ht-finbert-cls-v5_ftis_noPretrain_tdso-smlo
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6907
- Accuracy: 0.8868
- Macro F1: 0.7257
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6725
- training_steps: 134500
Training results
Training Loss | Epoch | Step | Accuracy | Validation Loss | Macro F1 |
---|---|---|---|---|---|
44.0634 | 1.0002 | 100 | 0.0595 | 29.9918 | 0.0263 |
19.1118 | 2.0005 | 200 | 0.1467 | 11.5596 | 0.0416 |
8.853 | 4.0002 | 300 | 0.4289 | 7.4544 | 0.1234 |
6.8425 | 5.0004 | 400 | 0.5379 | 6.4752 | 0.1508 |
6.1411 | 7.0002 | 500 | 0.5803 | 5.9858 | 0.1737 |
5.5871 | 8.0004 | 600 | 0.6020 | 5.1220 | 0.1865 |
5.0055 | 10.0001 | 700 | 0.6202 | 4.7439 | 0.1986 |
4.3707 | 11.0004 | 800 | 0.6417 | 3.9550 | 0.2182 |
3.7629 | 13.0001 | 900 | 0.6585 | 3.1581 | 0.2357 |
3.2179 | 14.0004 | 1000 | 0.6677 | 2.7556 | 0.2395 |
2.8272 | 16.0001 | 1100 | 0.6812 | 2.5731 | 0.2645 |
2.4461 | 17.0003 | 1200 | 0.6894 | 2.3137 | 0.2912 |
2.3096 | 19.0001 | 1300 | 0.6994 | 2.2256 | 0.2997 |
2.0965 | 20.0003 | 1400 | 0.7268 | 2.0883 | 0.3325 |
1.9418 | 22.0000 | 1500 | 0.7235 | 2.0158 | 0.3398 |
1.84 | 23.0003 | 1600 | 0.7512 | 1.9096 | 0.3812 |
1.691 | 24.0005 | 1700 | 0.7574 | 1.8696 | 0.3914 |
1.542 | 26.0002 | 1800 | 0.7720 | 1.7970 | 0.4056 |
1.5266 | 27.0005 | 1900 | 0.7500 | 1.9204 | 0.4029 |
1.3604 | 29.0002 | 2000 | 0.7771 | 1.7457 | 0.4448 |
1.3339 | 30.0004 | 2100 | 0.7747 | 1.7573 | 0.4367 |
1.252 | 32.0002 | 2200 | 0.7927 | 1.6343 | 0.4727 |
1.1473 | 33.0004 | 2300 | 0.7869 | 1.6833 | 0.4831 |
1.1114 | 35.0001 | 2400 | 0.7935 | 1.6654 | 0.4990 |
1.0166 | 36.0004 | 2500 | 0.8013 | 1.6315 | 0.5143 |
0.9504 | 38.0001 | 2600 | 0.8041 | 1.6400 | 0.5227 |
0.9445 | 39.0004 | 2700 | 0.8034 | 1.6071 | 0.5461 |
0.8922 | 41.0001 | 2800 | 0.8165 | 1.4921 | 0.5505 |
0.8301 | 42.0003 | 2900 | 0.8204 | 1.5482 | 0.5628 |
0.8187 | 44.0001 | 3000 | 0.8220 | 1.5218 | 0.5662 |
0.7591 | 45.0003 | 3100 | 0.8235 | 1.6234 | 0.5630 |
0.7511 | 47.0000 | 3200 | 0.8258 | 1.5506 | 0.5663 |
0.6991 | 48.0003 | 3300 | 0.8310 | 1.5546 | 0.5789 |
0.68 | 49.0005 | 3400 | 0.8370 | 1.4486 | 0.5925 |
0.6554 | 51.0002 | 3500 | 0.8392 | 1.4459 | 0.5955 |
0.6513 | 52.0005 | 3600 | 0.8387 | 1.4237 | 0.5949 |
