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CocoRoF
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KMB_SimCSE_test

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modernbert
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@@ -16,15 +16,15 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [x2bee/KoModernBERT-base-mlm-v03-retry-ckp02](https://huggingface.co/x2bee/KoModernBERT-base-mlm-v03-retry-ckp02) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0463
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- - Pearson Cosine: 0.8264
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- - Spearman Cosine: 0.8281
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- - Pearson Manhattan: 0.8304
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- - Spearman Manhattan: 0.8368
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- - Pearson Euclidean: 0.8298
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- - Spearman Euclidean: 0.8363
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- - Pearson Dot: 0.7651
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- - Spearman Dot: 0.7620
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  ## Model description
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@@ -53,33 +53,54 @@ The following hyperparameters were used during training:
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 1.0
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Pearson Cosine | Spearman Cosine | Pearson Manhattan | Spearman Manhattan | Pearson Euclidean | Spearman Euclidean | Pearson Dot | Spearman Dot |
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  |:-------------:|:------:|:----:|:---------------:|:--------------:|:---------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:-----------:|:------------:|
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- | 0.6339 | 0.0469 | 100 | 0.0942 | 0.7833 | 0.7817 | 0.7822 | 0.7875 | 0.7816 | 0.7865 | 0.7100 | 0.7029 |
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- | 0.448 | 0.0937 | 200 | 0.0808 | 0.7856 | 0.7882 | 0.7887 | 0.7945 | 0.7873 | 0.7930 | 0.7399 | 0.7354 |
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- | 0.427 | 0.1406 | 300 | 0.0699 | 0.8037 | 0.8029 | 0.8021 | 0.8090 | 0.8010 | 0.8077 | 0.7505 | 0.7461 |
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- | 0.4052 | 0.1874 | 400 | 0.0616 | 0.8109 | 0.8096 | 0.8094 | 0.8143 | 0.8080 | 0.8128 | 0.7663 | 0.7620 |
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- | 0.4023 | 0.2343 | 500 | 0.0612 | 0.8109 | 0.8135 | 0.8133 | 0.8187 | 0.8113 | 0.8168 | 0.7739 | 0.7705 |
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- | 0.3754 | 0.2812 | 600 | 0.0595 | 0.8105 | 0.8125 | 0.8101 | 0.8162 | 0.8087 | 0.8146 | 0.7706 | 0.7681 |
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- | 0.3729 | 0.3280 | 700 | 0.0619 | 0.8155 | 0.8176 | 0.8144 | 0.8212 | 0.8128 | 0.8196 | 0.7761 | 0.7736 |
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- | 0.341 | 0.3749 | 800 | 0.0530 | 0.8137 | 0.8155 | 0.8201 | 0.8246 | 0.8190 | 0.8234 | 0.7696 | 0.7663 |
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- | 0.3161 | 0.4217 | 900 | 0.0568 | 0.8162 | 0.8182 | 0.8209 | 0.8262 | 0.8198 | 0.8252 | 0.7660 | 0.7625 |
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- | 0.3122 | 0.4686 | 1000 | 0.0541 | 0.8215 | 0.8236 | 0.8220 | 0.8284 | 0.8207 | 0.8268 | 0.7790 | 0.7745 |
