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cls-comment-phobert-base-v2-v3.2.1

Text Classification
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Safetensors
roberta
Generated from Trainer
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  1. README.md +46 -35
  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: 0.2873
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- - Accuracy: 0.9323
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- - F1 Score: 0.9262
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- - Recall: 0.9217
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- - Precision: 0.9320
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  ## Model description
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@@ -51,41 +51,52 @@ The following hyperparameters were used during training:
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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  - training_steps: 4000
 
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
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- | 1.8947 | 0.8696 | 100 | 1.6875 | 0.4001 | 0.0832 | 0.1437 | 0.1464 |
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- | 1.5395 | 1.7391 | 200 | 1.2897 | 0.5849 | 0.2356 | 0.2632 | 0.2752 |
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- | 1.1205 | 2.6087 | 300 | 0.8468 | 0.7999 | 0.5833 | 0.5810 | 0.5890 |
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- | 0.82 | 3.4783 | 400 | 0.6537 | 0.8369 | 0.6179 | 0.6355 | 0.6062 |
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- | 0.6232 | 4.3478 | 500 | 0.5371 | 0.8538 | 0.6337 | 0.6518 | 0.7525 |
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- | 0.5148 | 5.2174 | 600 | 0.4651 | 0.8728 | 0.7299 | 0.7211 | 0.7549 |
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- | 0.4204 | 6.0870 | 700 | 0.4010 | 0.8869 | 0.7654 | 0.7712 | 0.8914 |
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- | 0.3421 | 6.9565 | 800 | 0.3648 | 0.9051 | 0.8714 | 0.8588 | 0.8941 |
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- | 0.2841 | 7.8261 | 900 | 0.3240 | 0.9182 | 0.9007 | 0.9038 | 0.8978 |
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- | 0.2319 | 8.6957 | 1000 | 0.3025 | 0.9204 | 0.9061 | 0.8976 | 0.9175 |
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- | 0.205 | 9.5652 | 1100 | 0.2986 | 0.9209 | 0.9099 | 0.9086 | 0.9123 |
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- | 0.1783 | 10.4348 | 1200 | 0.3047 | 0.9206 | 0.9104 | 0.9207 | 0.9025 |
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- | 0.1587 | 11.3043 | 1300 | 0.2758 | 0.9296 | 0.9203 | 0.9177 | 0.9233 |
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- | 0.1286 | 12.1739 | 1400 | 0.2927 | 0.9266 | 0.9144 | 0.9199 | 0.9101 |
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- | 0.1221 | 13.0435 | 1500 | 0.2821 | 0.9318 | 0.9245 | 0.9194 | 0.9309 |
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- | 0.1087 | 13.9130 | 1600 | 0.2789 | 0.9293 | 0.9160 | 0.9237 | 0.9090 |
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- | 0.0982 | 14.7826 | 1700 | 0.2834 | 0.9291 | 0.9196 | 0.9213 | 0.9188 |
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- | 0.089 | 15.6522 | 1800 | 0.2828 | 0.9299 | 0.9202 | 0.9261 | 0.9152 |
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- | 0.0795 | 16.5217 | 1900 | 0.2737 | 0.9331 | 0.9244 | 0.9239 | 0.9253 |
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- | 0.0684 | 17.3913 | 2000 | 0.2873 | 0.9323 | 0.9262 | 0.9217 | 0.9320 |
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- | 0.0673 | 18.2609 | 2100 | 0.2904 | 0.9320 | 0.9252 | 0.9184 | 0.9333 |
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- | 0.0571 | 19.1304 | 2200 | 0.3166 | 0.9293 | 0.9222 | 0.9210 | 0.9251 |
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- | 0.0561 | 20.0 | 2300 | 0.2922 | 0.9318 | 0.9221 | 0.9298 | 0.9150 |
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- | 0.0511 | 20.8696 | 2400 | 0.2993 | 0.9315 | 0.9191 | 0.9303 | 0.9088 |
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- | 0.0442 | 21.7391 | 2500 | 0.3201 | 0.9266 | 0.9162 | 0.9280 | 0.9060 |
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- | 0.0447 | 22.6087 | 2600 | 0.3155 | 0.9282 | 0.9137 | 0.9282 | 0.9010 |
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- | 0.0415 | 23.4783 | 2700 | 0.3018 | 0.9334 | 0.9226 | 0.9270 | 0.9185 |
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- | 0.0359 | 24.3478 | 2800 | 0.3192 | 0.9299 | 0.9177 | 0.9308 | 0.9063 |
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- | 0.0369 | 25.2174 | 2900 | 0.3064 | 0.9337 | 0.9211 | 0.9286 | 0.9141 |
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- | 0.0296 | 26.0870 | 3000 | 0.3110 | 0.9329 | 0.9237 | 0.9279 | 0.9198 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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: 0.4654
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+ - Accuracy: 0.9402
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+ - F1 Score: 0.9310
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+ - Recall: 0.9300
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+ - Precision: 0.9325
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  ## Model description
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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  - training_steps: 4000
