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library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ht-finbert-cls-v5_ftis_noPretrain_tdso
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. -->
# ht-finbert-cls-v5_ftis_noPretrain_tdso
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7215
- Accuracy: 0.8960
- Macro F1: 0.7522
## 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 | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:--------:|:-----:|:---------------:|:--------:|:--------:|
| 54.1267 | 1.0002 | 100 | 42.4539 | 0.0693 | 0.0340 |
| 20.8339 | 2.0005 | 200 | 75.1536 | 0.1443 | 0.0557 |
| 8.6862 | 4.0002 | 300 | 105.8607 | 0.4400 | 0.1239 |
| 6.9579 | 5.0004 | 400 | 132.3561 | 0.5393 | 0.1544 |
| 6.3137 | 7.0002 | 500 | 163.3538 | 0.5817 | 0.1790 |
| 5.7884 | 8.0004 | 600 | 149.1104 | 0.6043 | 0.1961 |
| 5.238 | 10.0001 | 700 | 149.0267 | 0.6232 | 0.2087 |
| 4.6053 | 11.0004 | 800 | 122.6960 | 0.6450 | 0.2278 |
| 3.9789 | 13.0001 | 900 | 88.3309 | 0.6608 | 0.2490 |
| 3.4193 | 14.0004 | 1000 | 70.7087 | 0.6655 | 0.2566 |
| 2.9712 | 16.0001 | 1100 | 53.3170 | 0.6815 | 0.2796 |
| 2.5305 | 17.0003 | 1200 | 36.2476 | 0.7014 | 0.3222 |
| 2.3318 | 19.0001 | 1300 | 26.5832 | 0.7201 | 0.3406 |
| 2.04 | 20.0003 | 1400 | 19.7882 | 0.7461 | 0.3732 |
| 1.8635 | 22.0000 | 1500 | 18.2478 | 0.7632 | 0.4002 |
| 1.7301 | 23.0003 | 1600 | 12.3389 | 0.7779 | 0.4322 |
| 1.5551 | 24.0005 | 1700 | 9.3268 | 0.7815 | 0.4542 |
| 1.4405 | 26.0002 | 1800 | 9.2790 | 0.7954 | 0.4642 |
| 1.4124 | 27.0005 | 1900 | 7.5042 | 0.7902 | 0.4744 |
| 1.265 | 29.0002 | 2000 | 6.2492 | 0.7913 | 0.4995 |
| 1.2012 | 30.0004 | 2100 | 6.2005 | 0.8002 | 0.5169 |
| 1.1371 | 32.0002 | 2200 | 5.8517 | 0.8080 | 0.5313 |
| 1.0514 | 33.0004 | 2300 | 5.5730 | 0.8110 | 0.5412 |
| 1.0293 | 35.0001 | 2400 | 4.6457 | 0.8153 | 0.5503 |
| 0.9164 | 36.0004 | 2500 | 4.8986 | 0.8244 | 0.5791 |
| 0.8696 | 38.0001 | 2600 | 5.2707 | 0.8242 | 0.5853 |
| 0.8678 | 39.0004 | 2700 | 5.4791 | 0.8199 | 0.5702 |
| 0.817 | 41.0001 | 2800 | 5.8519 | 0.8276 | 0.5881 |
| 0.7781 | 42.0003 | 2900 | 5.7369 | 0.8361 | 0.6065 |
| 0.7561 | 44.0001 | 3000 | 6.8293 | 0.8322 | 0.5961 |
| 0.7016 | 45.0003 | 3100 | 6.5144 | 0.8343 | 0.6086 |
| 0.7073 | 47.0000 | 3200 | 6.8592 | 0.8387 | 0.6133 |
| 0.6632 | 48.0003 | 3300 | 6.8253 | 0.8402 | 0.6156 |