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- | 0.3301 | 0.5155 | 1100 | 0.0617 | 0.8116 | 0.8150 | 0.8177 | 0.8228 | 0.8161 | 0.8212 | 0.7638 | 0.7598 |
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- | 0.3637 | 0.5623 | 1200 | 0.0532 | 0.8108 | 0.8145 | 0.8175 | 0.8222 | 0.8156 | 0.8202 | 0.7681 | 0.7643 |
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- | 0.2885 | 0.6092 | 1300 | 0.0451 | 0.8272 | 0.8278 | 0.8275 | 0.8324 | 0.8268 | 0.8318 | 0.7925 | 0.7888 |
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- | 0.2852 | 0.6560 | 1400 | 0.0473 | 0.8246 | 0.8264 | 0.8228 | 0.8281 | 0.8221 | 0.8275 | 0.7893 | 0.7874 |
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- | 0.3225 | 0.7029 | 1500 | 0.0507 | 0.8259 | 0.8284 | 0.8274 | 0.8335 | 0.8263 | 0.8325 | 0.7737 | 0.7708 |
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- | 0.3201 | 0.7498 | 1600 | 0.0467 | 0.8248 | 0.8268 | 0.8232 | 0.8282 | 0.8222 | 0.8274 | 0.7800 | 0.7772 |
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- | 0.3199 | 0.7966 | 1700 | 0.0511 | 0.8215 | 0.8239 | 0.8266 | 0.8322 | 0.8257 | 0.8308 | 0.7702 | 0.7658 |
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- | 0.2431 | 0.8435 | 1800 | 0.0482 | 0.8271 | 0.8287 | 0.8282 | 0.8333 | 0.8277 | 0.8326 | 0.7791 | 0.7749 |
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- | 0.3051 | 0.8903 | 1900 | 0.0465 | 0.8277 | 0.8295 | 0.8257 | 0.8324 | 0.8249 | 0.8319 | 0.7814 | 0.7782 |
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- | 0.3287 | 0.9372 | 2000 | 0.0551 | 0.8207 | 0.8244 | 0.8238 | 0.8296 | 0.8229 | 0.8287 | 0.7620 | 0.7569 |
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- | 0.2889 | 0.9841 | 2100 | 0.0463 | 0.8264 | 0.8281 | 0.8304 | 0.8368 | 0.8298 | 0.8363 | 0.7651 | 0.7620 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [x2bee/KoModernBERT-base-mlm-v03-retry-ckp02](https://huggingface.co/x2bee/KoModernBERT-base-mlm-v03-retry-ckp02) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0355
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+ - Pearson Cosine: 0.8274
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+ - Spearman Cosine: 0.8298
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+ - Pearson Manhattan: 0.8125
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+ - Spearman Manhattan: 0.8227
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+ - Pearson Euclidean: 0.8113
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+ - Spearman Euclidean: 0.8215
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+ - Pearson Dot: 0.7647
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+ - Spearman Dot: 0.7648
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  ## Model description
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 2.0
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Pearson Cosine | Spearman Cosine | Pearson Manhattan | Spearman Manhattan | Pearson Euclidean | Spearman Euclidean | Pearson Dot | Spearman Dot |
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  |:-------------:|:------:|:----:|:---------------:|:--------------:|:---------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:-----------:|:------------:|
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+ | 0.596 | 0.0469 | 100 | 0.0828 | 0.7829 | 0.7827 | 0.7914 | 0.7948 | 0.7911 | 0.7954 | 0.6840 | 0.6773 |
