Training in progress, step 3375

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.16,0.34,0.42,0.5,0.16,0.07166666666666666,0.12,0.18833333333333332,0.08800000000000001,0.22166666666666668,0.05200000000000001,0.24566666666666667,0.26733333333333337,0.1944844277985001,0.15241233916622948
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Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.56,0.64,0.72,0.82,0.56,0.28,0.23333333333333336,0.35,0.15600000000000003,0.39,0.09799999999999999,0.49,0.6222380952380951,0.4508488327340793,0.3626886794313907
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 -1,-1,0.64,0.8,0.82,0.86,0.64,0.32,0.3066666666666667,0.46,0.19999999999999996,0.5,0.10999999999999999,0.55,0.7215555555555556,0.5358878886491945,0.45765435050351483
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Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.36,0.56,0.62,0.7,0.36,0.36,0.18666666666666668,0.56,0.124,0.62,0.07,0.7,0.47305555555555556,0.527653146668366,0.48547129622714985
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Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.28,0.4,0.46,0.52,0.28,0.01036110624216468,0.28,0.028442351530705748,0.26,0.04337383907822003,0.22,0.0837990261148129,0.352047619047619,0.24116587806524095,0.08689693851931407
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Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.42,0.56,0.58,0.7,0.42,0.39,0.19333333333333333,0.53,0.12,0.55,0.07200000000000001,0.66,0.504968253968254,0.5277599639847894,0.4914701525341206
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Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.84,0.9,0.94,0.96,0.84,0.7173333333333332,0.3666666666666666,0.8553333333333333,0.244,0.9159999999999999,0.12999999999999998,0.9493333333333334,0.8805000000000001,0.885075805951598,0.860204365079365
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.44,0.66,0.72,0.8,0.44,0.08900000000000001,0.3133333333333333,0.19466666666666665,0.24799999999999997,0.2546666666666667,0.176,0.36066666666666664,0.5596031746031747,0.3500919864968737,0.265638928993131
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Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.32,0.46,0.54,0.6,0.32,0.285,0.16666666666666663,0.425,0.12,0.51,0.068,0.58,0.4111904761904762,0.44050270090049004,0.3979953927287097
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.6938775510204082,0.8367346938775511,0.8775510204081632,0.9591836734693877,0.6938775510204082,0.047703239867479694,0.5646258503401359,0.12270801802207613,0.5102040816326531,0.18312719718047293,0.39999999999999997,0.27070672617961283,0.7761904761904761,0.47320998886606885,0.34568014855280155
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NanoBEIR_evaluation_mean_results.csv

@@ -25,3 +25,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.4318367346938776,0.5874411302982732,0.6551962323390895,0.7399372056514915,0.4318367346938776,0.23190735730811163,0.2639455782312925,0.3552536688782658,0.2060156985871272,0.413608543025798,0.1449230769230769,0.49880360995700285,0.5265299145299145,0.4511005221439489,0.3717355699535433
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config.json

@@ -15,7 +15,7 @@
   "max_position_embeddings": 512,
   "model_type": "bert",
   "num_attention_heads": 12,
-  "num_hidden_layers": 6,
+  "num_hidden_layers": 12,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "transformers_version": "4.57.3",

eval/Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -301,3 +301,17 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 5.172413793103448,4500,0.3,0.48,0.54,0.72,0.3,0.3,0.15999999999999998,0.48,0.10800000000000001,0.54,0.07200000000000001,0.72,0.41860317460317453,0.489037897592066,0.4276088975952252
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eval/Information-Retrieval_evaluation_NanoNQ_results.csv

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eval/NanoBEIR_evaluation_mean_results.csv

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final_metrics.json

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tokenizer_config.json

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