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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: tabert-2k-naamapadam |
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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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# tabert-2k-naamapadam |
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This model is a fine-tuned version of [livinNector/tabert-2k](https://huggingface.co/livinNector/tabert-2k) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2850 |
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- Precision: 0.7765 |
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- Recall: 0.8041 |
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- F1: 0.7901 |
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- Accuracy: 0.9065 |
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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: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 128 |
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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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.4679 | 0.05 | 400 | 0.3991 | 0.7155 | 0.6561 | 0.6845 | 0.8720 | |
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| 0.3907 | 0.1 | 800 | 0.3632 | 0.7181 | 0.7233 | 0.7207 | 0.8822 | |
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| 0.3663 | 0.15 | 1200 | 0.3483 | 0.7271 | 0.7371 | 0.7321 | 0.8857 | |
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| 0.3557 | 0.21 | 1600 | 0.3457 | 0.7286 | 0.7506 | 0.7395 | 0.8874 | |
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| 0.3533 | 0.26 | 2000 | 0.3413 | 0.7371 | 0.7435 | 0.7403 | 0.8895 | |
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| 0.3396 | 0.31 | 2400 | 0.3326 | 0.7435 | 0.7546 | 0.7490 | 0.8910 | |
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| 0.3302 | 0.36 | 2800 | 0.3264 | 0.7528 | 0.7553 | 0.7540 | 0.8937 | |
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| 0.3344 | 0.41 | 3200 | 0.3231 | 0.7503 | 0.7720 | 0.7610 | 0.8951 | |
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| 0.3262 | 0.46 | 3600 | 0.3228 | 0.7387 | 0.7762 | 0.7570 | 0.8941 | |
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| 0.3186 | 0.51 | 4000 | 0.3158 | 0.7699 | 0.7666 | 0.7683 | 0.8986 | |
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| 0.3163 | 0.57 | 4400 | 0.3130 | 0.7453 | 0.7798 | 0.7622 | 0.8955 | |
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| 0.3143 | 0.62 | 4800 | 0.3150 | 0.7572 | 0.7751 | 0.7660 | 0.8961 | |
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| 0.3088 | 0.67 | 5200 | 0.3151 | 0.7543 | 0.7828 | 0.7683 | 0.8972 | |
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| 0.3115 | 0.72 | 5600 | 0.3141 | 0.7708 | 0.7706 | 0.7707 | 0.8977 | |
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| 0.3095 | 0.77 | 6000 | 0.3043 | 0.7657 | 0.7831 | 0.7743 | 0.8991 | |
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| 0.3044 | 0.82 | 6400 | 0.3087 | 0.7526 | 0.7881 | 0.7699 | 0.8972 | |
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| 0.2964 | 0.87 | 6800 | 0.3070 | 0.7644 | 0.7928 | 0.7783 | 0.8992 | |
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| 0.2972 | 0.93 | 7200 | 0.3102 | 0.7692 | 0.7738 | 0.7715 | 0.8999 | |
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| 0.2985 | 0.98 | 7600 | 0.3016 | 0.7731 | 0.7858 | 0.7794 | 0.9018 | |
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| 0.2822 | 1.03 | 8000 | 0.3049 | 0.7734 | 0.7909 | 0.7820 | 0.9031 | |
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| 0.2764 | 1.08 | 8400 | 0.3059 | 0.7575 | 0.7976 | 0.7770 | 0.9011 | |
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| 0.2752 | 1.13 | 8800 | 0.3052 | 0.7553 | 0.7996 | 0.7768 | 0.9015 | |
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| 0.2689 | 1.18 | 9200 | 0.2990 | 0.7642 | 0.7982 | 0.7808 | 0.9037 | |
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| 0.2738 | 1.23 | 9600 | 0.2985 | 0.7698 | 0.7987 | 0.7840 | 0.9035 | |
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| 0.2731 | 1.29 | 10000 | 0.2950 | 0.7713 | 0.7982 | 0.7845 | 0.9037 | |
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| 0.2694 | 1.34 | 10400 | 0.2920 | 0.7743 | 0.8017 | 0.7878 | 0.9059 | |
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| 0.2727 | 1.39 | 10800 | 0.2931 | 0.7693 | 0.7979 | 0.7834 | 0.9040 | |
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| 0.2622 | 1.44 | 11200 | 0.2946 | 0.7702 | 0.7942 | 0.7820 | 0.9032 | |
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| 0.2672 | 1.49 | 11600 | 0.2894 | 0.7724 | 0.8062 | 0.7890 | 0.9060 | |
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| 0.2601 | 1.54 | 12000 | 0.2907 | 0.7706 | 0.8010 | 0.7855 | 0.9058 | |
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| 0.2629 | 1.59 | 12400 | 0.2930 | 0.7628 | 0.8150 | 0.7880 | 0.9052 | |
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| 0.2635 | 1.65 | 12800 | 0.2907 | 0.7775 | 0.7970 | 0.7871 | 0.9047 | |
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| 0.2673 | 1.7 | 13200 | 0.2909 | 0.7753 | 0.7982 | 0.7866 | 0.9045 | |
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| 0.2726 | 1.75 | 13600 | 0.2880 | 0.7714 | 0.8048 | 0.7877 | 0.9054 | |
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| 0.2607 | 1.8 | 14000 | 0.2850 | 0.7760 | 0.8010 | 0.7883 | 0.9053 | |
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| 0.2684 | 1.85 | 14400 | 0.2847 | 0.7709 | 0.8077 | 0.7889 | 0.9059 | |
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| 0.2625 | 1.9 | 14800 | 0.2849 | 0.7742 | 0.8079 | 0.7907 | 0.9067 | |
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| 0.2631 | 1.95 | 15200 | 0.2850 | 0.7765 | 0.8041 | 0.7901 | 0.9065 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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