Training in progress, step 1687

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

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Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.58,0.82,0.84,0.88,0.58,0.5206666666666666,0.32,0.7519999999999999,0.204,0.7826666666666666,0.11199999999999999,0.8426666666666668,0.6984444444444443,0.7234901243687714,0.6843854607082449
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.16,0.38,0.42,0.6,0.16,0.035666666666666666,0.15333333333333332,0.09766666666666665,0.11600000000000002,0.12166666666666667,0.09799999999999999,0.20166666666666663,0.29457142857142854,0.17783402811268673,0.1311048840899592
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Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.02,0.04,0.06,0.12,0.02,0.02,0.013333333333333332,0.025,0.012000000000000002,0.045,0.013999999999999999,0.09,0.03844444444444445,0.04832365289646013,0.048732825667632367
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.10204081632653061,0.1836734693877551,0.3469387755102041,0.4897959183673469,0.10204081632653061,0.004037585569540457,0.06802721088435373,0.009683620207171733,0.08163265306122448,0.01988219552025967,0.0653061224489796,0.0381138118321127,0.18743926141885323,0.07108495919268427,0.02810759948511094
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NanoBEIR_evaluation_mean_results.csv

@@ -2,3 +2,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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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

@@ -51,3 +51,10 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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eval/Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -51,3 +51,10 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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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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