Training in progress, step 500

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

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Information-Retrieval_evaluation_NanoArguAna_results.csv

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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

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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"
   ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
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+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
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+  "classifier_pooling": "mean",
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+  "decoder_bias": true,
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   "dtype": "float32",
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+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
   "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "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,
+  "sparse_prediction": false,
   "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
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 1.422475106685633,1000,0.34,0.54,0.64,0.78,0.34,0.34,0.18,0.54,0.128,0.64,0.07800000000000001,0.78,0.47073809523809523,0.5447080049645561,0.4806962957327628
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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
 0.7112375533428165,500,0.4,0.6,0.7,0.76,0.4,0.38,0.20666666666666667,0.58,0.14800000000000002,0.67,0.08,0.73,0.5193333333333332,0.5631398504518083,0.5124939989368681
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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
 0.7112375533428165,500,0.37,0.55,0.6799999999999999,0.79,0.37,0.36,0.18666666666666668,0.54,0.14,0.665,0.08099999999999999,0.7749999999999999,0.49445238095238087,0.5577233371647814,0.4945011992368348
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final_metrics.json

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