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+ | 0.4303 | 0.0937 | 200 | 0.0730 | 0.7867 | 0.7920 | 0.7977 | 0.8026 | 0.7981 | 0.8036 | 0.7104 | 0.7050 |
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+ | 0.409 | 0.1406 | 300 | 0.0649 | 0.8013 | 0.8024 | 0.8094 | 0.8136 | 0.8093 | 0.8141 | 0.7203 | 0.7128 |
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+ | 0.3979 | 0.1874 | 400 | 0.0562 | 0.8114 | 0.8115 | 0.8150 | 0.8187 | 0.8144 | 0.8188 | 0.7383 | 0.7353 |
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+ | 0.4072 | 0.2343 | 500 | 0.0635 | 0.8095 | 0.8150 | 0.8159 | 0.8223 | 0.8155 | 0.8221 | 0.7243 | 0.7169 |
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+ | 0.3625 | 0.2812 | 600 | 0.0590 | 0.8091 | 0.8144 | 0.8111 | 0.8165 | 0.8107 | 0.8163 | 0.7392 | 0.7378 |
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+ | 0.3796 | 0.3280 | 700 | 0.0638 | 0.8146 | 0.8208 | 0.8168 | 0.8234 | 0.8164 | 0.8232 | 0.7567 | 0.7549 |
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+ | 0.3474 | 0.3749 | 800 | 0.0496 | 0.8119 | 0.8182 | 0.8252 | 0.8302 | 0.8249 | 0.8303 | 0.7310 | 0.7270 |
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+ | 0.3159 | 0.4217 | 900 | 0.0567 | 0.8164 | 0.8209 | 0.8233 | 0.8286 | 0.8229 | 0.8285 | 0.7461 | 0.7445 |
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+ | 0.3132 | 0.4686 | 1000 | 0.0541 | 0.8214 | 0.8266 | 0.8214 | 0.8282 | 0.8205 | 0.8277 | 0.7568 | 0.7562 |
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+ | 0.3258 | 0.5155 | 1100 | 0.0605 | 0.8104 | 0.8165 | 0.8166 | 0.8232 | 0.8162 | 0.8231 | 0.7357 | 0.7310 |
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+ | 0.3566 | 0.5623 | 1200 | 0.0541 | 0.8126 | 0.8195 | 0.8142 | 0.8205 | 0.8132 | 0.8195 | 0.7469 | 0.7424 |
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+ | 0.2999 | 0.6092 | 1300 | 0.0474 | 0.8244 | 0.8290 | 0.8228 | 0.8289 | 0.8216 | 0.8284 | 0.7661 | 0.7629 |
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+ | 0.2793 | 0.6560 | 1400 | 0.0471 | 0.8212 | 0.8265 | 0.8201 | 0.8264 | 0.8187 | 0.8256 | 0.7625 | 0.7615 |
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+ | 0.3287 | 0.7029 | 1500 | 0.0523 | 0.8238 | 0.8296 | 0.8193 | 0.8276 | 0.8181 | 0.8266 | 0.7419 | 0.7435 |
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+ | 0.3227 | 0.7498 | 1600 | 0.0504 | 0.8223 | 0.8279 | 0.8180 | 0.8252 | 0.8172 | 0.8244 | 0.7568 | 0.7556 |
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+ | 0.3217 | 0.7966 | 1700 | 0.0516 | 0.8194 | 0.8249 | 0.8182 | 0.8243 | 0.8169 | 0.8233 | 0.7497 | 0.7466 |
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+ | 0.2344 | 0.8435 | 1800 | 0.0449 | 0.8292 | 0.8331 | 0.8188 | 0.8258 | 0.8174 | 0.8244 | 0.7711 | 0.7723 |
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+ | 0.2974 | 0.8903 | 1900 | 0.0502 | 0.8223 | 0.8270 | 0.8133 | 0.8208 | 0.8125 | 0.8199 | 0.7658 | 0.7662 |
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+ | 0.3285 | 0.9372 | 2000 | 0.0574 | 0.8144 | 0.8209 | 0.8112 | 0.8191 | 0.8105 | 0.8178 | 0.7339 | 0.7302 |
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+ | 0.2791 | 0.9841 | 2100 | 0.0479 | 0.8211 | 0.8250 | 0.8175 | 0.8237 | 0.8165 | 0.8229 | 0.7503 | 0.7507 |
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+ | 0.1703 | 1.0309 | 2200 | 0.0359 | 0.8254 | 0.8256 | 0.8156 | 0.8203 | 0.8143 | 0.8194 | 0.7736 | 0.7731 |
