Training in progress, step 703

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -27,3 +27,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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NanoBEIR_evaluation_mean_results.csv

@@ -27,3 +27,4 @@ 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,
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
   "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
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   "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 384,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
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-  "intermediate_size": 1536,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
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 }

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final_metrics.json

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