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,-1,0.64,0.74,0.76,0.8,0.64,0.5539999999999999,0.2866666666666667,0.6753333333333333,0.19199999999999995,0.716,0.10599999999999998,0.7793333333333333,0.6965,0.7012017830718481,0.6704263695106392
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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,-1,0.22,0.4,0.52,0.66,0.22,0.044000000000000004,0.15999999999999998,0.098,0.14,0.14566666666666667,0.10400000000000001,0.21466666666666664,0.34705555555555556,0.1967244058819425,0.14605831068833375
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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,-1,0.04,0.08,0.12,0.2,0.04,0.04,0.026666666666666665,0.065,0.024000000000000004,0.095,0.022000000000000002,0.18,0.07412698412698412,0.09642182769273433,0.07900009439847851
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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,-1,0.04081632653061224,0.12244897959183673,0.16326530612244897,0.2653061224489796,0.04081632653061224,0.0018299717547837849,0.04081632653061224,0.005780782848452021,0.036734693877551024,0.00873800969523469,0.028571428571428574,0.01428957020369175,0.09631519274376418,0.03171521068958811,0.012294180268056192
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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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