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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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- f1 |
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model-index: |
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- name: PhoBert_Lexical_Datasnet51KBoDuoiWithNewLexical |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/phunganhsang123/huggingface/runs/ueje0ytn) |
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# PhoBert_Lexical_Datasnet51KBoDuoiWithNewLexical |
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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: 0.9059 |
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- Accuracy: 0.848 |
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- F1: 0.8469 |
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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: 2e-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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:| |
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| No log | 0.2506 | 200 | 0.6421 | 0.727 | 0.7274 | |
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| No log | 0.5013 | 400 | 0.5851 | 0.745 | 0.7340 | |
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| No log | 0.7519 | 600 | 0.5522 | 0.758 | 0.7561 | |
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| 0.3486 | 1.0025 | 800 | 0.5461 | 0.779 | 0.7693 | |
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| 0.3486 | 1.2531 | 1000 | 0.5009 | 0.786 | 0.7796 | |
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| 0.3486 | 1.5038 | 1200 | 0.5538 | 0.77 | 0.7681 | |
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| 0.3486 | 1.7544 | 1400 | 0.5067 | 0.777 | 0.7676 | |
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| 0.2582 | 2.0050 | 1600 | 0.5031 | 0.792 | 0.7842 | |
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| 0.2582 | 2.2556 | 1800 | 0.5232 | 0.786 | 0.7811 | |
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| 0.2582 | 2.5063 | 2000 | 0.5484 | 0.785 | 0.7827 | |
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| 0.2582 | 2.7569 | 2200 | 0.5155 | 0.798 | 0.7950 | |
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| 0.2134 | 3.0075 | 2400 | 0.5280 | 0.787 | 0.7847 | |
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| 0.2134 | 3.2581 | 2600 | 0.5263 | 0.793 | 0.7860 | |
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| 0.2134 | 3.5088 | 2800 | 0.5273 | 0.794 | 0.7930 | |
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| 0.2134 | 3.7594 | 3000 | 0.5354 | 0.792 | 0.7871 | |
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| 0.1799 | 4.0100 | 3200 | 0.5071 | 0.804 | 0.7982 | |
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| 0.1799 | 4.2607 | 3400 | 0.5655 | 0.802 | 0.7975 | |
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| 0.1799 | 4.5113 | 3600 | 0.5497 | 0.809 | 0.8034 | |
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| 0.1799 | 4.7619 | 3800 | 0.5645 | 0.808 | 0.8020 | |
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| 0.1493 | 5.0125 | 4000 | 0.5565 | 0.807 | 0.8003 | |
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| 0.1493 | 5.2632 | 4200 | 0.5861 | 0.813 | 0.8091 | |
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| 0.1493 | 5.5138 | 4400 | 0.6189 | 0.807 | 0.8055 | |
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| 0.1493 | 5.7644 | 4600 | 0.5003 | 0.819 | 0.8151 | |
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| 0.1278 | 6.0150 | 4800 | 0.5903 | 0.81 | 0.8080 | |
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| 0.1278 | 6.2657 | 5000 | 0.6112 | 0.81 | 0.8090 | |
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| 0.1278 | 6.5163 | 5200 | 0.5987 | 0.818 | 0.8150 | |
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| 0.1278 | 6.7669 | 5400 | 0.6466 | 0.802 | 0.8006 | |
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| 0.1091 | 7.0175 | 5600 | 0.6515 | 0.818 | 0.8172 | |
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| 0.1091 | 7.2682 | 5800 | 0.6541 | 0.819 | 0.8174 | |
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| 0.1091 | 7.5188 | 6000 | 0.6148 | 0.828 | 0.8246 | |
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| 0.1091 | 7.7694 | 6200 | 0.6246 | 0.827 | 0.8262 | |
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| 0.0917 | 8.0201 | 6400 | 0.6751 | 0.812 | 0.812 | |
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| 0.0917 | 8.2707 | 6600 | 0.6371 | 0.829 | 0.8281 | |
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| 0.0917 | 8.5213 | 6800 | 0.6979 | 0.823 | 0.8212 | |
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| 0.0917 | 8.7719 | 7000 | 0.6792 | 0.827 | 0.8241 | |
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| 0.0764 | 9.0226 | 7200 | 0.6833 | 0.835 | 0.8324 | |
