Training in progress, step 5062

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.2,0.36,0.48,0.64,0.2,0.2,0.11999999999999998,0.36,0.09600000000000002,0.48,0.06400000000000002,0.64,0.3259920634920635,0.399971817141543,0.33708074446236025
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.22,0.32,0.42,0.54,0.22,0.10233333333333333,0.11333333333333333,0.17233333333333334,0.08800000000000001,0.214,0.06,0.264,0.2971587301587302,0.2160448650908654,0.16726239030401754
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Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.52,0.8,0.84,0.94,0.52,0.053947284988985926,0.4733333333333334,0.12872420563450496,0.4,0.1614012024961392,0.33399999999999996,0.22961144710899753,0.658079365079365,0.4215257975939991,0.25522727699994674
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Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.4,0.66,0.74,0.86,0.4,0.37666666666666665,0.22666666666666668,0.6133333333333333,0.156,0.6933333333333332,0.09,0.8133333333333332,0.551190476190476,0.60296133344697,0.5334152795270443
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+-1,-1,0.48,0.82,0.92,0.96,0.48,0.45666666666666667,0.2733333333333333,0.7666666666666666,0.184,0.8566666666666667,0.09799999999999998,0.9066666666666667,0.6655,0.7068297646021358,0.6300429505135388

Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.26,0.4,0.54,0.6,0.26,0.16085714285714287,0.14666666666666667,0.23674603174603173,0.124,0.32276984126984126,0.07400000000000001,0.4006269841269841,0.36907936507936495,0.30934647962611306,0.2520414233453418
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+-1,-1,0.3,0.44,0.52,0.64,0.3,0.19085714285714286,0.1733333333333333,0.2721031746031746,0.14800000000000002,0.3444603174603174,0.088,0.4182936507936508,0.4016031746031746,0.3450848887082074,0.2890649644684096

Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.52,0.62,0.64,0.68,0.52,0.26,0.23333333333333336,0.35,0.14800000000000002,0.37,0.08,0.4,0.5784999999999999,0.4083907397898535,0.34877240523636466
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 -1,-1,0.62,0.74,0.82,0.9,0.62,0.31,0.2866666666666667,0.43,0.204,0.51,0.11599999999999998,0.58,0.7044444444444445,0.534686691533984,0.443534200936931
+-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

Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.3,0.5,0.58,0.7,0.3,0.3,0.16666666666666669,0.5,0.11599999999999999,0.58,0.07,0.7,0.4282222222222222,0.49319518919591504,0.44089915437302396
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+-1,-1,0.4,0.5,0.58,0.7,0.4,0.4,0.16666666666666669,0.5,0.11600000000000002,0.58,0.07,0.7,0.4768809523809523,0.5288863274905288,0.4889131039012955

Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.36,0.48,0.5,0.62,0.36,0.012173359206591558,0.3,0.028722229702908417,0.284,0.04538392607412603,0.23399999999999999,0.0669521595133137,0.43173809523809525,0.2590743459873399,0.08094835917197184
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Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.26,0.38,0.48,0.54,0.26,0.23,0.13333333333333333,0.35,0.1,0.45,0.05600000000000001,0.51,0.34907936507936504,0.3731343310366586,0.33487673362538833
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+-1,-1,0.48,0.56,0.64,0.74,0.48,0.45,0.18666666666666665,0.52,0.132,0.61,0.078,0.7,0.5437222222222222,0.5665139206257088,0.5291425906394946

Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.8,0.9,0.92,0.96,0.8,0.7006666666666667,0.3733333333333333,0.862,0.23199999999999996,0.8886666666666667,0.12799999999999997,0.946,0.8575,0.8709345901262452,0.8417246177735308
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.44,0.62,0.7,0.8,0.44,0.09,0.2866666666666667,0.175,0.21999999999999997,0.22399999999999998,0.16400000000000003,0.3356666666666667,0.5561031746031745,0.32838811799960327,0.24434998843859707
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+-1,-1,0.42,0.68,0.74,0.88,0.42,0.086,0.33333333333333337,0.20466666666666666,0.276,0.2806666666666667,0.18599999999999997,0.3796666666666666,0.5601507936507936,0.36185978568636756,0.27304447285762495

Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.3,0.5,0.52,0.62,0.3,0.265,0.1733333333333333,0.46,0.11600000000000002,0.495,0.07,0.6,0.4002698412698413,0.43558334687061145,0.3861669168464833
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.6326530612244898,0.8163265306122449,0.8979591836734694,0.9591836734693877,0.6326530612244898,0.04499413135420742,0.4965986394557823,0.1065943935391371,0.46938775510204084,0.16649967473172042,0.37142857142857144,0.24826882087723884,0.7421768707482992,0.43458124412524113,0.31599660374216376
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NanoBEIR_evaluation_mean_results.csv

@@ -24,3 +24,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.40097331240188383,0.5658712715855574,0.6352276295133438,0.7276295133437991,0.40097331240188383,0.21512604500566113,0.24948194662480377,0.33411180979148064,0.19610675039246467,0.39161958804398667,0.1381098901098901,0.4734199547405025,0.5034684283969997,0.4271640152331507,0.3491355302958642
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config.json

@@ -5,6 +5,7 @@
   "attention_probs_dropout_prob": 0.1,
   "classifier_dropout": null,
   "dtype": "float32",
+  "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 384,
@@ -14,7 +15,7 @@
   "max_position_embeddings": 512,
   "model_type": "bert",
   "num_attention_heads": 12,
-  "num_hidden_layers": 12,
+  "num_hidden_layers": 6,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "transformers_version": "4.57.3",

eval/Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -280,3 +280,24 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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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

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