0.6294 | 54.0002 | 3700 | 0.8385 | 1.4449 | 0.6036 |
0.5897 | 55.0004 | 3800 | 0.8457 | 1.4372 | 0.6122 |
0.572 | 57.0002 | 3900 | 0.8463 | 1.4642 | 0.6213 |
0.5596 | 58.0004 | 4000 | 0.8438 | 1.5312 | 0.6118 |
0.5365 | 60.0001 | 4100 | 0.8511 | 1.3978 | 0.6273 |
0.5213 | 61.0004 | 4200 | 0.8523 | 1.3690 | 0.6243 |
0.5106 | 63.0001 | 4300 | 0.8526 | 1.4379 | 0.6339 |
0.5022 | 64.0004 | 4400 | 0.8536 | 1.4027 | 0.6378 |
0.4973 | 66.0001 | 4500 | 0.8590 | 1.3383 | 0.6429 |
0.4711 | 67.0003 | 4600 | 0.8616 | 1.4064 | 0.6463 |
0.4569 | 69.0001 | 4700 | 0.8585 | 1.5020 | 0.6435 |
0.447 | 70.0003 | 4800 | 0.8541 | 1.4575 | 0.6329 |
0.4626 | 72.0000 | 4900 | 0.8581 | 1.4142 | 0.6378 |
0.4335 | 73.0003 | 5000 | 0.8646 | 1.3917 | 0.6567 |
0.4313 | 74.0005 | 5100 | 0.8606 | 1.5024 | 0.6607 |
0.4238 | 76.0002 | 5200 | 0.8627 | 1.4333 | 0.6488 |
0.4178 | 77.0005 | 5300 | 0.8604 | 1.4772 | 0.6437 |
0.4187 | 79.0002 | 5400 | 0.8605 | 1.4489 | 0.6585 |
0.3949 | 80.0004 | 5500 | 0.8641 | 1.4555 | 0.6552 |
0.3945 | 82.0002 | 5600 | 0.8659 | 1.4706 | 0.6668 |
0.3932 | 83.0004 | 5700 | 0.8645 | 1.3544 | 0.6617 |
0.3961 | 85.0001 | 5800 | 0.8641 | 1.4833 | 0.6637 |
0.3785 | 86.0004 | 5900 | 0.8711 | 1.4085 | 0.6697 |
0.3756 | 88.0001 | 6000 | 0.8703 | 1.3995 | 0.6750 |
0.3663 | 89.0004 | 6100 | 0.8707 | 1.5094 | 0.6729 |
0.3625 | 91.0001 | 6200 | 0.8709 | 1.5208 | 0.6712 |
0.3717 | 92.0003 | 6300 | 0.8740 | 1.3985 | 0.6826 |
0.3638 | 94.0001 | 6400 | 0.8725 | 1.4122 | 0.6820 |
0.3574 | 95.0003 | 6500 | 0.8645 | 1.5587 | 0.6835 |
0.3506 | 97.0000 | 6600 | 0.8692 | 1.4160 | 0.6771 |
0.3498 | 98.0003 | 6700 | 0.8741 | 1.4029 | 0.6843 |
0.3466 | 99.0005 | 6800 | 0.8735 | 1.4862 | 0.6836 |
0.3366 | 101.0002 | 6900 | 0.8766 | 1.3953 | 0.6864 |
0.3344 | 102.0005 | 7000 | 0.8734 | 1.4884 | 0.6843 |
0.3394 | 104.0002 | 7100 | 0.8740 | 1.4627 | 0.6856 |
0.3335 | 105.0004 | 7200 | 0.8742 | 1.4323 | 0.6883 |
0.3358 | 107.0002 | 7300 | 0.8712 | 1.4419 | 0.6818 |
0.3288 | 108.0004 | 7400 | 0.8717 | 1.3731 | 0.6962 |
0.3183 | 110.0001 | 7500 | 0.8775 | 1.5245 | 0.6928 |
0.3119 | 111.0004 | 7600 | 0.8779 | 1.4321 | 0.6951 |
0.3114 | 113.0001 | 7700 | 0.8794 | 1.4112 | 0.6936 |
0.307 | 114.0004 | 7800 | 0.8792 | 1.4891 | 0.6982 |
