Training in progress, step 562

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.24,0.44,0.66,0.8,0.24,0.24,0.14666666666666664,0.44,0.132,0.66,0.08,0.8,0.39463492063492056,0.4902991099751076,0.4003835599098757
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.64,0.86,0.9,0.94,0.64,0.06357468346770019,0.5599999999999999,0.13735963585112515,0.4720000000000001,0.166831656051438,0.37999999999999995,0.26420780996847976,0.7535555555555556,0.48911555700232834,0.3157231020691947
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Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.68,0.86,0.94,0.96,0.68,0.6266666666666666,0.2866666666666666,0.8066666666666665,0.19599999999999998,0.8933333333333333,0.09999999999999998,0.9133333333333333,0.7823333333333333,0.7907698101544155,0.7371080182072829
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Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.58,0.7,0.8,0.86,0.58,0.29,0.27999999999999997,0.42,0.192,0.48,0.10599999999999998,0.53,0.658079365079365,0.49885278401211314,0.4235954555015261
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+-1,-1,0.72,0.82,0.86,0.9,0.72,0.36,0.3933333333333333,0.59,0.252,0.63,0.13399999999999998,0.67,0.7801666666666668,0.646633458142901,0.5837268808977187

Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.32,0.54,0.62,0.72,0.32,0.32,0.18,0.54,0.124,0.62,0.07200000000000001,0.72,0.44588095238095227,0.5113600890173079,0.46015815095025
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Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.38,0.48,0.56,0.7,0.38,0.012693306422073789,0.3066666666666667,0.04214726266623383,0.296,0.060842489623681925,0.262,0.11303442021619761,0.4633809523809523,0.29532590300926675,0.10812887551074139
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Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.34,0.5,0.58,0.62,0.34,0.31,0.18,0.49,0.12400000000000003,0.56,0.068,0.61,0.44116666666666665,0.47606860320855016,0.4435242837774235
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Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.86,0.94,0.96,0.96,0.86,0.7473333333333332,0.38666666666666655,0.8986666666666667,0.25199999999999995,0.9426666666666667,0.132,0.9533333333333335,0.9016666666666666,0.9072932413699512,0.888297619047619
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.48,0.74,0.8,0.86,0.48,0.098,0.3666666666666666,0.22766666666666666,0.304,0.31166666666666665,0.19599999999999998,0.4016666666666666,0.6166666666666667,0.39641992547924204,0.31328986730997044
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+-1,-1,0.48,0.76,0.86,0.9,0.48,0.10066666666666667,0.38666666666666666,0.23866666666666667,0.308,0.31566666666666665,0.20199999999999996,0.41266666666666657,0.6355,0.4089221118591332,0.3233322684896943

Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.38,0.5,0.56,0.74,0.38,0.345,0.18,0.47,0.132,0.55,0.08599999999999998,0.74,0.47410317460317464,0.5305656106537925,0.47051873061317523
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.5510204081632653,0.8163265306122449,0.8979591836734694,0.9183673469387755,0.5510204081632653,0.04038091396109546,0.5442176870748298,0.11742651442897102,0.46938775510204084,0.16752011739210812,0.3897959183673469,0.2612054305395196,0.6896825396825397,0.44501341794656196,0.3370332763378243
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NanoBEIR_evaluation_mean_results.csv

@@ -13,3 +13,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.46546310832025123,0.6381789638932496,0.724458398744113,0.7983359497645213,0.46546310832025123,0.26177396940488984,0.28955520669806384,0.3944130708371414,0.2293375196232339,0.46910163439955477,0.15813814756671898,0.5479544598603472,0.5724340659340659,0.4994963826952332,0.417664151163137
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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

@@ -213,3 +213,6 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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eval/Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -213,3 +213,6 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 3.5561877667140824,2500,0.4,0.56,0.64,0.78,0.4,0.37,0.19333333333333333,0.54,0.14,0.63,0.08399999999999999,0.76,0.5076904761904761,0.5589912230825993,0.49755415101079564
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 4.2674253200568995,3000,0.4,0.56,0.64,0.76,0.4,0.37,0.19333333333333333,0.54,0.14,0.63,0.08199999999999999,0.74,0.505468253968254,0.5529706231693197,0.4971957129558554
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eval/NanoBEIR_evaluation_mean_results.csv

@@ -213,3 +213,6 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 3.5561877667140824,2500,0.39,0.56,0.66,0.75,0.39,0.375,0.19,0.55,0.138,0.655,0.078,0.74,0.5050119047619047,0.5574964915451918,0.5062805404722638
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final_metrics.json

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