Training in progress, step 1125

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

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Information-Retrieval_evaluation_NanoArguAna_results.csv

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

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

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

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

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

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

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

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

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

@@ -3,3 +3,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.62,0.88,0.9,0.92,0.62,0.5373333333333333,0.3533333333333333,0.8353333333333333,0.23999999999999996,0.8733333333333333,0.12599999999999997,0.9066666666666666,0.7438888888888888,0.781903375113035,0.7386325337416886
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -3,3 +3,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.26,0.36,0.48,0.64,0.26,0.052000000000000005,0.12666666666666665,0.07600000000000001,0.128,0.131,0.1,0.204,0.3489126984126984,0.18798354592069366,0.1379120212807064
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Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -3,3 +3,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.2,0.3,0.34,0.38,0.2,0.175,0.10666666666666666,0.285,0.08,0.325,0.046000000000000006,0.37,0.2505,0.2737205402887792,0.24776085289830674
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -3,3 +3,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.5102040816326531,0.6938775510204082,0.8367346938775511,0.9183673469387755,0.5102040816326531,0.03843268640196565,0.4081632653061224,0.08879738447702933,0.39183673469387753,0.14115407637278626,0.32653061224489793,0.21644573734426228,0.6354227405247813,0.37353019419538835,0.2727638999098376
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NanoBEIR_evaluation_mean_results.csv

@@ -3,3 +3,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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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

@@ -58,3 +58,8 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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eval/Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -58,3 +58,8 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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eval/NanoBEIR_evaluation_mean_results.csv

@@ -58,3 +58,8 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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final_metrics.json

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