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+ - label_smoothing_factor: 0.05
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
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+ | 1.8571 | 0.8696 | 100 | 1.6996 | 0.3986 | 0.0814 | 0.1429 | 0.0569 |
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+ | 1.552 | 1.7391 | 200 | 1.2878 | 0.6150 | 0.2552 | 0.2860 | 0.2738 |
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+ | 1.1701 | 2.6087 | 300 | 0.9309 | 0.7746 | 0.5380 | 0.5249 | 0.5797 |
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+ | 0.8958 | 3.4783 | 400 | 0.7468 | 0.8371 | 0.6099 | 0.6121 | 0.6113 |
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+ | 0.7463 | 4.3478 | 500 | 0.6540 | 0.8641 | 0.6758 | 0.6741 | 0.7556 |
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+ | 0.6489 | 5.2174 | 600 | 0.5884 | 0.8866 | 0.7502 | 0.7443 | 0.7611 |
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+ | 0.5604 | 6.0870 | 700 | 0.5297 | 0.9010 | 0.8350 | 0.8196 | 0.9060 |
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+ | 0.4907 | 6.9565 | 800 | 0.4928 | 0.9171 | 0.8962 | 0.8769 | 0.9190 |
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+ | 0.4428 | 7.8261 | 900 | 0.4692 | 0.9220 | 0.9048 | 0.8958 | 0.9170 |
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+ | 0.4086 | 8.6957 | 1000 | 0.4600 | 0.9236 | 0.9073 | 0.9183 | 0.8978 |
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+ | 0.3892 | 9.5652 | 1100 | 0.4530 | 0.9293 | 0.9156 | 0.9159 | 0.9156 |
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+ | 0.3659 | 10.4348 | 1200 | 0.4574 | 0.9258 | 0.9154 | 0.9258 | 0.9071 |
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+ | 0.3577 | 11.3043 | 1300 | 0.4533 | 0.9288 | 0.9159 | 0.9177 | 0.9152 |
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+ | 0.338 | 12.1739 | 1400 | 0.4454 | 0.9339 | 0.9203 | 0.9285 | 0.9128 |
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+ | 0.3302 | 13.0435 | 1500 | 0.4539 | 0.9312 | 0.9179 | 0.9172 | 0.9196 |
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+ | 0.3186 | 13.9130 | 1600 | 0.4533 | 0.9320 | 0.9220 | 0.9146 | 0.9298 |
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+ | 0.3146 | 14.7826 | 1700 | 0.4485 | 0.9356 | 0.9246 | 0.9224 | 0.9281 |
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+ | 0.3093 | 15.6522 | 1800 | 0.4557 | 0.9326 | 0.9194 | 0.9125 | 0.9291 |
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+ | 0.3019 | 16.5217 | 1900 | 0.4684 | 0.9290 | 0.9169 | 0.9234 | 0.9128 |
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+ | 0.2985 | 17.3913 | 2000 | 0.4545 | 0.9347 | 0.9248 | 0.9238 | 0.9259 |
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+ | 0.2959 | 18.2609 | 2100 | 0.4689 | 0.9334 | 0.9220 | 0.9208 | 0.9249 |
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+ | 0.2891 | 19.1304 | 2200 | 0.4558 | 0.9386 | 0.9262 | 0.9180 | 0.9360 |
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+ | 0.2905 | 20.0 | 2300 | 0.4590 | 0.9358 | 0.9227 | 0.9163 | 0.9308 |
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+ | 0.2875 | 20.8696 | 2400 | 0.4797 | 0.9307 | 0.9193 | 0.9146 | 0.9268 |
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+ | 0.2812 | 21.7391 | 2500 | 0.4697 | 0.9356 | 0.9247 | 0.9257 | 0.9242 |
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+ | 0.2789 | 22.6087 | 2600 | 0.4668 | 0.9380 | 0.9255 | 0.9250 | 0.9271 |
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+ | 0.2785 | 23.4783 | 2700 | 0.4671 | 0.9383 | 0.9293 | 0.9301 | 0.9289 |
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+ | 0.2773 | 24.3478 | 2800 | 0.4657 | 0.9391 | 0.9293 | 0.9274 | 0.9328 |
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+ | 0.2814 | 25.2174 | 2900 | 0.4702 | 0.9361 | 0.9259 | 0.9285 | 0.9244 |
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+ | 0.2744 | 26.0870 | 3000 | 0.4732 | 0.9353 | 0.9274 | 0.9290 | 0.9273 |
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+ | 0.2772 | 26.9565 | 3100 | 0.4676 | 0.9388 | 0.9281 | 0.9301 | 0.9264 |
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+ | 0.2736 | 27.8261 | 3200 | 0.4661 | 0.9394 | 0.9281 | 0.9242 | 0.9325 |
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+ | 0.2754 | 28.6957 | 3300 | 0.4746 | 0.9367 | 0.9257 | 0.9233 | 0.9288 |
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+ | 0.2717 | 29.5652 | 3400 | 0.4688 | 0.9380 | 0.9283 | 0.9255 | 0.9315 |
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+ | 0.27 | 30.4348 | 3500 | 0.4697 | 0.9388 | 0.9304 | 0.9307 | 0.9308 |
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+ | 0.2674 | 31.3043 | 3600 | 0.4668 | 0.9391 | 0.9274 | 0.9311 | 0.9240 |
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+ | 0.2693 | 32.1739 | 3700 | 0.4657 | 0.9407 | 0.9319 | 0.9326 | 0.9319 |
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+ | 0.2685 | 33.0435 | 3800 | 0.4672 | 0.9402 | 0.9298 | 0.9304 | 0.9297 |
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+ | 0.268 | 33.9130 | 3900 | 0.4668 | 0.9410 | 0.9317 | 0.9311 | 0.9328 |
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+ | 0.272 | 34.7826 | 4000 | 0.4654 | 0.9402 | 0.9310 | 0.9300 | 0.9325 |
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  ### Framework versions
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