|
--- |
|
license: mit |
|
base_model: haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- massive |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: scenario-KD-PR-MSV-EN-EN-D2_data-en-massive_all_1_166 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# scenario-KD-PR-MSV-EN-EN-D2_data-en-massive_all_1_166 |
|
|
|
This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1) on the massive dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 3.9034 |
|
- Accuracy: 0.3152 |
|
- F1: 0.2967 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 5e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 66 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 30 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
|
| No log | 0.28 | 100 | 4.2349 | 0.0671 | 0.0036 | |
|
| No log | 0.56 | 200 | 4.0584 | 0.1478 | 0.0387 | |
|
| No log | 0.83 | 300 | 3.8868 | 0.2207 | 0.1016 | |
|
| No log | 1.11 | 400 | 3.8941 | 0.2315 | 0.1309 | |
|
| 3.3584 | 1.39 | 500 | 3.7344 | 0.2735 | 0.1722 | |
|
| 3.3584 | 1.67 | 600 | 3.8172 | 0.2668 | 0.1933 | |
|
| 3.3584 | 1.94 | 700 | 3.7445 | 0.2709 | 0.2084 | |
|
| 3.3584 | 2.22 | 800 | 3.6741 | 0.3013 | 0.2250 | |
|
| 3.3584 | 2.5 | 900 | 3.7111 | 0.2996 | 0.2395 | |
|
| 1.98 | 2.78 | 1000 | 3.6822 | 0.2994 | 0.2445 | |
|
| 1.98 | 3.06 | 1100 | 3.7436 | 0.2984 | 0.2477 | |
|
| 1.98 | 3.33 | 1200 | 3.7195 | 0.3040 | 0.2515 | |
|
| 1.98 | 3.61 | 1300 | 3.8800 | 0.2706 | 0.2273 | |
|
| 1.98 | 3.89 | 1400 | 3.7345 | 0.3057 | 0.2428 | |
|
| 1.5602 | 4.17 | 1500 | 3.8605 | 0.3010 | 0.2528 | |
|
| 1.5602 | 4.44 | 1600 | 3.7124 | 0.3140 | 0.2674 | |
|
| 1.5602 | 4.72 | 1700 | 3.7400 | 0.3041 | 0.2521 | |
|
| 1.5602 | 5.0 | 1800 | 3.9425 | 0.2957 | 0.2605 | |
|
| 1.5602 | 5.28 | 1900 | 3.7719 | 0.3133 | 0.2768 | |
|
| 1.3533 | 5.56 | 2000 | 3.8076 | 0.3100 | 0.2835 | |
|
| 1.3533 | 5.83 | 2100 | 3.6673 | 0.3258 | 0.2794 | |
|
| 1.3533 | 6.11 | 2200 | 3.8029 | 0.3080 | 0.2641 | |
|
| 1.3533 | 6.39 | 2300 | 3.7847 | 0.3079 | 0.2601 | |
|
| 1.3533 | 6.67 | 2400 | 3.8791 | 0.2994 | 0.2807 | |
|
| 1.2425 | 6.94 | 2500 | 3.7637 | 0.3122 | 0.2892 | |
|
| 1.2425 | 7.22 | 2600 | 3.8474 | 0.3155 | 0.2742 | |
|
| 1.2425 | 7.5 | 2700 | 3.8424 | 0.3131 | 0.2776 | |
|
| 1.2425 | 7.78 | 2800 | 3.8016 | 0.3113 | 0.2648 | |
|
| 1.2425 | 8.06 | 2900 | 3.8632 | 0.2981 | 0.2643 | |
