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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-4k-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-4k-naamapadam |
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This model is a fine-tuned version of [livinNector/tabert-4k](https://huggingface.co/livinNector/tabert-4k) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2805 |
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- Precision: 0.7758 |
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- Recall: 0.8034 |
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- F1: 0.7894 |
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- Accuracy: 0.9077 |
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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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| 0.4467 | 0.05 | 400 | 0.3882 | 0.7144 | 0.6655 | 0.6891 | 0.8755 | |
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| 0.3775 | 0.1 | 800 | 0.3540 | 0.7122 | 0.7155 | 0.7138 | 0.8845 | |
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| 0.3571 | 0.15 | 1200 | 0.3432 | 0.7329 | 0.7266 | 0.7297 | 0.8872 | |
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| 0.3461 | 0.21 | 1600 | 0.3360 | 0.7252 | 0.7368 | 0.7309 | 0.8893 | |
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| 0.3456 | 0.26 | 2000 | 0.3359 | 0.7388 | 0.7470 | 0.7428 | 0.8896 | |
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| 0.3318 | 0.31 | 2400 | 0.3298 | 0.7460 | 0.7435 | 0.7447 | 0.8908 | |
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| 0.326 | 0.36 | 2800 | 0.3255 | 0.7490 | 0.7391 | 0.7440 | 0.8940 | |
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| 0.3264 | 0.41 | 3200 | 0.3243 | 0.7493 | 0.7605 | 0.7549 | 0.8953 | |
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| 0.3189 | 0.46 | 3600 | 0.3231 | 0.7305 | 0.7715 | 0.7504 | 0.8936 | |
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| 0.3119 | 0.51 | 4000 | 0.3125 | 0.7645 | 0.7525 | 0.7584 | 0.8985 | |
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| 0.3111 | 0.57 | 4400 | 0.3100 | 0.7479 | 0.7729 | 0.7602 | 0.8970 | |
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| 0.3088 | 0.62 | 4800 | 0.3148 | 0.7510 | 0.7749 | 0.7628 | 0.8966 | |
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| 0.3047 | 0.67 | 5200 | 0.3089 | 0.7581 | 0.7728 | 0.7654 | 0.8981 | |
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| 0.3054 | 0.72 | 5600 | 0.3073 | 0.7615 | 0.7709 | 0.7662 | 0.8990 | |
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| 0.3028 | 0.77 | 6000 | 0.3066 | 0.7466 | 0.7835 | 0.7646 | 0.8984 | |
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| 0.3007 | 0.82 | 6400 | 0.3035 | 0.7555 | 0.7791 | 0.7671 | 0.8995 | |
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| 0.2923 | 0.87 | 6800 | 0.3004 | 0.7647 | 0.7829 | 0.7737 | 0.9008 | |
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| 0.2927 | 0.93 | 7200 | 0.3050 | 0.7700 | 0.7646 | 0.7673 | 0.9002 | |
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| 0.2949 | 0.98 | 7600 | 0.2979 | 0.7686 | 0.7723 | 0.7704 | 0.9014 | |
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| 0.2758 | 1.03 | 8000 | 0.3013 | 0.7713 | 0.7783 | 0.7748 | 0.9030 | |
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| 0.2699 | 1.08 | 8400 | 0.3019 | 0.7503 | 0.7997 | 0.7742 | 0.9017 | |
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| 0.2688 | 1.13 | 8800 | 0.3002 | 0.7593 | 0.7940 | 0.7762 | 0.9017 | |
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| 0.2625 | 1.18 | 9200 | 0.2926 | 0.7590 | 0.7941 | 0.7762 | 0.9033 | |
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| 0.2671 | 1.23 | 9600 | 0.2922 | 0.7640 | 0.8019 | 0.7825 | 0.9043 | |
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| 0.267 | 1.29 | 10000 | 0.2895 | 0.7719 | 0.7877 | 0.7797 | 0.9044 | |
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| 0.2611 | 1.34 | 10400 | 0.2897 | 0.7704 | 0.7978 | 0.7839 | 0.9053 | |
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| 0.2666 | 1.39 | 10800 | 0.2896 | 0.7688 | 0.7887 | 0.7786 | 0.9042 | |
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| 0.2563 | 1.44 | 11200 | 0.2894 | 0.7672 | 0.7981 | 0.7823 | 0.9045 | |
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| 0.2598 | 1.49 | 11600 | 0.2841 | 0.7705 | 0.7960 | 0.7831 | 0.9058 | |
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| 0.2549 | 1.54 | 12000 | 0.2854 | 0.7695 | 0.7975 | 0.7832 | 0.9065 | |
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| 0.2558 | 1.59 | 12400 | 0.2873 | 0.7619 | 0.8108 | 0.7856 | 0.9045 | |
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| 0.2564 | 1.65 | 12800 | 0.2863 | 0.7757 | 0.7897 | 0.7826 | 0.9062 | |
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| 0.2618 | 1.7 | 13200 | 0.2860 | 0.7778 | 0.7899 | 0.7838 | 0.9066 | |
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| 0.2659 | 1.75 | 13600 | 0.2831 | 0.7748 | 0.8013 | 0.7879 | 0.9073 | |
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| 0.254 | 1.8 | 14000 | 0.2811 | 0.7761 | 0.7978 | 0.7868 | 0.9079 | |
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| 0.2628 | 1.85 | 14400 | 0.2807 | 0.7713 | 0.8028 | 0.7868 | 0.9069 | |
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| 0.2552 | 1.9 | 14800 | 0.2806 | 0.7756 | 0.7990 | 0.7872 | 0.9077 | |
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| 0.2568 | 1.95 | 15200 | 0.2805 | 0.7758 | 0.8034 | 0.7894 | 0.9077 | |
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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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