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| 0.0764 | 9.2732 | 7400 | 0.6810 | 0.84 | 0.8383 | |
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| 0.0764 | 9.5238 | 7600 | 0.6799 | 0.826 | 0.8233 | |
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| 0.0764 | 9.7744 | 7800 | 0.6988 | 0.823 | 0.8211 | |
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| 0.0676 | 10.0251 | 8000 | 0.7213 | 0.832 | 0.8299 | |
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| 0.0676 | 10.2757 | 8200 | 0.7325 | 0.83 | 0.8288 | |
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| 0.0676 | 10.5263 | 8400 | 0.7338 | 0.831 | 0.8293 | |
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| 0.0676 | 10.7769 | 8600 | 0.7210 | 0.827 | 0.8262 | |
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| 0.0585 | 11.0276 | 8800 | 0.7874 | 0.829 | 0.8282 | |
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| 0.0585 | 11.2782 | 9000 | 0.7764 | 0.836 | 0.8347 | |
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| 0.0585 | 11.5288 | 9200 | 0.8112 | 0.835 | 0.8342 | |
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| 0.0585 | 11.7794 | 9400 | 0.7485 | 0.832 | 0.8307 | |
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| 0.0536 | 12.0301 | 9600 | 0.7640 | 0.835 | 0.8339 | |
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| 0.0536 | 12.2807 | 9800 | 0.7865 | 0.837 | 0.8344 | |
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| 0.0536 | 12.5313 | 10000 | 0.7579 | 0.836 | 0.8350 | |
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| 0.0536 | 12.7820 | 10200 | 0.7846 | 0.837 | 0.8357 | |
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| 0.0449 | 13.0326 | 10400 | 0.7385 | 0.84 | 0.8384 | |
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| 0.0449 | 13.2832 | 10600 | 0.8481 | 0.838 | 0.8372 | |
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| 0.0449 | 13.5338 | 10800 | 0.8422 | 0.838 | 0.8368 | |
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| 0.0449 | 13.7845 | 11000 | 0.7742 | 0.846 | 0.8445 | |
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| 0.0387 | 14.0351 | 11200 | 0.8304 | 0.842 | 0.8406 | |
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| 0.0387 | 14.2857 | 11400 | 0.7922 | 0.84 | 0.8386 | |
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| 0.0387 | 14.5363 | 11600 | 0.8284 | 0.843 | 0.8415 | |
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| 0.0387 | 14.7870 | 11800 | 0.8289 | 0.846 | 0.8449 | |
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| 0.0345 | 15.0376 | 12000 | 0.8012 | 0.849 | 0.8475 | |
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| 0.0345 | 15.2882 | 12200 | 0.8320 | 0.85 | 0.8489 | |
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| 0.0345 | 15.5388 | 12400 | 0.8118 | 0.853 | 0.8516 | |
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| 0.0345 | 15.7895 | 12600 | 0.8084 | 0.849 | 0.8480 | |
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| 0.0292 | 16.0401 | 12800 | 0.8427 | 0.841 | 0.8399 | |
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| 0.0292 | 16.2907 | 13000 | 0.8499 | 0.848 | 0.8469 | |
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| 0.0292 | 16.5414 | 13200 | 0.8477 | 0.848 | 0.8470 | |
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| 0.0292 | 16.7920 | 13400 | 0.8211 | 0.846 | 0.8441 | |
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| 0.0263 | 17.0426 | 13600 | 0.8418 | 0.848 | 0.8466 | |
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| 0.0263 | 17.2932 | 13800 | 0.8637 | 0.842 | 0.8408 | |
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| 0.0263 | 17.5439 | 14000 | 0.8693 | 0.845 | 0.8440 | |
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| 0.0263 | 17.7945 | 14200 | 0.8415 | 0.85 | 0.8483 | |
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| 0.0243 | 18.0451 | 14400 | 0.8735 | 0.848 | 0.8470 | |
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| 0.0243 | 18.2957 | 14600 | 0.8827 | 0.847 | 0.8460 | |
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| 0.0243 | 18.5464 | 14800 | 0.8729 | 0.85 | 0.8489 | |
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| 0.0243 | 18.7970 | 15000 | 0.8964 | 0.848 | 0.8469 | |
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| 0.0207 | 19.0476 | 15200 | 0.8939 | 0.846 | 0.8449 | |
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| 0.0207 | 19.2982 | 15400 | 0.9019 | 0.847 | 0.8460 | |
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| 0.0207 | 19.5489 | 15600 | 0.8948 | 0.848 | 0.8469 | |
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| 0.0207 | 19.7995 | 15800 | 0.9059 | 0.848 | 0.8469 | |
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### Framework versions |
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- Transformers 4.43.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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