|
| 1.1513 | 8.33 | 3000 | 3.8469 | 0.3088 | 0.2705 | |
|
| 1.1513 | 8.61 | 3100 | 3.9476 | 0.2929 | 0.2589 | |
|
| 1.1513 | 8.89 | 3200 | 3.8249 | 0.3178 | 0.2684 | |
|
| 1.1513 | 9.17 | 3300 | 3.7724 | 0.3166 | 0.2801 | |
|
| 1.1513 | 9.44 | 3400 | 3.7976 | 0.3215 | 0.2793 | |
|
| 1.0936 | 9.72 | 3500 | 3.6198 | 0.3498 | 0.3023 | |
|
| 1.0936 | 10.0 | 3600 | 3.8257 | 0.3075 | 0.2775 | |
|
| 1.0936 | 10.28 | 3700 | 3.7182 | 0.3224 | 0.2892 | |
|
| 1.0936 | 10.56 | 3800 | 3.8149 | 0.3149 | 0.2797 | |
|
| 1.0936 | 10.83 | 3900 | 3.7853 | 0.3276 | 0.2893 | |
|
| 1.0476 | 11.11 | 4000 | 3.8488 | 0.3177 | 0.2833 | |
|
| 1.0476 | 11.39 | 4100 | 4.0615 | 0.2979 | 0.2812 | |
|
| 1.0476 | 11.67 | 4200 | 3.8836 | 0.3178 | 0.2891 | |
|
| 1.0476 | 11.94 | 4300 | 4.1136 | 0.2832 | 0.2705 | |
|
| 1.0476 | 12.22 | 4400 | 3.8156 | 0.3144 | 0.2999 | |
|
| 1.0048 | 12.5 | 4500 | 3.9173 | 0.3117 | 0.2946 | |
|
| 1.0048 | 12.78 | 4600 | 3.7431 | 0.3293 | 0.2965 | |
|
| 1.0048 | 13.06 | 4700 | 3.7538 | 0.3245 | 0.2914 | |
|
| 1.0048 | 13.33 | 4800 | 3.9135 | 0.2957 | 0.2827 | |
|
| 1.0048 | 13.61 | 4900 | 3.8702 | 0.3133 | 0.2926 | |
|
| 0.9752 | 13.89 | 5000 | 3.8238 | 0.3131 | 0.2861 | |
|
| 0.9752 | 14.17 | 5100 | 3.9863 | 0.2986 | 0.2860 | |
|
| 0.9752 | 14.44 | 5200 | 3.9071 | 0.3068 | 0.2891 | |
|
| 0.9752 | 14.72 | 5300 | 4.1397 | 0.2902 | 0.2831 | |
|
| 0.9752 | 15.0 | 5400 | 4.0661 | 0.2916 | 0.2760 | |
|
| 0.9544 | 15.28 | 5500 | 3.9804 | 0.3059 | 0.2848 | |
|
| 0.9544 | 15.56 | 5600 | 4.1628 | 0.2815 | 0.2757 | |
|
| 0.9544 | 15.83 | 5700 | 3.8083 | 0.3233 | 0.2940 | |
|
| 0.9544 | 16.11 | 5800 | 3.8357 | 0.3144 | 0.2821 | |
|
| 0.9544 | 16.39 | 5900 | 4.0037 | 0.2987 | 0.2914 | |
|
| 0.935 | 16.67 | 6000 | 3.8943 | 0.3073 | 0.2803 | |
|
| 0.935 | 16.94 | 6100 | 3.8387 | 0.3171 | 0.2978 | |
|
| 0.935 | 17.22 | 6200 | 3.9244 | 0.3046 | 0.2799 | |
|
| 0.935 | 17.5 | 6300 | 3.9478 | 0.3065 | 0.2900 | |
|
| 0.935 | 17.78 | 6400 | 4.0418 | 0.3036 | 0.2754 | |
|
| 0.9186 | 18.06 | 6500 | 4.1112 | 0.2862 | 0.2773 | |
|
| 0.9186 | 18.33 | 6600 | 4.1101 | 0.2907 | 0.2750 | |
|
| 0.9186 | 18.61 | 6700 | 4.0951 | 0.2908 | 0.2763 | |
|
| 0.9186 | 18.89 | 6800 | 3.9274 | 0.3049 | 0.2824 | |
|
| 0.9186 | 19.17 | 6900 | 3.9502 | 0.2988 | 0.2843 | |
|