| 0.6415 | 49.0005 | 3400 | 7.3926 | 0.8440 | 0.6237 |
| 0.6284 | 51.0002 | 3500 | 8.3232 | 0.8490 | 0.6307 |
| 0.6271 | 52.0005 | 3600 | 8.3714 | 0.8462 | 0.6354 |
| 0.6024 | 54.0002 | 3700 | 7.8233 | 0.8496 | 0.6374 |
| 0.5716 | 55.0004 | 3800 | 8.6780 | 0.8491 | 0.6344 |
| 0.5656 | 57.0002 | 3900 | 8.8122 | 0.8523 | 0.6446 |
| 0.5519 | 58.0004 | 4000 | 7.9019 | 0.8485 | 0.6443 |
| 0.5383 | 60.0001 | 4100 | 8.2911 | 0.8525 | 0.6447 |
| 0.5225 | 61.0004 | 4200 | 8.9799 | 0.8580 | 0.6595 |
| 0.5166 | 63.0001 | 4300 | 9.7411 | 0.8562 | 0.6523 |
| 0.5067 | 64.0004 | 4400 | 10.2261 | 0.8612 | 0.6616 |
| 0.5083 | 66.0001 | 4500 | 10.1360 | 0.8607 | 0.6669 |
| 0.4822 | 67.0003 | 4600 | 10.8119 | 0.8633 | 0.6696 |
| 0.478 | 69.0001 | 4700 | 12.0300 | 0.8601 | 0.6650 |
| 0.468 | 70.0003 | 4800 | 10.7509 | 0.8601 | 0.6679 |
| 0.4786 | 72.0000 | 4900 | 11.1814 | 0.8606 | 0.6688 |
| 0.4542 | 73.0003 | 5000 | 11.7393 | 0.8686 | 0.6788 |
| 0.4471 | 74.0005 | 5100 | 11.8721 | 0.8661 | 0.6778 |
| 0.4425 | 76.0002 | 5200 | 12.2049 | 0.8681 | 0.6829 |
| 0.4378 | 77.0005 | 5300 | 10.3071 | 0.8684 | 0.6814 |
| 0.441 | 79.0002 | 5400 | 12.7758 | 0.8656 | 0.6875 |
| 0.4218 | 80.0004 | 5500 | 13.9862 | 0.8695 | 0.6838 |
| 0.4237 | 82.0002 | 5600 | 11.4036 | 0.8681 | 0.6824 |
| 0.4261 | 83.0004 | 5700 | 12.3810 | 0.8685 | 0.6849 |
| 0.4189 | 85.0001 | 5800 | 10.9566 | 0.8703 | 0.6840 |
| 0.4163 | 86.0004 | 5900 | 11.9092 | 0.8658 | 0.6862 |
| 0.4062 | 88.0001 | 6000 | 10.6187 | 0.8748 | 0.7032 |
| 0.4054 | 89.0004 | 6100 | 11.1740 | 0.8753 | 0.6960 |
| 0.3989 | 91.0001 | 6200 | 10.6474 | 0.8748 | 0.6953 |
| 0.4013 | 92.0003 | 6300 | 11.6176 | 0.8754 | 0.7027 |
| 0.3905 | 94.0001 | 6400 | 10.2454 | 0.8753 | 0.7045 |
| 0.3826 | 95.0003 | 6500 | 12.9530 | 0.8729 | 0.6963 |
| 0.3971 | 97.0000 | 6600 | 11.5349 | 0.8716 | 0.6941 |
| 0.3878 | 98.0003 | 6700 | 10.0413 | 0.8744 | 0.7073 |
| 0.3875 | 99.0005 | 6800 | 9.9055 | 0.8734 | 0.7015 |
| 0.3761 | 101.0002 | 6900 | 10.3418 | 0.8789 | 0.7061 |
| 0.3774 | 102.0005 | 7000 | 10.1067 | 0.8806 | 0.7090 |
| 0.3825 | 104.0002 | 7100 | 8.3686 | 0.8806 | 0.7144 |
| 0.3876 | 105.0004 | 7200 | 6.3883 | 0.8724 | 0.6980 |
| 0.3852 | 107.0002 | 7300 | 9.0841 | 0.8739 | 0.7034 |
| 0.3767 | 108.0004 | 7400 | 7.3012 | 0.8813 | 0.7106 |
| 0.3718 | 110.0001 | 7500 | 8.5764 | 0.8828 | 0.7159 |
