {
-    "BeIR-touche2020-subset-test_cosine_accuracy@1": 0.7346938775510204,
-    "BeIR-touche2020-subset-test_cosine_accuracy@3": 0.8775510204081632,
-    "BeIR-touche2020-subset-test_cosine_accuracy@5": 0.9591836734693877,
-    "BeIR-touche2020-subset-test_cosine_accuracy@10": 0.9795918367346939,
-    "BeIR-touche2020-subset-test_cosine_precision@1": 0.7346938775510204,
-    "BeIR-touche2020-subset-test_cosine_precision@3": 0.6734693877551019,
-    "BeIR-touche2020-subset-test_cosine_precision@5": 0.6367346938775511,
-    "BeIR-touche2020-subset-test_cosine_precision@10": 0.5510204081632654,
-    "BeIR-touche2020-subset-test_cosine_recall@1": 0.01624309573792563,
-    "BeIR-touche2020-subset-test_cosine_recall@3": 0.04452074460941084,
-    "BeIR-touche2020-subset-test_cosine_recall@5": 0.07034165898860735,
-    "BeIR-touche2020-subset-test_cosine_recall@10": 0.12170213074025006,
-    "BeIR-touche2020-subset-test_cosine_ndcg@10": 0.5930193473989395,
-    "BeIR-touche2020-subset-test_cosine_mrr@10": 0.8187074829931973,
-    "BeIR-touche2020-subset-test_cosine_map@100": 0.2518399254976325
+    "BeIR-touche2020-subset-test_cosine_accuracy@1": 0.673469387755102,
+    "BeIR-touche2020-subset-test_cosine_accuracy@3": 0.8571428571428571,
+    "BeIR-touche2020-subset-test_cosine_accuracy@5": 0.9387755102040817,
+    "BeIR-touche2020-subset-test_cosine_accuracy@10": 0.9591836734693877,
+    "BeIR-touche2020-subset-test_cosine_precision@1": 0.673469387755102,
+    "BeIR-touche2020-subset-test_cosine_precision@3": 0.6258503401360543,
+    "BeIR-touche2020-subset-test_cosine_precision@5": 0.6122448979591837,
+    "BeIR-touche2020-subset-test_cosine_precision@10": 0.5816326530612245,
+    "BeIR-touche2020-subset-test_cosine_recall@1": 0.014854954185034354,
+    "BeIR-touche2020-subset-test_cosine_recall@3": 0.041323822718693934,
+    "BeIR-touche2020-subset-test_cosine_recall@5": 0.06744310800628797,
+    "BeIR-touche2020-subset-test_cosine_recall@10": 0.12790902925353234,
+    "BeIR-touche2020-subset-test_cosine_ndcg@10": 0.6000606062963675,
+    "BeIR-touche2020-subset-test_cosine_mrr@10": 0.7784256559766765,
+    "BeIR-touche2020-subset-test_cosine_map@100": 0.2654061071722756
   }
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e8047d77ced88713240245a68d96f88c650644e287418c20b245e56ea70742aa
-size 133462128
+oid sha256:8c96a038f541608358be6a34ac513d209f898592c738d9f2b3943b60c76359f3
+size 596070136

modules.json

@@ -10,11 +10,5 @@
     "name": "1",
     "path": "1_Pooling",
     "type": "sentence_transformers.models.Pooling"
-  },
-  {
-    "idx": 2,
-    "name": "2",
-    "path": "2_Normalize",
-    "type": "sentence_transformers.models.Normalize"
   }
 ]

special_tokens_map.json

@@ -8,7 +8,7 @@
   },
   "mask_token": {
     "content": "[MASK]",
-    "lstrip": false,
+    "lstrip": true,
     "normalized": false,
     "rstrip": false,
     "single_word": false

tokenizer.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2fc687b11de0bc1b3d8348f92e3b49ef1089a621506c7661fbf3248fcd54947e
-size 711649
+oid sha256:218484396a9d08293b108b0e5ea31e0a1b7c801dcbed35a821deb392d2fe9bb4
+size 3583485

