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mdeberta-semeval25_fold2

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deberta-v2
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  1. README.md +22 -22
  2. runs/Oct26_21-12-15_icuff-Z790-UD/events.out.tfevents.1729987936.icuff-Z790-UD.65127.2 +2 -2
README.md CHANGED
@@ -16,19 +16,19 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 8.8782
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- - Precision Samples: 0.0547
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- - Recall Samples: 0.8274
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- - F1 Samples: 0.0996
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  - Precision Macro: 0.4439
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- - Recall Macro: 0.6509
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- - F1 Macro: 0.2249
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- - Precision Micro: 0.0550
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- - Recall Micro: 0.7818
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- - F1 Micro: 0.1028
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- - Precision Weighted: 0.1905
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- - Recall Weighted: 0.7818
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- - F1 Weighted: 0.1231
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  ## Model description
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@@ -59,16 +59,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted |
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  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
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- | 10.355 | 1.0 | 19 | 9.8642 | 0.0379 | 0.7715 | 0.0705 | 0.4199 | 0.6368 | 0.1700 | 0.0380 | 0.7091 | 0.0722 | 0.2225 | 0.7091 | 0.0993 |
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- | 10.0038 | 2.0 | 38 | 9.5714 | 0.0450 | 0.7906 | 0.0827 | 0.5206 | 0.5917 | 0.1818 | 0.0449 | 0.7273 | 0.0845 | 0.2903 | 0.7273 | 0.1016 |
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- | 9.8117 | 3.0 | 57 | 9.4477 | 0.0482 | 0.7769 | 0.0880 | 0.5626 | 0.5574 | 0.2117 | 0.0480 | 0.7 | 0.0898 | 0.2906 | 0.7 | 0.0975 |
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- | 9.5902 | 4.0 | 76 | 9.3400 | 0.0497 | 0.7943 | 0.0909 | 0.5989 | 0.5608 | 0.2269 | 0.0496 | 0.7152 | 0.0928 | 0.2982 | 0.7152 | 0.1039 |
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- | 9.2784 | 5.0 | 95 | 9.2330 | 0.0501 | 0.8035 | 0.0916 | 0.5587 | 0.5779 | 0.2316 | 0.05 | 0.7333 | 0.0936 | 0.2642 | 0.7333 | 0.1107 |
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- | 9.4919 | 6.0 | 114 | 9.1259 | 0.0515 | 0.8262 | 0.0941 | 0.5368 | 0.6249 | 0.2241 | 0.0514 | 0.7667 | 0.0964 | 0.2607 | 0.7667 | 0.1131 |
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- | 9.2119 | 7.0 | 133 | 9.0005 | 0.0538 | 0.8278 | 0.0980 | 0.4847 | 0.6332 | 0.2181 | 0.0537 | 0.7758 | 0.1005 | 0.2198 | 0.7758 | 0.1199 |
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- | 9.063 | 8.0 | 152 | 8.9385 | 0.0541 | 0.8327 | 0.0987 | 0.4853 | 0.6402 | 0.2199 | 0.0542 | 0.7788 | 0.1014 | 0.2232 | 0.7788 | 0.1215 |
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- | 9.3401 | 9.0 | 171 | 8.8895 | 0.0545 | 0.8336 | 0.0993 | 0.4186 | 0.6420 | 0.2201 | 0.0548 | 0.7818 | 0.1024 | 0.1838 | 0.7818 | 0.1219 |
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- | 8.511 | 10.0 | 190 | 8.8782 | 0.0547 | 0.8274 | 0.0996 | 0.4439 | 0.6509 | 0.2249 | 0.0550 | 0.7818 | 0.1028 | 0.1905 | 0.7818 | 0.1231 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 8.8698
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+ - Precision Samples: 0.0554
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+ - Recall Samples: 0.8389
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+ - F1 Samples: 0.1008
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  - Precision Macro: 0.4439
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+ - Recall Macro: 0.6615
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+ - F1 Macro: 0.2256
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+ - Precision Micro: 0.0556
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+ - Recall Micro: 0.7909
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+ - F1 Micro: 0.1039
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+ - Precision Weighted: 0.1912
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+ - Recall Weighted: 0.7909
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+ - F1 Weighted: 0.1245
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted |
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  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
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+ | 10.355 | 1.0 | 19 | 9.8648 | 0.0379 | 0.7711 | 0.0705 | 0.4200 | 0.6387 | 0.1701 | 0.0380 | 0.7091 | 0.0722 | 0.2227 | 0.7091 | 0.0996 |
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+ | 10.0045 | 2.0 | 38 | 9.5721 | 0.0451 | 0.7906 | 0.0828 | 0.5212 | 0.5917 | 0.1823 | 0.0449 | 0.7273 | 0.0846 | 0.2911 | 0.7273 | 0.1023 |
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+ | 9.813 | 3.0 | 57 | 9.4463 | 0.0484 | 0.7777 | 0.0883 | 0.5628 | 0.5593 | 0.2119 | 0.0483 | 0.7030 | 0.0903 | 0.2908 | 0.7030 | 0.0978 |
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+ | 9.5884 | 4.0 | 76 | 9.3384 | 0.0494 | 0.7926 | 0.0903 | 0.5984 | 0.5590 | 0.2262 | 0.0493 | 0.7121 | 0.0923 | 0.2977 | 0.7121 | 0.1030 |
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+ | 9.2762 | 5.0 | 95 | 9.2320 | 0.0499 | 0.8026 | 0.0913 | 0.5663 | 0.5788 | 0.2291 | 0.0498 | 0.7303 | 0.0932 | 0.2718 | 0.7303 | 0.1066 |
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+ | 9.4881 | 6.0 | 114 | 9.1234 | 0.0514 | 0.8262 | 0.0941 | 0.5370 | 0.6249 | 0.2242 | 0.0514 | 0.7667 | 0.0963 | 0.2608 | 0.7667 | 0.1131 |
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+ | 9.2059 | 7.0 | 133 | 8.9948 | 0.0537 | 0.8278 | 0.0979 | 0.4847 | 0.6332 | 0.2181 | 0.0537 | 0.7758 | 0.1004 | 0.2199 | 0.7758 | 0.1200 |
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+ | 9.0569 | 8.0 | 152 | 8.9326 | 0.0539 | 0.8293 | 0.0984 | 0.4849 | 0.6374 | 0.2192 | 0.0541 | 0.7758 | 0.1012 | 0.2228 | 0.7758 | 0.1208 |
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+ | 9.332 | 9.0 | 171 | 8.8818 | 0.0545 | 0.8336 | 0.0993 | 0.4187 | 0.6420 | 0.2203 | 0.0548 | 0.7818 | 0.1024 | 0.1839 | 0.7818 | 0.1220 |
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+ | 8.5028 | 10.0 | 190 | 8.8698 | 0.0554 | 0.8389 | 0.1008 | 0.4439 | 0.6615 | 0.2256 | 0.0556 | 0.7909 | 0.1039 | 0.1912 | 0.7909 | 0.1245 |
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  ### Framework versions
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