| 0.3696 | 111.0004 | 7600 | 8.4104 | 0.8832 | 0.7154 |
| 0.3759 | 113.0001 | 7700 | 8.6043 | 0.8832 | 0.7155 |
| 0.3668 | 114.0004 | 7800 | 7.4298 | 0.8847 | 0.7246 |
| 0.3684 | 116.0001 | 7900 | 9.5902 | 0.8848 | 0.7244 |
| 0.363 | 117.0003 | 8000 | 6.8931 | 0.8838 | 0.7222 |
| 0.357 | 119.0001 | 8100 | 6.9210 | 0.8831 | 0.7257 |
| 0.3575 | 120.0003 | 8200 | 5.8508 | 0.8858 | 0.7287 |
| 0.35 | 122.0000 | 8300 | 6.6739 | 0.8851 | 0.7277 |
| 0.3518 | 123.0003 | 8400 | 6.6337 | 0.8850 | 0.7269 |
| 0.3539 | 124.0005 | 8500 | 6.4691 | 0.8851 | 0.7266 |
| 0.3478 | 126.0002 | 8600 | 5.6397 | 0.8865 | 0.7300 |
| 0.3451 | 127.0005 | 8700 | 5.6356 | 0.8880 | 0.7322 |
| 0.3415 | 129.0002 | 8800 | 4.4881 | 0.8871 | 0.7312 |
| 0.3425 | 130.0004 | 8900 | 4.5283 | 0.8854 | 0.7296 |
| 0.3484 | 132.0002 | 9000 | 4.5845 | 0.8811 | 0.7249 |
| 0.3767 | 133.0004 | 9100 | 4.6531 | 0.8795 | 0.7151 |
| 0.3704 | 135.0001 | 9200 | 3.2323 | 0.8747 | 0.7109 |
| 0.3663 | 136.0004 | 9300 | 4.3291 | 0.8773 | 0.7187 |
| 0.3553 | 138.0001 | 9400 | 3.9327 | 0.8841 | 0.7259 |
| 0.3423 | 139.0004 | 9500 | 4.4498 | 0.8860 | 0.7305 |
| 0.3433 | 141.0001 | 9600 | 4.7134 | 0.8794 | 0.7298 |
| 0.3424 | 142.0003 | 9700 | 4.5364 | 0.8863 | 0.7271 |
| 0.3576 | 144.0001 | 9800 | 3.2682 | 0.8843 | 0.7313 |
| 0.3422 | 145.0003 | 9900 | 2.8321 | 0.8844 | 0.7296 |
| 0.3412 | 147.0000 | 10000 | 4.3759 | 0.8890 | 0.7307 |
| 0.349 | 148.0003 | 10100 | 3.9332 | 0.8851 | 0.7280 |
| 0.3391 | 149.0005 | 10200 | 4.5327 | 0.8893 | 0.7355 |
| 0.3335 | 151.0002 | 10300 | 4.9665 | 0.8898 | 0.7368 |
| 0.3369 | 152.0005 | 10400 | 3.4262 | 0.8880 | 0.7363 |
| 0.3314 | 154.0002 | 10500 | 3.5618 | 0.8893 | 0.7368 |
| 0.3277 | 155.0004 | 10600 | 3.4955 | 0.8914 | 0.7393 |
| 0.326 | 157.0002 | 10700 | 3.0240 | 0.8900 | 0.7380 |
| 0.3266 | 158.0004 | 10800 | 2.4971 | 0.8895 | 0.7372 |
| 0.3254 | 160.0001 | 10900 | 2.9598 | 0.8879 | 0.7346 |
| 0.3249 | 161.0004 | 11000 | 3.1897 | 0.8910 | 0.7416 |
| 0.3247 | 163.0001 | 11100 | 3.0436 | 0.8897 | 0.7377 |
| 0.331 | 164.0004 | 11200 | 3.0063 | 0.8884 | 0.7356 |
| 0.3372 | 166.0001 | 11300 | 3.3399 | 0.8830 | 0.7247 |
| 0.3365 | 167.0003 | 11400 | 3.2443 | 0.8871 | 0.7314 |
| 0.3404 | 169.0001 | 11500 | 2.7850 | 0.8745 | 0.7107 |
| 0.3669 | 170.0003 | 11600 | 2.0442 | 0.8828 | 0.7278 |