0.3103 | 116.0001 | 7900 | 0.8807 | 1.4647 | 0.6997 |
0.3083 | 117.0003 | 8000 | 0.8793 | 1.4318 | 0.6992 |
0.3044 | 119.0001 | 8100 | 0.8722 | 1.5052 | 0.6861 |
0.3071 | 120.0003 | 8200 | 0.8718 | 1.5389 | 0.6892 |
0.3019 | 122.0000 | 8300 | 0.8781 | 1.4060 | 0.7009 |
0.3112 | 123.0003 | 8400 | 0.8765 | 1.4961 | 0.6939 |
0.3137 | 124.0005 | 8500 | 0.8776 | 1.4601 | 0.6921 |
0.2936 | 126.0002 | 8600 | 0.8791 | 1.5031 | 0.6961 |
0.2925 | 127.0005 | 8700 | 0.8816 | 1.4697 | 0.7045 |
0.2889 | 129.0002 | 8800 | 0.8811 | 1.4811 | 0.7056 |
0.2877 | 130.0004 | 8900 | 0.8835 | 1.4229 | 0.7072 |
0.2867 | 132.0002 | 9000 | 0.8810 | 1.5132 | 0.7023 |
0.2846 | 133.0004 | 9100 | 0.8804 | 1.4948 | 0.7003 |
0.2882 | 135.0001 | 9200 | 0.8777 | 1.4862 | 0.6975 |
0.2956 | 136.0004 | 9300 | 0.8750 | 1.5419 | 0.7053 |
0.2893 | 138.0001 | 9400 | 0.8781 | 1.5585 | 0.7006 |
0.2891 | 139.0004 | 9500 | 0.8813 | 1.5358 | 0.7051 |
0.2911 | 141.0001 | 9600 | 0.8724 | 1.5901 | 0.7008 |
0.2867 | 142.0003 | 9700 | 0.8777 | 1.6218 | 0.7004 |
0.2816 | 144.0001 | 9800 | 0.8813 | 1.5292 | 0.7046 |
0.2795 | 145.0003 | 9900 | 0.8809 | 1.5299 | 0.7057 |
0.2771 | 147.0000 | 10000 | 0.8833 | 1.4584 | 0.7060 |
0.2778 | 148.0003 | 10100 | 0.8761 | 1.5843 | 0.7137 |
0.279 | 149.0005 | 10200 | 0.8802 | 1.4920 | 0.7007 |
0.2824 | 151.0002 | 10300 | 0.8752 | 1.6153 | 0.7011 |
0.291 | 152.0005 | 10400 | 0.8796 | 1.5264 | 0.7113 |
0.2968 | 154.0002 | 10500 | 0.8743 | 1.5351 | 0.7047 |
0.2889 | 155.0004 | 10600 | 0.8773 | 1.5191 | 0.6989 |
0.2788 | 157.0002 | 10700 | 0.8752 | 1.5644 | 0.7110 |
0.2719 | 158.0004 | 10800 | 0.8803 | 1.6073 | 0.7054 |
0.2789 | 160.0001 | 10900 | 0.8796 | 1.6489 | 0.7034 |
0.2809 | 161.0004 | 11000 | 0.8806 | 1.4471 | 0.7110 |
0.2713 | 163.0001 | 11100 | 0.8824 | 1.4910 | 0.7125 |
0.271 | 164.0004 | 11200 | 0.8828 | 1.5711 | 0.7121 |
0.2647 | 166.0001 | 11300 | 0.8838 | 1.4951 | 0.7115 |
0.27 | 167.0003 | 11400 | 0.8815 | 1.6220 | 0.7099 |
0.2688 | 169.0001 | 11500 | 0.8854 | 1.5027 | 0.7122 |
0.2652 | 170.0003 | 11600 | 0.8839 | 1.4662 | 0.7119 |
0.27 | 172.0000 | 11700 | 0.8807 | 1.4239 | 0.7080 |
0.2616 | 173.0003 | 11800 | 0.8862 | 1.4642 | 0.7121 |
0.2609 | 174.0005 | 11900 | 0.8847 | 1.4295 | 0.7150 |
