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final_V1-roberta-text-classification-model

Text Classification
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roberta
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AmirlyPhd/final_V1-roberta-text-classification-model

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  1. README.md +32 -32
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2223
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- - Accuracy: 0.9595
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- - F1: 0.8274
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- - Precision: 0.8263
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- - Recall: 0.8294
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  ## Model description
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@@ -57,33 +57,33 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 1.6161 | 0.11 | 50 | 1.5152 | 0.4668 | 0.2435 | 0.2538 | 0.2617 |
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- | 0.6416 | 0.22 | 100 | 0.8046 | 0.7383 | 0.4474 | 0.4688 | 0.4675 |
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- | 0.3606 | 0.33 | 150 | 0.4826 | 0.8917 | 0.6667 | 0.6578 | 0.6789 |
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- | 0.3063 | 0.44 | 200 | 0.4505 | 0.8923 | 0.6638 | 0.6562 | 0.6752 |
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- | 0.2098 | 0.55 | 250 | 0.5294 | 0.8934 | 0.6602 | 0.6443 | 0.6769 |
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- | 0.2269 | 0.66 | 300 | 0.3460 | 0.8977 | 0.6702 | 0.6626 | 0.6784 |
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- | 0.1874 | 0.76 | 350 | 0.5650 | 0.8715 | 0.6497 | 0.6302 | 0.6719 |
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- | 0.2914 | 0.87 | 400 | 0.5704 | 0.8901 | 0.6608 | 0.6433 | 0.6798 |
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- | 0.1799 | 0.98 | 450 | 0.6265 | 0.8482 | 0.6321 | 0.7509 | 0.6583 |
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- | 0.1625 | 1.09 | 500 | 0.3098 | 0.9144 | 0.7058 | 0.8019 | 0.7080 |
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- | 0.0929 | 1.2 | 550 | 0.3927 | 0.9147 | 0.7113 | 0.7939 | 0.7122 |
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- | 0.096 | 1.31 | 600 | 0.2251 | 0.9497 | 0.8113 | 0.8129 | 0.8112 |
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- | 0.0862 | 1.42 | 650 | 0.1522 | 0.9655 | 0.8264 | 0.8235 | 0.8297 |
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- | 0.0943 | 1.53 | 700 | 0.2669 | 0.9480 | 0.8207 | 0.8184 | 0.8240 |
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- | 0.0799 | 1.64 | 750 | 0.2508 | 0.9532 | 0.8208 | 0.8155 | 0.8287 |
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- | 0.1052 | 1.75 | 800 | 0.1721 | 0.9653 | 0.8300 | 0.8291 | 0.8313 |
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- | 0.0867 | 1.86 | 850 | 0.1813 | 0.9658 | 0.8290 | 0.8262 | 0.8326 |
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- | 0.0616 | 1.97 | 900 | 0.2174 | 0.9590 | 0.8255 | 0.8216 | 0.8313 |
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- | 0.0704 | 2.07 | 950 | 0.1680 | 0.9672 | 0.8305 | 0.8245 | 0.8376 |
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- | 0.0436 | 2.18 | 1000 | 0.2169 | 0.9601 | 0.8264 | 0.8243 | 0.8299 |
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- | 0.0603 | 2.29 | 1050 | 0.1769 | 0.9634 | 0.8272 | 0.8220 | 0.8338 |
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- | 0.0638 | 2.4 | 1100 | 0.1431 | 0.9699 | 0.8300 | 0.8230 | 0.8380 |
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- | 0.017 | 2.51 | 1150 | 0.1741 | 0.9658 | 0.8294 | 0.8243 | 0.8358 |
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- | 0.0025 | 2.62 | 1200 | 0.2140 | 0.9623 | 0.8270 | 0.8218 | 0.8340 |
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- | 0.0427 | 2.73 | 1250 | 0.1680 | 0.9680 | 0.8293 | 0.8227 | 0.8372 |