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+ | 0.1991 | 1.0778 | 2300 | 0.0362 | 0.8266 | 0.8265 | 0.8119 | 0.8186 | 0.8107 | 0.8177 | 0.7657 | 0.7682 |
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+ | 0.2088 | 1.1246 | 2400 | 0.0379 | 0.8224 | 0.8243 | 0.8158 | 0.8232 | 0.8148 | 0.8222 | 0.7539 | 0.7536 |
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+ | 0.2007 | 1.1715 | 2500 | 0.0336 | 0.8289 | 0.8304 | 0.8124 | 0.8206 | 0.8108 | 0.8195 | 0.7759 | 0.7778 |
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+ | 0.1828 | 1.2184 | 2600 | 0.0356 | 0.8246 | 0.8266 | 0.8162 | 0.8217 | 0.8154 | 0.8215 | 0.7684 | 0.7674 |
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+ | 0.2069 | 1.2652 | 2700 | 0.0368 | 0.8171 | 0.8196 | 0.8128 | 0.8187 | 0.8116 | 0.8179 | 0.7549 | 0.7544 |
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+ | 0.1957 | 1.3121 | 2800 | 0.0398 | 0.8185 | 0.8216 | 0.8168 | 0.8240 | 0.8160 | 0.8234 | 0.7474 | 0.7459 |
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+ | 0.1917 | 1.3590 | 2900 | 0.0355 | 0.8240 | 0.8256 | 0.8125 | 0.8199 | 0.8108 | 0.8186 | 0.7592 | 0.7607 |
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+ | 0.1944 | 1.4058 | 3000 | 0.0355 | 0.8271 | 0.8292 | 0.8163 | 0.8243 | 0.8148 | 0.8230 | 0.7621 | 0.7643 |
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+ | 0.1777 | 1.4527 | 3100 | 0.0360 | 0.8219 | 0.8227 | 0.8169 | 0.8232 | 0.8154 | 0.8221 | 0.7545 | 0.7557 |
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+ | 0.1816 | 1.4995 | 3200 | 0.0364 | 0.8213 | 0.8228 | 0.8185 | 0.8247 | 0.8169 | 0.8237 | 0.7616 | 0.7590 |
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+ | 0.229 | 1.5464 | 3300 | 0.0396 | 0.8169 | 0.8199 | 0.8177 | 0.8241 | 0.8165 | 0.8235 | 0.7529 | 0.7498 |
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+ | 0.1742 | 1.5933 | 3400 | 0.0345 | 0.8245 | 0.8252 | 0.8185 | 0.8253 | 0.8169 | 0.8243 | 0.7647 | 0.7634 |
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+ | 0.1606 | 1.6401 | 3500 | 0.0345 | 0.8219 | 0.8230 | 0.8146 | 0.8223 | 0.8128 | 0.8213 | 0.7629 | 0.7622 |
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+ | 0.1982 | 1.6870 | 3600 | 0.0380 | 0.8220 | 0.8233 | 0.8196 | 0.8257 | 0.8182 | 0.8249 | 0.7552 | 0.7535 |
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+ | 0.1824 | 1.7338 | 3700 | 0.0352 | 0.8246 | 0.8252 | 0.8181 | 0.8242 | 0.8166 | 0.8233 | 0.7567 | 0.7554 |
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+ | 0.2009 | 1.7807 | 3800 | 0.0358 | 0.8270 | 0.8278 | 0.8105 | 0.8181 | 0.8090 | 0.8164 | 0.7669 | 0.7655 |
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+ | 0.1899 | 1.8276 | 3900 | 0.0385 | 0.8240 | 0.8252 | 0.8133 | 0.8202 | 0.8111 | 0.8180 | 0.7418 | 0.7383 |
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+ | 0.1858 | 1.8744 | 4000 | 0.0337 | 0.8281 | 0.8274 | 0.8122 | 0.8198 | 0.8102 | 0.8180 | 0.7620 | 0.7590 |
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+ | 0.1679 | 1.9213 | 4100 | 0.0349 | 0.8238 | 0.8249 | 0.8109 | 0.8200 | 0.8097 | 0.8187 | 0.7561 | 0.7551 |
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+ | 0.1699 | 1.9681 | 4200 | 0.0355 | 0.8274 | 0.8298 | 0.8125 | 0.8227 | 0.8113 | 0.8215 | 0.7647 | 0.7648 |
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  ### Framework versions