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cls-comment-phobert-base-v2-v3.2

Text Classification
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Safetensors
roberta
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  1. README.md +46 -46
  2. model.safetensors +1 -1
README.md CHANGED
@@ -18,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4632
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- - Accuracy: 0.9408
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- - F1 Score: 0.9253
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- - Recall: 0.9343
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- - Precision: 0.9169
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  ## Model description
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@@ -57,51 +57,51 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
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- | 1.854 | 0.8696 | 100 | 1.6864 | 0.3995 | 0.0816 | 0.1429 | 0.0571 |
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- | 1.5604 | 1.7391 | 200 | 1.3090 | 0.6106 | 0.2504 | 0.2819 | 0.2671 |
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- | 1.1923 | 2.6087 | 300 | 0.9517 | 0.7864 | 0.5715 | 0.5777 | 0.5674 |
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- | 0.9214 | 3.4783 | 400 | 0.7528 | 0.8372 | 0.6124 | 0.6292 | 0.5977 |
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- | 0.758 | 4.3478 | 500 | 0.6326 | 0.8668 | 0.6659 | 0.6622 | 0.7570 |
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- | 0.6389 | 5.2174 | 600 | 0.5610 | 0.8913 | 0.7804 | 0.7578 | 0.8994 |
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- | 0.5577 | 6.0870 | 700 | 0.5189 | 0.9098 | 0.8772 | 0.8752 | 0.8849 |
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- | 0.4924 | 6.9565 | 800 | 0.4865 | 0.9158 | 0.8883 | 0.8831 | 0.8952 |
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- | 0.4466 | 7.8261 | 900 | 0.4718 | 0.9234 | 0.9011 | 0.9000 | 0.9036 |
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- | 0.4074 | 8.6957 | 1000 | 0.4614 | 0.9242 | 0.9037 | 0.9023 | 0.9075 |
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- | 0.3986 | 9.5652 | 1100 | 0.4673 | 0.9236 | 0.9049 | 0.9137 | 0.8981 |
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- | 0.3673 | 10.4348 | 1200 | 0.4504 | 0.9307 | 0.9134 | 0.9059 | 0.9213 |
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- | 0.3579 | 11.3043 | 1300 | 0.4478 | 0.9315 | 0.9145 | 0.9180 | 0.9123 |
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- | 0.3408 | 12.1739 | 1400 | 0.4463 | 0.9315 | 0.9126 | 0.9101 | 0.9161 |
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- | 0.3316 | 13.0435 | 1500 | 0.4618 | 0.9304 | 0.9114 | 0.9233 | 0.9015 |
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- | 0.321 | 13.9130 | 1600 | 0.4430 | 0.9361 | 0.9178 | 0.9203 | 0.9163 |
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- | 0.3113 | 14.7826 | 1700 | 0.4418 | 0.9394 | 0.9206 | 0.9233 | 0.9180 |
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- | 0.3085 | 15.6522 | 1800 | 0.4470 | 0.9391 | 0.9214 | 0.9226 | 0.9207 |
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- | 0.304 | 16.5217 | 1900 | 0.4500 | 0.9370 | 0.9171 | 0.9136 | 0.9217 |
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- | 0.2967 | 17.3913 | 2000 | 0.4605 | 0.9345 | 0.9149 | 0.9135 | 0.9175 |
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- | 0.2956 | 18.2609 | 2100 | 0.4595 | 0.9348 | 0.9145 | 0.9238 | 0.9061 |
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- | 0.2874 | 19.1304 | 2200 | 0.4620 | 0.9378 | 0.9185 | 0.9200 | 0.9179 |
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- | 0.2891 | 20.0 | 2300 | 0.4602 | 0.9361 | 0.9167 | 0.9182 | 0.9166 |
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- | 0.2862 | 20.8696 | 2400 | 0.4600 | 0.9337 | 0.9149 | 0.9169 | 0.9133 |
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- | 0.2851 | 21.7391 | 2500 | 0.4556 | 0.9372 | 0.9184 | 0.9282 | 0.9095 |
