Training in progress, step 500

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -19,3 +19,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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NanoBEIR_evaluation_mean_results.csv

@@ -19,3 +19,4 @@ 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",
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+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
   "dtype": "float32",
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-  "hidden_act": "gelu",
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-  "intermediate_size": 1536,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
+  "intermediate_size": 1152,
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+  "max_position_embeddings": 8192,
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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

@@ -242,3 +242,6 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 1.422475106685633,1000,0.36,0.58,0.66,0.76,0.36,0.36,0.19333333333333333,0.58,0.132,0.66,0.07600000000000001,0.76,0.48749999999999993,0.5528646729879835,0.49795590132761597
 1.7780938833570412,1250,0.34,0.58,0.64,0.72,0.34,0.34,0.19333333333333333,0.58,0.128,0.64,0.07200000000000001,0.72,0.4674999999999999,0.5287566904972468,0.48136252648416433
 2.1337126600284497,1500,0.34,0.56,0.64,0.74,0.34,0.34,0.18666666666666668,0.56,0.128,0.64,0.07400000000000001,0.74,0.47276190476190466,0.5374407455925777,0.48561801675925215
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+0.35561877667140823,250,0.5,0.68,0.76,0.84,0.5,0.5,0.22666666666666668,0.68,0.15200000000000002,0.76,0.08399999999999999,0.84,0.603079365079365,0.6601111089623012,0.6104568724713685
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eval/Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -242,3 +242,6 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 1.422475106685633,1000,0.42,0.52,0.58,0.66,0.42,0.39,0.18666666666666665,0.5,0.124,0.56,0.07200000000000001,0.64,0.4852777777777777,0.5129550595376149,0.4778814511409428
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eval/NanoBEIR_evaluation_mean_results.csv

@@ -242,3 +242,6 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 1.422475106685633,1000,0.39,0.55,0.62,0.71,0.39,0.375,0.19,0.54,0.128,0.6100000000000001,0.07400000000000001,0.7,0.4863888888888888,0.5329098662627991,0.4879186762342794
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final_metrics.json

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