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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-1k-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-1k-naamapadam |
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This model is a fine-tuned version of [livinNector/tabert-1k](https://huggingface.co/livinNector/tabert-1k) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2825 |
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- Precision: 0.7764 |
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- Recall: 0.8055 |
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- F1: 0.7907 |
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- Accuracy: 0.9068 |
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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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| 0.4618 | 0.05 | 400 | 0.3963 | 0.7329 | 0.6498 | 0.6889 | 0.8716 | |
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| 0.3869 | 0.1 | 800 | 0.3583 | 0.7145 | 0.7347 | 0.7244 | 0.8828 | |
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| 0.3642 | 0.15 | 1200 | 0.3511 | 0.7241 | 0.7412 | 0.7325 | 0.8842 | |
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| 0.3533 | 0.21 | 1600 | 0.3451 | 0.7393 | 0.7429 | 0.7411 | 0.8873 | |
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| 0.3501 | 0.26 | 2000 | 0.3367 | 0.7456 | 0.7562 | 0.7509 | 0.8899 | |
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| 0.3369 | 0.31 | 2400 | 0.3343 | 0.7476 | 0.7549 | 0.7512 | 0.8909 | |
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| 0.3302 | 0.36 | 2800 | 0.3282 | 0.7413 | 0.7584 | 0.7497 | 0.8926 | |
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| 0.3327 | 0.41 | 3200 | 0.3238 | 0.7584 | 0.7717 | 0.7650 | 0.8961 | |
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| 0.3248 | 0.46 | 3600 | 0.3209 | 0.7468 | 0.7795 | 0.7628 | 0.8956 | |
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| 0.3175 | 0.51 | 4000 | 0.3140 | 0.7659 | 0.7681 | 0.7670 | 0.8985 | |
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| 0.3132 | 0.57 | 4400 | 0.3111 | 0.7537 | 0.7795 | 0.7664 | 0.8970 | |
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| 0.3141 | 0.62 | 4800 | 0.3122 | 0.7529 | 0.7797 | 0.7661 | 0.8972 | |
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| 0.3077 | 0.67 | 5200 | 0.3138 | 0.7493 | 0.7844 | 0.7665 | 0.8974 | |
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| 0.309 | 0.72 | 5600 | 0.3099 | 0.7674 | 0.7729 | 0.7702 | 0.8992 | |
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| 0.3085 | 0.77 | 6000 | 0.3038 | 0.7626 | 0.7940 | 0.7780 | 0.9009 | |
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| 0.3031 | 0.82 | 6400 | 0.3055 | 0.7633 | 0.7834 | 0.7732 | 0.8992 | |
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| 0.2958 | 0.87 | 6800 | 0.3054 | 0.7621 | 0.7924 | 0.7770 | 0.8991 | |
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| 0.2953 | 0.93 | 7200 | 0.3076 | 0.7714 | 0.7834 | 0.7774 | 0.9005 | |
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| 0.2978 | 0.98 | 7600 | 0.3003 | 0.7729 | 0.7855 | 0.7792 | 0.9017 | |
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| 0.2826 | 1.03 | 8000 | 0.3016 | 0.7665 | 0.7905 | 0.7783 | 0.9012 | |
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| 0.2757 | 1.08 | 8400 | 0.3053 | 0.7520 | 0.8072 | 0.7786 | 0.8996 | |
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| 0.2751 | 1.13 | 8800 | 0.3026 | 0.7626 | 0.7982 | 0.7800 | 0.9008 | |
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| 0.2694 | 1.18 | 9200 | 0.2957 | 0.7682 | 0.8007 | 0.7841 | 0.9039 | |
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| 0.2723 | 1.23 | 9600 | 0.2944 | 0.7698 | 0.8005 | 0.7849 | 0.9039 | |
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| 0.2726 | 1.29 | 10000 | 0.2912 | 0.7774 | 0.7930 | 0.7851 | 0.9042 | |
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| 0.2674 | 1.34 | 10400 | 0.2912 | 0.7739 | 0.7973 | 0.7854 | 0.9043 | |
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| 0.2714 | 1.39 | 10800 | 0.2907 | 0.7729 | 0.7995 | 0.7860 | 0.9036 | |
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| 0.2625 | 1.44 | 11200 | 0.2949 | 0.7716 | 0.7965 | 0.7838 | 0.9041 | |
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| 0.2669 | 1.49 | 11600 | 0.2883 | 0.7701 | 0.8087 | 0.7889 | 0.9054 | |
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| 0.2601 | 1.54 | 12000 | 0.2868 | 0.7759 | 0.8069 | 0.7911 | 0.9066 | |
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| 0.2633 | 1.59 | 12400 | 0.2895 | 0.7659 | 0.8125 | 0.7885 | 0.9051 | |
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| 0.2641 | 1.65 | 12800 | 0.2878 | 0.7790 | 0.7972 | 0.7880 | 0.9059 | |
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| 0.2661 | 1.7 | 13200 | 0.2875 | 0.7800 | 0.7999 | 0.7898 | 0.9068 | |
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| 0.2719 | 1.75 | 13600 | 0.2853 | 0.7783 | 0.8025 | 0.7902 | 0.9070 | |
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| 0.2602 | 1.8 | 14000 | 0.2827 | 0.7801 | 0.8051 | 0.7924 | 0.9070 | |
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| 0.2688 | 1.85 | 14400 | 0.2819 | 0.7742 | 0.8061 | 0.7898 | 0.9066 | |
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| 0.2615 | 1.9 | 14800 | 0.2828 | 0.7764 | 0.8017 | 0.7888 | 0.9065 | |
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| 0.2623 | 1.95 | 15200 | 0.2825 | 0.7764 | 0.8055 | 0.7907 | 0.9068 | |
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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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