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- | 0.2798 | 22.6087 | 2600 | 0.4586 | 0.9405 | 0.9223 | 0.9296 | 0.9156 |
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- | 0.2787 | 23.4783 | 2700 | 0.4547 | 0.9408 | 0.9250 | 0.9280 | 0.9223 |
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- | 0.2806 | 24.3478 | 2800 | 0.4590 | 0.9380 | 0.9188 | 0.9259 | 0.9124 |
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- | 0.2768 | 25.2174 | 2900 | 0.4618 | 0.9361 | 0.9188 | 0.9180 | 0.9204 |
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- | 0.2773 | 26.0870 | 3000 | 0.4579 | 0.9380 | 0.9203 | 0.9231 | 0.9177 |
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- | 0.2724 | 26.9565 | 3100 | 0.4632 | 0.9408 | 0.9253 | 0.9343 | 0.9169 |
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- | 0.2716 | 27.8261 | 3200 | 0.4744 | 0.9364 | 0.9205 | 0.9338 | 0.9088 |
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- | 0.2705 | 28.6957 | 3300 | 0.4600 | 0.9402 | 0.9218 | 0.9282 | 0.9159 |
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- | 0.2682 | 29.5652 | 3400 | 0.4689 | 0.9380 | 0.9196 | 0.9256 | 0.9142 |
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- | 0.2718 | 30.4348 | 3500 | 0.4682 | 0.9413 | 0.9226 | 0.9288 | 0.9173 |
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- | 0.2694 | 31.3043 | 3600 | 0.4660 | 0.9386 | 0.9201 | 0.9289 | 0.9119 |
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- | 0.2678 | 32.1739 | 3700 | 0.4613 | 0.9405 | 0.9216 | 0.9239 | 0.9195 |
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- | 0.2679 | 33.0435 | 3800 | 0.4631 | 0.9408 | 0.9224 | 0.9280 | 0.9171 |
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- | 0.2681 | 33.9130 | 3900 | 0.4643 | 0.9402 | 0.9223 | 0.9299 | 0.9152 |
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- | 0.2685 | 34.7826 | 4000 | 0.4661 | 0.9399 | 0.9222 | 0.9304 | 0.9146 |
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  ### Framework versions
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  - Transformers 4.40.1
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  - Pytorch 2.2.1+cu121
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- - Datasets 2.19.0
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  - Tokenizers 0.19.1
 
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  This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4863
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+ - Accuracy: 0.9361
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+ - F1 Score: 0.9269
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+ - Recall: 0.9320
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+ - Precision: 0.9223
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
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+ | 1.8639 | 0.8696 | 100 | 1.7088 | 0.4004 | 0.0835 | 0.1438 | 0.1795 |
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+ | 1.5668 | 1.7391 | 200 | 1.3288 | 0.5800 | 0.2172 | 0.2575 | 0.2674 |
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+ | 1.2197 | 2.6087 | 300 | 0.9746 | 0.7668 | 0.5366 | 0.5148 | 0.5820 |
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+ | 0.9384 | 3.4783 | 400 | 0.7674 | 0.8391 | 0.6138 | 0.6267 | 0.6053 |
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+ | 0.7551 | 4.3478 | 500 | 0.6780 | 0.8527 | 0.6284 | 0.6454 | 0.6147 |
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+ | 0.6636 | 5.2174 | 600 | 0.6152 | 0.8684 | 0.6833 | 0.6785 | 0.7626 |
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+ | 0.5767 | 6.0870 | 700 | 0.5487 | 0.8929 | 0.7884 | 0.7698 | 0.8968 |
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+ | 0.5059 | 6.9565 | 800 | 0.5262 | 0.8986 | 0.8665 | 0.8534 | 0.8880 |
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+ | 0.4512 | 7.8261 | 900 | 0.4882 | 0.9195 | 0.9002 | 0.9082 | 0.8928 |
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+ | 0.4098 | 8.6957 | 1000 | 0.4828 | 0.9212 | 0.9111 | 0.9061 | 0.9183 |