| 0.3399 | 172.0000 | 11700 | 2.4131 | 0.8859 | 0.7352 |
| 0.33 | 173.0003 | 11800 | 2.5376 | 0.8850 | 0.7363 |
| 0.3209 | 174.0005 | 11900 | 2.9024 | 0.8932 | 0.7460 |
| 0.3181 | 176.0002 | 12000 | 3.0534 | 0.8935 | 0.7446 |
| 0.3167 | 177.0005 | 12100 | 2.8713 | 0.8931 | 0.7458 |
| 0.3149 | 179.0002 | 12200 | 3.1409 | 0.8911 | 0.7397 |
| 0.3168 | 180.0004 | 12300 | 2.7827 | 0.8927 | 0.7423 |
| 0.3154 | 182.0002 | 12400 | 2.9169 | 0.8938 | 0.7436 |
| 0.3143 | 183.0004 | 12500 | 2.7046 | 0.8927 | 0.7427 |
| 0.3175 | 185.0001 | 12600 | 3.0517 | 0.8904 | 0.7388 |
| 0.3473 | 186.0004 | 12700 | 2.5254 | 0.8668 | 0.7191 |
| 0.373 | 188.0001 | 12800 | 1.9765 | 0.8802 | 0.7184 |
| 0.335 | 189.0004 | 12900 | 2.3713 | 0.8882 | 0.7289 |
| 0.3308 | 191.0001 | 13000 | 2.3606 | 0.8891 | 0.7436 |
| 0.3272 | 192.0003 | 13100 | 2.3548 | 0.8907 | 0.7420 |
| 0.3138 | 194.0001 | 13200 | 2.3770 | 0.8858 | 0.7389 |
| 0.3148 | 195.0003 | 13300 | 2.4873 | 0.8950 | 0.7485 |
| 0.3104 | 197.0000 | 13400 | 2.6665 | 0.8949 | 0.7487 |
| 0.308 | 198.0003 | 13500 | 2.7137 | 0.8960 | 0.7522 |
| 0.3089 | 199.0005 | 13600 | 2.8533 | 0.8954 | 0.7502 |
| 0.3083 | 201.0002 | 13700 | 2.7738 | 0.8949 | 0.7473 |
| 0.3058 | 202.0005 | 13800 | 2.8489 | 0.8945 | 0.7490 |
| 0.3171 | 204.0002 | 13900 | 2.4588 | 0.8926 | 0.7478 |
| 0.312 | 205.0004 | 14000 | 2.1815 | 0.8930 | 0.7438 |
| 0.3102 | 207.0002 | 14100 | 2.3426 | 0.8928 | 0.7459 |
| 0.3074 | 208.0004 | 14200 | 2.4680 | 0.8914 | 0.7424 |
| 0.305 | 210.0001 | 14300 | 2.3430 | 0.8940 | 0.7440 |
| 0.3055 | 211.0004 | 14400 | 2.6109 | 0.8942 | 0.7492 |
| 0.3065 | 213.0001 | 14500 | 3.2677 | 0.8936 | 0.7400 |
| 0.3038 | 214.0004 | 14600 | 2.6056 | 0.8951 | 0.7500 |
| 0.3022 | 216.0001 | 14700 | 3.7801 | 0.8929 | 0.7465 |
| 0.3045 | 217.0003 | 14800 | 3.1071 | 0.8881 | 0.7453 |
| 0.3045 | 219.0001 | 14900 | 3.9928 | 0.8911 | 0.7454 |
| 0.3066 | 220.0003 | 15000 | 2.8048 | 0.8893 | 0.7415 |
| 0.3083 | 222.0000 | 15100 | 2.8299 | 0.8808 | 0.7369 |
| 0.3089 | 223.0003 | 15200 | 2.6550 | 0.8845 | 0.7384 |
| 0.3122 | 224.0005 | 15300 | 2.8834 | 0.8858 | 0.7403 |
| 0.3318 | 226.0002 | 15400 | 1.9369 | 0.8823 | 0.7328 |
| 0.3205 | 227.0005 | 15500 | 2.7083 | 0.8886 | 0.7431 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.1
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