Training in progress, step 3000

Information-Retrieval_evaluation_BeIR-touche2020-subset-test_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoArguAna_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoClimateFEVER_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoDBPedia_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFEVER_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoFiQA2018_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoHotpotQA_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.34,0.56,0.68,0.76,0.34,0.34,0.18666666666666668,0.56,0.136,0.68,0.07600000000000001,0.76,0.4722222222222222,0.5416899912877863,0.48271862745098043
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Information-Retrieval_evaluation_NanoNFCorpus_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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Information-Retrieval_evaluation_NanoNQ_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.44,0.56,0.6,0.66,0.44,0.42,0.2,0.54,0.132,0.59,0.07200000000000001,0.65,0.5043571428571428,0.5366135966288905,0.5082834994230612
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Information-Retrieval_evaluation_NanoQuoraRetrieval_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.86,0.96,0.96,0.98,0.86,0.7473333333333332,0.4,0.9253333333333333,0.25199999999999995,0.9426666666666667,0.132,0.97,0.9133333333333334,0.9200725814533811,0.9009989259106906
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Information-Retrieval_evaluation_NanoSCIDOCS_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.42,0.74,0.78,0.84,0.42,0.08866666666666667,0.37999999999999995,0.23566666666666664,0.308,0.31766666666666665,0.206,0.42266666666666663,0.5818571428571429,0.40435823767573387,0.323977515434263
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Information-Retrieval_evaluation_NanoSciFact_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.44,0.62,0.68,0.76,0.44,0.405,0.23333333333333336,0.605,0.156,0.67,0.08599999999999998,0.75,0.5513809523809523,0.5979962793714768,0.5573177733530136
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Information-Retrieval_evaluation_NanoTouche2020_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.5306122448979592,0.8571428571428571,0.8979591836734694,0.9795918367346939,0.5306122448979592,0.039526113999152644,0.5510204081632653,0.11537854933328684,0.5102040816326532,0.17364885334764324,0.40204081632653066,0.26913088576893424,0.7014415289925494,0.4579555008551029,0.34517137650160645
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NanoBEIR_evaluation_mean_results.csv

@@ -10,3 +10,4 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
 -1,-1,0.4823547880690739,0.669010989010989,0.7413814756671899,0.8168916797488225,0.4823547880690739,0.27810226922930104,0.3059759288330717,0.4228096254762406,0.23863108320251178,0.49154870385803073,0.1618492935635793,0.5654237444317253,0.590928347163041,0.5201522210707754,0.44175014933476214
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config.json

@@ -15,7 +15,7 @@
   "max_position_embeddings": 512,
   "model_type": "bert",
   "num_attention_heads": 12,
-  "num_hidden_layers": 6,
+  "num_hidden_layers": 12,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "transformers_version": "4.57.3",

eval/Information-Retrieval_evaluation_NanoMSMARCO_results.csv

@@ -174,3 +174,16 @@ epoch,steps,cosine-Accuracy@1,cosine-Accuracy@3,cosine-Accuracy@5,cosine-Accurac
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eval/Information-Retrieval_evaluation_NanoNQ_results.csv

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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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 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0af2eedea8f246b94f5ad89cf972791b4ddb4c7f0b89f7a0df2cdf130a0bcb34
-size 90864192
+oid sha256:568fa63abf1d4f1af18673fc5030a2550c82276425dbe1bb8d6d8a29c5bc6c47
+size 133462128

tokenizer_config.json

@@ -48,7 +48,7 @@
   "extra_special_tokens": {},
   "mask_token": "[MASK]",
   "max_length": 128,
-  "model_max_length": 256,
+  "model_max_length": 128,
   "never_split": null,
   "pad_to_multiple_of": null,
   "pad_token": "[PAD]",