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+ | 0.3916 | 9.5652 | 1100 | 0.4685 | 0.9280 | 0.9193 | 0.9140 | 0.9254 |
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+ | 0.373 | 10.4348 | 1200 | 0.4756 | 0.9239 | 0.9145 | 0.9210 | 0.9100 |
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+ | 0.3592 | 11.3043 | 1300 | 0.4597 | 0.9318 | 0.9230 | 0.9203 | 0.9263 |
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+ | 0.3377 | 12.1739 | 1400 | 0.4692 | 0.9304 | 0.9181 | 0.9198 | 0.9175 |
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+ | 0.3299 | 13.0435 | 1500 | 0.4672 | 0.9329 | 0.9244 | 0.9216 | 0.9292 |
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+ | 0.3198 | 13.9130 | 1600 | 0.4619 | 0.9331 | 0.9241 | 0.9225 | 0.9264 |
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+ | 0.3121 | 14.7826 | 1700 | 0.4672 | 0.9331 | 0.9243 | 0.9245 | 0.9249 |
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+ | 0.3053 | 15.6522 | 1800 | 0.4664 | 0.9345 | 0.9216 | 0.9272 | 0.9167 |
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+ | 0.3058 | 16.5217 | 1900 | 0.4655 | 0.9331 | 0.9229 | 0.9221 | 0.9240 |
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+ | 0.2976 | 17.3913 | 2000 | 0.4619 | 0.9356 | 0.9259 | 0.9221 | 0.9299 |
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+ | 0.2975 | 18.2609 | 2100 | 0.4663 | 0.9342 | 0.9255 | 0.9248 | 0.9267 |
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+ | 0.2872 | 19.1304 | 2200 | 0.4737 | 0.9345 | 0.9237 | 0.9194 | 0.9285 |
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+ | 0.2879 | 20.0 | 2300 | 0.4799 | 0.9318 | 0.9201 | 0.9295 | 0.9116 |
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+ | 0.2848 | 20.8696 | 2400 | 0.4843 | 0.9326 | 0.9194 | 0.9309 | 0.9092 |
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+ | 0.2808 | 21.7391 | 2500 | 0.4839 | 0.9326 | 0.9243 | 0.9237 | 0.9259 |
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+ | 0.2798 | 22.6087 | 2600 | 0.4840 | 0.9342 | 0.9240 | 0.9289 | 0.9197 |
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+ | 0.2797 | 23.4783 | 2700 | 0.4770 | 0.9334 | 0.9223 | 0.9246 | 0.9203 |
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+ | 0.2754 | 24.3478 | 2800 | 0.4863 | 0.9318 | 0.9225 | 0.9252 | 0.9212 |
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+ | 0.2752 | 25.2174 | 2900 | 0.4879 | 0.9326 | 0.9243 | 0.9259 | 0.9238 |
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+ | 0.2718 | 26.0870 | 3000 | 0.4788 | 0.9361 | 0.9270 | 0.9244 | 0.9301 |
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+ | 0.2712 | 26.9565 | 3100 | 0.4766 | 0.9356 | 0.9253 | 0.9237 | 0.9273 |
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+ | 0.2714 | 27.8261 | 3200 | 0.4780 | 0.9383 | 0.9288 | 0.9294 | 0.9285 |
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+ | 0.2697 | 28.6957 | 3300 | 0.4857 | 0.9367 | 0.9263 | 0.9286 | 0.9243 |
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+ | 0.2674 | 29.5652 | 3400 | 0.4876 | 0.9348 | 0.9235 | 0.9304 | 0.9174 |
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+ | 0.2681 | 30.4348 | 3500 | 0.4869 | 0.9361 | 0.9262 | 0.9348 | 0.9184 |
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+ | 0.2685 | 31.3043 | 3600 | 0.4931 | 0.9339 | 0.9241 | 0.9279 | 0.9212 |
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+ | 0.2665 | 32.1739 | 3700 | 0.4851 | 0.9339 | 0.9234 | 0.9262 | 0.9211 |
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+ | 0.2703 | 33.0435 | 3800 | 0.4864 | 0.9367 | 0.9263 | 0.9304 | 0.9226 |
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+ | 0.2661 | 33.9130 | 3900 | 0.4849 | 0.9364 | 0.9271 | 0.9319 | 0.9227 |
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+ | 0.2695 | 34.7826 | 4000 | 0.4863 | 0.9361 | 0.9269 | 0.9320 | 0.9223 |
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  ### Framework versions
103
 
104
  - Transformers 4.40.1
105
  - Pytorch 2.2.1+cu121
106
+ - Datasets 2.19.1
107
  - Tokenizers 0.19.1
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