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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