0.2608 | 176.0002 | 12000 | 0.8834 | 1.5385 | 0.7123 |
0.2592 | 177.0005 | 12100 | 0.8842 | 1.5430 | 0.7123 |
0.2591 | 179.0002 | 12200 | 0.8845 | 1.5631 | 0.7160 |
0.2605 | 180.0004 | 12300 | 0.8847 | 1.4659 | 0.7147 |
0.2634 | 182.0002 | 12400 | 0.8769 | 1.6519 | 0.6988 |
0.264 | 183.0004 | 12500 | 0.8817 | 1.6334 | 0.7057 |
0.2637 | 185.0001 | 12600 | 0.8794 | 1.6027 | 0.7026 |
0.2595 | 186.0004 | 12700 | 0.8825 | 1.4887 | 0.7109 |
0.2637 | 188.0001 | 12800 | 0.8800 | 1.6395 | 0.7103 |
0.2618 | 189.0004 | 12900 | 0.8827 | 1.4701 | 0.7161 |
0.2543 | 191.0001 | 13000 | 0.8828 | 1.6473 | 0.7121 |
0.2535 | 192.0003 | 13100 | 0.8830 | 1.5540 | 0.7155 |
0.2519 | 194.0001 | 13200 | 0.8844 | 1.5464 | 0.7151 |
0.2554 | 195.0003 | 13300 | 0.8836 | 1.6787 | 0.7160 |
0.2559 | 197.0000 | 13400 | 0.8818 | 1.6256 | 0.7106 |
0.26 | 198.0003 | 13500 | 0.8809 | 1.5540 | 0.7104 |
0.2511 | 199.0005 | 13600 | 0.8833 | 1.4974 | 0.7131 |
0.2501 | 201.0002 | 13700 | 0.8865 | 1.5228 | 0.7141 |
0.2485 | 202.0005 | 13800 | 0.8820 | 1.5597 | 0.7092 |
0.2511 | 204.0002 | 13900 | 0.8853 | 1.5517 | 0.7122 |
0.2577 | 205.0004 | 14000 | 0.8806 | 1.5872 | 0.7048 |
0.2649 | 207.0002 | 14100 | 0.8810 | 1.6096 | 0.7052 |
0.2549 | 208.0004 | 14200 | 0.8695 | 1.9520 | 0.6949 |
0.2562 | 210.0001 | 14300 | 0.8872 | 1.5661 | 0.7184 |
0.2473 | 211.0004 | 14400 | 0.8858 | 1.5051 | 0.7169 |
0.2449 | 213.0001 | 14500 | 0.8837 | 1.5391 | 0.7173 |
0.2424 | 214.0004 | 14600 | 0.8850 | 1.6149 | 0.7188 |
0.2415 | 216.0001 | 14700 | 0.8871 | 1.4451 | 0.7187 |
0.2459 | 217.0003 | 14800 | 0.8856 | 1.5276 | 0.7181 |
0.2571 | 219.0001 | 14900 | 0.8822 | 1.4217 | 0.7096 |
0.2498 | 220.0003 | 15000 | 0.8737 | 1.6880 | 0.6961 |
0.2499 | 222.0000 | 15100 | 0.8840 | 1.6290 | 0.7171 |
0.254 | 223.0003 | 15200 | 0.8797 | 1.5733 | 0.7120 |
0.2424 | 224.0005 | 15300 | 0.8865 | 1.5997 | 0.7225 |
0.2441 | 226.0002 | 15400 | 0.8838 | 1.4853 | 0.7178 |
0.2394 | 227.0005 | 15500 | 0.8884 | 1.5919 | 0.7195 |
0.2402 | 229.0002 | 15600 | 0.8807 | 1.6230 | 0.7179 |
0.2452 | 230.0004 | 15700 | 0.8851 | 1.6242 | 0.7149 |
0.2432 | 232.0002 | 15800 | 0.8855 | 1.5234 | 0.7186 |
0.2388 | 233.0004 | 15900 | 0.8870 | 1.5284 | 0.7253 |
0.2422 | 235.0001 | 16000 | 0.8859 | 1.4673 | 0.7172 |
