Training in progress, step 3000

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.32,0.52,0.58,0.7,0.32,0.32,0.1733333333333333,0.52,0.11599999999999999,0.58,0.07,0.7,0.43693650793650785,0.49929515877847647,0.45205401276376506
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Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.38,0.5,0.6,0.72,0.38,0.013857792729417237,0.3133333333333333,0.053976395496446575,0.3,0.08888025754448707,0.28600000000000003,0.12308809405420085,0.4709365079365079,0.3118912881405269,0.11679455794345621
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Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.4,0.6,0.6,0.64,0.4,0.38,0.21333333333333332,0.58,0.128,0.58,0.07,0.63,0.49449999999999994,0.526038841299356,0.5043233378343615
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Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.84,0.94,0.98,0.98,0.84,0.7440000000000001,0.38666666666666655,0.8986666666666667,0.25199999999999995,0.956,0.13399999999999998,0.9733333333333334,0.8980000000000001,0.9131522859528636,0.8904040404040404
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.44,0.78,0.82,0.84,0.44,0.09266666666666666,0.4066666666666666,0.25266666666666665,0.31199999999999994,0.32166666666666666,0.2,0.4096666666666667,0.6133333333333334,0.40673423073840587,0.33110095208367585
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Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.4,0.58,0.72,0.78,0.4,0.365,0.21333333333333335,0.56,0.16399999999999998,0.71,0.08799999999999997,0.77,0.5201904761904761,0.573675179771537,0.5151451047621822
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.5102040816326531,0.8163265306122449,0.9183673469387755,0.9387755102040817,0.5102040816326531,0.03897252093355478,0.5306122448979592,0.11617830784156352,0.4857142857142858,0.16808387137068914,0.40204081632653066,0.2667952262251305,0.6756802721088435,0.4535107828147836,0.3458940792300294
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NanoBEIR_evaluation_mean_results.csv

@@ -8,3 +8,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.4730926216640503,0.670486656200942,0.7491051805337521,0.8152904238618525,0.4730926216640503,0.2690472516492284,0.3018419675562533,0.4240880282187189,0.23459340659340663,0.4888912761073963,0.16354160125588696,0.5635832428071486,0.5874912785627071,0.5159778895838031,0.43492691137404127
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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

@@ -137,3 +137,16 @@ 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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