| 0.9081 | 19.44 | 7000 | 4.0642 | 0.2935 | 0.2879 | |
|
| 0.9081 | 19.72 | 7100 | 3.8820 | 0.3102 | 0.2914 | |
|
| 0.9081 | 20.0 | 7200 | 4.0206 | 0.2987 | 0.2893 | |
|
| 0.9081 | 20.28 | 7300 | 3.9475 | 0.3105 | 0.2949 | |
|
| 0.9081 | 20.56 | 7400 | 3.9688 | 0.3088 | 0.2868 | |
|
| 0.9007 | 20.83 | 7500 | 3.9359 | 0.3088 | 0.2867 | |
|
| 0.9007 | 21.11 | 7600 | 4.0488 | 0.3001 | 0.2862 | |
|
| 0.9007 | 21.39 | 7700 | 3.8327 | 0.3246 | 0.2988 | |
|
| 0.9007 | 21.67 | 7800 | 3.9259 | 0.3146 | 0.2978 | |
|
| 0.9007 | 21.94 | 7900 | 3.8813 | 0.3191 | 0.2962 | |
|
| 0.8902 | 22.22 | 8000 | 3.9249 | 0.3129 | 0.2953 | |
|
| 0.8902 | 22.5 | 8100 | 3.9929 | 0.3066 | 0.2949 | |
|
| 0.8902 | 22.78 | 8200 | 3.9557 | 0.3118 | 0.2966 | |
|
| 0.8902 | 23.06 | 8300 | 4.0791 | 0.2933 | 0.2811 | |
|
| 0.8902 | 23.33 | 8400 | 3.8798 | 0.3173 | 0.2949 | |
|
| 0.8812 | 23.61 | 8500 | 4.0575 | 0.2969 | 0.2832 | |
|
| 0.8812 | 23.89 | 8600 | 3.9538 | 0.3071 | 0.2921 | |
|
| 0.8812 | 24.17 | 8700 | 4.1906 | 0.2817 | 0.2775 | |
|
| 0.8812 | 24.44 | 8800 | 3.9515 | 0.3113 | 0.2941 | |
|
| 0.8812 | 24.72 | 8900 | 3.8893 | 0.3190 | 0.2955 | |
|
| 0.8781 | 25.0 | 9000 | 3.9491 | 0.3094 | 0.2920 | |
|
| 0.8781 | 25.28 | 9100 | 3.8647 | 0.3171 | 0.2928 | |
|
| 0.8781 | 25.56 | 9200 | 3.8908 | 0.3146 | 0.2994 | |
|
| 0.8781 | 25.83 | 9300 | 3.9586 | 0.3088 | 0.2958 | |
|
| 0.8781 | 26.11 | 9400 | 3.9277 | 0.3104 | 0.2980 | |
|
| 0.8719 | 26.39 | 9500 | 3.9350 | 0.3097 | 0.2946 | |
|
| 0.8719 | 26.67 | 9600 | 4.0499 | 0.2948 | 0.2890 | |
|
| 0.8719 | 26.94 | 9700 | 3.9529 | 0.3109 | 0.2917 | |
|
| 0.8719 | 27.22 | 9800 | 3.9768 | 0.3073 | 0.2896 | |
|
| 0.8719 | 27.5 | 9900 | 3.8371 | 0.3239 | 0.3011 | |
|
| 0.871 | 27.78 | 10000 | 3.9067 | 0.3131 | 0.2976 | |
|
| 0.871 | 28.06 | 10100 | 3.8732 | 0.3183 | 0.2971 | |
|
| 0.871 | 28.33 | 10200 | 3.9588 | 0.3070 | 0.2915 | |
|
| 0.871 | 28.61 | 10300 | 3.9081 | 0.3143 | 0.2988 | |
|
| 0.871 | 28.89 | 10400 | 3.8574 | 0.3199 | 0.3004 | |
|
| 0.8673 | 29.17 | 10500 | 3.9120 | 0.3131 | 0.2961 | |
|
| 0.8673 | 29.44 | 10600 | 3.8986 | 0.3147 | 0.2972 | |
|
| 0.8673 | 29.72 | 10700 | 3.9068 | 0.3149 | 0.2967 | |
|
| 0.8673 | 30.0 | 10800 | 3.9034 | 0.3152 | 0.2967 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.3 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