0.2395 | 236.0004 | 16100 | 0.8867 | 1.5108 | 0.7183 |
0.2401 | 238.0001 | 16200 | 0.8756 | 1.6898 | 0.7047 |
0.2362 | 239.0004 | 16300 | 0.8841 | 1.5884 | 0.7187 |
0.2379 | 241.0001 | 16400 | 0.8825 | 1.5279 | 0.7214 |
0.2369 | 242.0003 | 16500 | 0.8847 | 1.5857 | 0.7217 |
0.233 | 244.0001 | 16600 | 0.8859 | 1.4943 | 0.7210 |
0.234 | 245.0003 | 16700 | 0.8871 | 1.5278 | 0.7258 |
0.2394 | 247.0000 | 16800 | 0.8818 | 1.6111 | 0.7102 |
0.2376 | 248.0003 | 16900 | 0.8854 | 1.5761 | 0.7199 |
0.2345 | 249.0005 | 17000 | 0.8855 | 1.5248 | 0.7216 |
0.2329 | 251.0002 | 17100 | 0.8861 | 1.6121 | 0.7188 |
0.2332 | 252.0005 | 17200 | 0.8854 | 1.4773 | 0.7170 |
0.2338 | 254.0002 | 17300 | 0.8872 | 1.6114 | 0.7232 |
0.2323 | 255.0004 | 17400 | 0.8863 | 1.5735 | 0.7240 |
0.2342 | 257.0002 | 17500 | 0.8849 | 1.5205 | 0.7242 |
0.2369 | 258.0004 | 17600 | 0.8857 | 1.6007 | 0.7211 |
0.2365 | 260.0001 | 17700 | 0.8840 | 1.6237 | 0.7195 |
0.2318 | 261.0004 | 17800 | 0.8868 | 1.5519 | 0.7264 |
0.2292 | 263.0001 | 17900 | 0.8845 | 1.5918 | 0.7134 |
0.2319 | 264.0004 | 18000 | 0.8813 | 1.6651 | 0.7116 |
0.2326 | 266.0001 | 18100 | 0.8829 | 1.6115 | 0.7139 |
0.2305 | 267.0003 | 18200 | 0.8868 | 1.5719 | 0.7194 |
0.2286 | 269.0001 | 18300 | 0.8851 | 1.4951 | 0.7140 |
0.2242 | 270.0003 | 18400 | 0.8835 | 1.5776 | 0.7204 |
0.2293 | 272.0000 | 18500 | 0.8838 | 1.5407 | 0.7201 |
0.2309 | 273.0003 | 18600 | 0.8832 | 1.5878 | 0.7175 |
0.23 | 274.0005 | 18700 | 0.8865 | 1.5139 | 0.7254 |
0.2354 | 276.0002 | 18800 | 0.8865 | 1.6560 | 0.7153 |
0.2241 | 277.0005 | 18900 | 0.8911 | 1.5799 | 0.7261 |
0.2244 | 279.0002 | 19000 | 0.8891 | 1.6104 | 0.7239 |
0.2224 | 280.0004 | 19100 | 0.8906 | 1.6413 | 0.7237 |
0.2269 | 282.0002 | 19200 | 0.8902 | 1.6174 | 0.7210 |
0.2269 | 283.0004 | 19300 | 0.8883 | 1.6139 | 0.7197 |
0.2232 | 285.0001 | 19400 | 0.8866 | 1.6865 | 0.7243 |
0.2237 | 286.0004 | 19500 | 0.8863 | 1.7122 | 0.7167 |
0.2227 | 288.0001 | 19600 | 0.8891 | 1.5793 | 0.7260 |
0.227 | 289.0004 | 19700 | 0.8872 | 1.7025 | 0.7202 |
0.2258 | 291.0001 | 19800 | 0.8864 | 1.5498 | 0.7242 |
Framework versions
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.1
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