Training in progress, step 500

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

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Information-Retrieval_evaluation_NanoArguAna_results.csv

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

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

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

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

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

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

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

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

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

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

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

@@ -4,3 +4,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

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

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

@@ -1,25 +1,45 @@
 {
   "architectures": [
-    "BertModel"
+    "ModernBertModel"
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-  "attention_probs_dropout_prob": 0.1,
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+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
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   "num_attention_heads": 12,
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-  "pad_token_id": 0,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
   "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
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   "transformers_version": "4.57.3",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
+  "vocab_size": 50368
 }

eval/Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -63,3 +63,6 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 0.5747126436781609,500,0.3,0.54,0.68,0.78,0.3,0.3,0.18,0.54,0.136,0.68,0.078,0.78,0.45233333333333337,0.5317394349114126,0.4614151250010316
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eval/Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -63,3 +63,6 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 0.5747126436781609,500,0.42,0.64,0.68,0.76,0.42,0.41,0.21333333333333332,0.6,0.14400000000000002,0.66,0.08199999999999999,0.74,0.528079365079365,0.5724316751635009,0.5203088825379404
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eval/NanoBEIR_evaluation_mean_results.csv

@@ -63,3 +63,6 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 0.5747126436781609,500,0.36,0.5900000000000001,0.68,0.77,0.36,0.355,0.19666666666666666,0.5700000000000001,0.14,0.67,0.07999999999999999,0.76,0.4902063492063492,0.5520855550374568,0.49086200376948597
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final_metrics.json

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