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- | 0.0421 | 2.84 | 1300 | 0.1580 | 0.9691 | 0.8297 | 0.8231 | 0.8375 |
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- | 0.0292 | 2.95 | 1350 | 0.1597 | 0.9699 | 0.8315 | 0.8260 | 0.8381 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2641
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+ - Accuracy: 0.9502
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+ - F1: 0.8186
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+ - Precision: 0.8164
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+ - Recall: 0.8225
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 1.6615 | 0.11 | 50 | 1.5503 | 0.3199 | 0.1016 | 0.2599 | 0.1518 |
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+ | 0.6244 | 0.22 | 100 | 0.7198 | 0.7692 | 0.4656 | 0.4593 | 0.4818 |
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+ | 0.3344 | 0.33 | 150 | 0.4852 | 0.8893 | 0.6484 | 0.6264 | 0.6733 |
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+ | 0.2596 | 0.44 | 200 | 0.5277 | 0.8805 | 0.6398 | 0.6124 | 0.6748 |
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+ | 0.2173 | 0.55 | 250 | 0.4417 | 0.8849 | 0.6577 | 0.6421 | 0.6750 |
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+ | 0.2393 | 0.66 | 300 | 0.5221 | 0.8707 | 0.6511 | 0.6361 | 0.6684 |
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+ | 0.2229 | 0.76 | 350 | 0.4997 | 0.8928 | 0.6602 | 0.6410 | 0.6814 |
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+ | 0.1482 | 0.87 | 400 | 0.5111 | 0.8983 | 0.6409 | 0.6131 | 0.6810 |
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+ | 0.1831 | 0.98 | 450 | 0.4251 | 0.8827 | 0.6827 | 0.7149 | 0.6957 |
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+ | 0.1882 | 1.09 | 500 | 0.4130 | 0.9043 | 0.6805 | 0.7998 | 0.6878 |
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+ | 0.1182 | 1.2 | 550 | 0.4513 | 0.9076 | 0.6973 | 0.7703 | 0.7004 |
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+ | 0.101 | 1.31 | 600 | 0.3402 | 0.9221 | 0.7040 | 0.8097 | 0.7036 |
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+ | 0.0749 | 1.42 | 650 | 0.1566 | 0.9658 | 0.8229 | 0.8350 | 0.8122 |
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+ | 0.1294 | 1.53 | 700 | 0.1586 | 0.9675 | 0.8336 | 0.8327 | 0.8346 |
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+ | 0.046 | 1.64 | 750 | 0.2010 | 0.9604 | 0.8264 | 0.8211 | 0.8334 |
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+ | 0.0833 | 1.75 | 800 | 0.1707 | 0.9647 | 0.8285 | 0.8244 | 0.8330 |
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+ | 0.0759 | 1.86 | 850 | 0.1625 | 0.9664 | 0.8278 | 0.8285 | 0.8271 |
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+ | 0.0459 | 1.97 | 900 | 0.1831 | 0.9620 | 0.8258 | 0.8200 | 0.8328 |
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+ | 0.0726 | 2.07 | 950 | 0.1753 | 0.9625 | 0.8279 | 0.8287 | 0.8276 |
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+ | 0.0369 | 2.18 | 1000 | 0.1871 | 0.9650 | 0.8300 | 0.8252 | 0.8362 |
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+ | 0.0456 | 2.29 | 1050 | 0.1524 | 0.9683 | 0.8320 | 0.8278 | 0.8367 |
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+ | 0.0371 | 2.4 | 1100 | 0.1857 | 0.9631 | 0.8280 | 0.8219 | 0.8353 |
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+ | 0.0106 | 2.51 | 1150 | 0.1850 | 0.9661 | 0.8318 | 0.8274 | 0.8370 |
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+ | 0.0173 | 2.62 | 1200 | 0.2055 | 0.9647 | 0.8310 | 0.8259 | 0.8374 |
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+ | 0.036 | 2.73 | 1250 | 0.1699 | 0.9694 | 0.8311 | 0.8267 | 0.8358 |
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+ | 0.0176 | 2.84 | 1300 | 0.1780 | 0.9691 | 0.8325 | 0.8274 | 0.8382 |
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+ | 0.0444 | 2.95 | 1350 | 0.1918 | 0.9672 | 0.8319 | 0.8275 | 0.8371 |
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  ### Framework versions
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