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--- |
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base_model: vinai/phobert-base-v2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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model-index: |
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- name: cls-comment-phobert-base-v2-v3.2.1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# cls-comment-phobert-base-v2-v3.2.1 |
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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: 0.4657 |
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- Accuracy: 0.9407 |
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- F1 Score: 0.9319 |
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- Recall: 0.9326 |
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- Precision: 0.9319 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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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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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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