tokenizer_config.json

@@ -1,14 +1,230 @@
 {
   "added_tokens_decoder": {
     "0": {
-      "content": "[PAD]",
+      "content": "|||IP_ADDRESS|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "1": {
+      "content": "<|padding|>",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
     },
-    "100": {
+    "50254": {
+      "content": "                        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50255": {
+      "content": "                       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50256": {
+      "content": "                      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50257": {
+      "content": "                     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50258": {
+      "content": "                    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50259": {
+      "content": "                   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50260": {
+      "content": "                  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50261": {
+      "content": "                 ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50262": {
+      "content": "                ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50263": {
+      "content": "               ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50264": {
+      "content": "              ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50265": {
+      "content": "             ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50266": {
+      "content": "            ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50267": {
+      "content": "           ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50268": {
+      "content": "          ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50269": {
+      "content": "         ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50270": {
+      "content": "        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50271": {
+      "content": "       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50272": {
+      "content": "      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50273": {
+      "content": "     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50274": {
+      "content": "    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50275": {
+      "content": "   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50276": {
+      "content": "  ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50277": {
+      "content": "|||EMAIL_ADDRESS|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50278": {
+      "content": "|||PHONE_NUMBER|||",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50279": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50280": {
       "content": "[UNK]",
       "lstrip": false,
       "normalized": false,
@@ -16,7 +232,7 @@
       "single_word": false,
       "special": true
     },
-    "101": {
+    "50281": {
       "content": "[CLS]",
       "lstrip": false,
       "normalized": false,
@@ -24,7 +240,7 @@
       "single_word": false,
       "special": true
     },
-    "102": {
+    "50282": {
       "content": "[SEP]",
       "lstrip": false,
       "normalized": false,
@@ -32,34 +248,698 @@
       "single_word": false,
       "special": true
     },
-    "103": {
-      "content": "[MASK]",
+    "50283": {
+      "content": "[PAD]",
       "lstrip": false,
       "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
+    },
+    "50284": {
+      "content": "[MASK]",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50285": {
+      "content": "[unused0]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50286": {
+      "content": "[unused1]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50287": {
+      "content": "[unused2]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50288": {
+      "content": "[unused3]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50289": {
+      "content": "[unused4]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50290": {
+      "content": "[unused5]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50291": {
+      "content": "[unused6]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50292": {
+      "content": "[unused7]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50293": {
+      "content": "[unused8]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50294": {
+      "content": "[unused9]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50295": {
+      "content": "[unused10]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50296": {
+      "content": "[unused11]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50297": {
+      "content": "[unused12]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50298": {
+      "content": "[unused13]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50299": {
+      "content": "[unused14]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50300": {
+      "content": "[unused15]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50301": {
+      "content": "[unused16]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50302": {
+      "content": "[unused17]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50303": {
+      "content": "[unused18]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50304": {
+      "content": "[unused19]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50305": {
+      "content": "[unused20]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50306": {
+      "content": "[unused21]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50307": {
+      "content": "[unused22]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50308": {
+      "content": "[unused23]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50309": {
+      "content": "[unused24]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50310": {
+      "content": "[unused25]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50311": {
+      "content": "[unused26]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50312": {
+      "content": "[unused27]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50313": {
+      "content": "[unused28]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50314": {
+      "content": "[unused29]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50315": {
+      "content": "[unused30]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50316": {
+      "content": "[unused31]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50317": {
+      "content": "[unused32]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50318": {
+      "content": "[unused33]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50319": {
+      "content": "[unused34]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50320": {
+      "content": "[unused35]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50321": {
+      "content": "[unused36]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50322": {
+      "content": "[unused37]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50323": {
+      "content": "[unused38]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50324": {
+      "content": "[unused39]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50325": {
+      "content": "[unused40]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50326": {
+      "content": "[unused41]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50327": {
+      "content": "[unused42]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50328": {
+      "content": "[unused43]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50329": {
+      "content": "[unused44]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50330": {
+      "content": "[unused45]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50331": {
+      "content": "[unused46]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50332": {
+      "content": "[unused47]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50333": {
+      "content": "[unused48]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50334": {
+      "content": "[unused49]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50335": {
+      "content": "[unused50]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50336": {
+      "content": "[unused51]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50337": {
+      "content": "[unused52]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50338": {
+      "content": "[unused53]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50339": {
+      "content": "[unused54]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50340": {
+      "content": "[unused55]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50341": {
+      "content": "[unused56]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50342": {
+      "content": "[unused57]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50343": {
+      "content": "[unused58]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50344": {
+      "content": "[unused59]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50345": {
+      "content": "[unused60]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50346": {
+      "content": "[unused61]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50347": {
+      "content": "[unused62]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50348": {
+      "content": "[unused63]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50349": {
+      "content": "[unused64]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50350": {
+      "content": "[unused65]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50351": {
+      "content": "[unused66]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50352": {
+      "content": "[unused67]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50353": {
+      "content": "[unused68]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50354": {
+      "content": "[unused69]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50355": {
+      "content": "[unused70]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50356": {
+      "content": "[unused71]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50357": {
+      "content": "[unused72]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50358": {
+      "content": "[unused73]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50359": {
+      "content": "[unused74]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50360": {
+      "content": "[unused75]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50361": {
+      "content": "[unused76]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50362": {
+      "content": "[unused77]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50363": {
+      "content": "[unused78]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50364": {
+      "content": "[unused79]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50365": {
+      "content": "[unused80]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50366": {
+      "content": "[unused81]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50367": {
+      "content": "[unused82]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
     }
   },
   "clean_up_tokenization_spaces": true,
   "cls_token": "[CLS]",
-  "do_basic_tokenize": true,
-  "do_lower_case": true,
   "extra_special_tokens": {},
   "mask_token": "[MASK]",
-  "max_length": 128,
-  "model_max_length": 512,
-  "never_split": null,
-  "pad_to_multiple_of": null,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
   "pad_token": "[PAD]",
-  "pad_token_type_id": 0,
-  "padding_side": "right",
   "sep_token": "[SEP]",
-  "stride": 0,
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
+  "tokenizer_class": "PreTrainedTokenizerFast",
   "unk_token": "[UNK]"
 }