Training in progress, step 2250

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.64,0.86,0.88,0.94,0.64,0.055166835143185204,0.56,0.13163725118575806,0.46,0.16309283546277217,0.37000000000000005,0.255052017593493,0.7482460317460319,0.4734084050500165,0.3024986653520944
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Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.62,0.9,0.92,0.92,0.62,0.5766666666666667,0.3,0.8366666666666667,0.18799999999999997,0.8666666666666667,0.09599999999999997,0.8733333333333333,0.745,0.7556112023709758,0.7055591841543971
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Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.6,0.7,0.8,0.84,0.6,0.3,0.28,0.42,0.19199999999999995,0.48,0.10799999999999998,0.54,0.6709999999999999,0.5110926227018934,0.44247510718231914
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Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.36,0.5,0.58,0.7,0.36,0.36,0.16666666666666669,0.5,0.11599999999999999,0.58,0.07,0.7,0.46210317460317457,0.518447520928203,0.47571343305416797
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Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.34,0.44,0.5,0.64,0.34,0.011223359167909725,0.29333333333333333,0.029511381922936213,0.284,0.053948334836425464,0.258,0.10731809178150171,0.4215714285714285,0.2824722349410338,0.1001988763213856
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Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.4,0.54,0.56,0.64,0.4,0.36,0.2,0.53,0.12400000000000003,0.55,0.07,0.63,0.48146825396825393,0.5099996252572562,0.48289978745216106
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+-1,-1,0.42,0.56,0.6,0.74,0.42,0.39,0.2,0.54,0.132,0.59,0.08,0.72,0.5070793650793651,0.5505596490405809,0.50321316661942

Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.86,0.94,0.96,0.98,0.86,0.7473333333333333,0.38666666666666655,0.8986666666666667,0.25199999999999995,0.9426666666666667,0.132,0.9693333333333334,0.9039999999999999,0.9104874488715865,0.8874039016539015
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.42,0.68,0.74,0.86,0.42,0.085,0.3399999999999999,0.21166666666666664,0.27599999999999997,0.2836666666666667,0.18800000000000006,0.3856666666666666,0.5761666666666668,0.37223534210874404,0.2915419714563916
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Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.38,0.54,0.64,0.72,0.38,0.345,0.2,0.52,0.14800000000000002,0.63,0.08399999999999999,0.72,0.48566666666666664,0.5367853689811245,0.4818779327708432
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.5510204081632653,0.7959183673469388,0.8775510204081632,0.9591836734693877,0.5510204081632653,0.04121377457786964,0.5374149659863945,0.11859614326826039,0.4775510204081632,0.17207686489962143,0.4061224489795919,0.2715620045902937,0.6986961451247167,0.4600561591051081,0.34520928442813403
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NanoBEIR_evaluation_mean_results.csv

@@ -16,3 +16,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.46854003139717426,0.6381475667189952,0.7105808477237048,0.7922448979591838,0.46854003139717426,0.26653974363858945,0.29108320251177394,0.39900112443436897,0.22535007849293567,0.4664400930616918,0.15647095761381477,0.5399233494259013,0.5727446363160649,0.4985169450713051,0.4199182290063064
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+-1,-1,0.4916169544740973,0.6596546310832024,0.7475353218210361,0.8276295133437992,0.4916169544740973,0.2820220284146258,0.304887493458922,0.42621929560658156,0.23767660910518057,0.5002767984689632,0.16524018838304555,0.5769488092013326,0.5990572998430141,0.5295630814194167,0.44769393862699014

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

@@ -201,3 +201,13 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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final_metrics.json

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