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---
base_model: ProsusAI/finbert
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finBert_SA_20e
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. -->
# finBert_SA_20e
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3343
- Accuracy: 0.8882
- F1: 0.8878
- Precision: 0.8875
- Recall: 0.8889
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2897 | 0.1323 | 50 | 0.5582 | 0.7846 | 0.7828 | 0.7861 | 0.7858 |
| 0.5094 | 0.2646 | 100 | 0.4590 | 0.8304 | 0.8249 | 0.8329 | 0.8315 |
| 0.4218 | 0.3968 | 150 | 0.4386 | 0.8467 | 0.8441 | 0.8473 | 0.8476 |
| 0.3986 | 0.5291 | 200 | 0.3985 | 0.8508 | 0.8487 | 0.8505 | 0.8516 |
| 0.3866 | 0.6614 | 250 | 0.3847 | 0.8553 | 0.8563 | 0.8584 | 0.8554 |
| 0.391 | 0.7937 | 300 | 0.3528 | 0.8707 | 0.8703 | 0.8699 | 0.8713 |
| 0.3739 | 0.9259 | 350 | 0.3515 | 0.8697 | 0.8691 | 0.8688 | 0.8702 |
| 0.3499 | 1.0582 | 400 | 0.3539 | 0.8656 | 0.8670 | 0.8696 | 0.8659 |
| 0.3211 | 1.1905 | 450 | 0.3659 | 0.8728 | 0.8719 | 0.8718 | 0.8734 |
| 0.312 | 1.3228 | 500 | 0.3514 | 0.8726 | 0.8721 | 0.8722 | 0.8730 |
| 0.3001 | 1.4550 | 550 | 0.3325 | 0.8738 | 0.8749 | 0.8765 | 0.8741 |
| 0.2968 | 1.5873 | 600 | 0.3355 | 0.8756 | 0.8751 | 0.8752 | 0.8761 |
| 0.2893 | 1.7196 | 650 | 0.3639 | 0.8718 | 0.8700 | 0.8717 | 0.8725 |
| 0.3084 | 1.8519 | 700 | 0.3285 | 0.8817 | 0.8812 | 0.8810 | 0.8822 |
| 0.2838 | 1.9841 | 750 | 0.3390 | 0.8833 | 0.8833 | 0.8832 | 0.8838 |
| 0.21 | 2.1164 | 800 | 0.3772 | 0.8809 | 0.8808 | 0.8810 | 0.8815 |
| 0.2195 | 2.2487 | 850 | 0.3524 | 0.8820 | 0.8808 | 0.8814 | 0.8827 |
| 0.2109 | 2.3810 | 900 | 0.3530 | 0.8788 | 0.8776 | 0.8785 | 0.8794 |
| 0.2131 | 2.5132 | 950 | 0.3497 | 0.8838 | 0.8843 | 0.8846 | 0.8842 |
| 0.2028 | 2.6455 | 1000 | 0.3466 | 0.8876 | 0.8872 | 0.8870 | 0.8881 |
| 0.225 | 2.7778 | 1050 | 0.3544 | 0.8786 | 0.8804 | 0.8853 | 0.8786 |
| 0.212 | 2.9101 | 1100 | 0.3528 | 0.8850 | 0.8860 | 0.8878 | 0.8851 |
| 0.1993 | 3.0423 | 1150 | 0.3674 | 0.8896 | 0.8906 | 0.8923 | 0.8898 |
| 0.1349 | 3.1746 | 1200 | 0.3870 | 0.8904 | 0.8906 | 0.8906 | 0.8908 |
| 0.1371 | 3.3069 | 1250 | 0.3886 | 0.8876 | 0.8865 | 0.8871 | 0.8882 |
| 0.1587 | 3.4392 | 1300 | 0.3873 | 0.8857 | 0.8843 | 0.8856 | 0.8865 |
| 0.1406 | 3.5714 | 1350 | 0.4001 | 0.8833 | 0.8839 | 0.8852 | 0.8834 |
| 0.1884 | 3.7037 | 1400 | 0.3576 | 0.8861 | 0.8867 | 0.8875 | 0.8864 |
| 0.1539 | 3.8360 | 1450 | 0.3761 | 0.8927 | 0.8919 | 0.8922 | 0.8933 |
| 0.1426 | 3.9683 | 1500 | 0.3774 | 0.8902 | 0.8908 | 0.8917 | 0.8905 |
| 0.106 | 4.1005 | 1550 | 0.4692 | 0.8823 | 0.8836 | 0.8867 | 0.8825 |
| 0.0955 | 4.2328 | 1600 | 0.4351 | 0.8891 | 0.8891 | 0.8890 | 0.8894 |
| 0.1062 | 4.3651 | 1650 | 0.4300 | 0.8896 | 0.8889 | 0.8891 | 0.8901 |
| 0.1204 | 4.4974 | 1700 | 0.4308 | 0.8872 | 0.8862 | 0.8865 | 0.8878 |
| 0.0932 | 4.6296 | 1750 | 0.4403 | 0.8902 | 0.8905 | 0.8905 | 0.8906 |
| 0.1163 | 4.7619 | 1800 | 0.4199 | 0.8961 | 0.8954 | 0.8957 | 0.8966 |
| 0.1225 | 4.8942 | 1850 | 0.4211 | 0.8855 | 0.8844 | 0.8851 | 0.8861 |
| 0.1087 | 5.0265 | 1900 | 0.4421 | 0.8927 | 0.8929 | 0.8928 | 0.8931 |
| 0.0693 | 5.1587 | 1950 | 0.5357 | 0.8842 | 0.8847 | 0.8857 | 0.8843 |
| 0.0727 | 5.2910 | 2000 | 0.5088 | 0.8864 | 0.8862 | 0.8867 | 0.8867 |
| 0.0874 | 5.4233 | 2050 | 0.4516 | 0.8940 | 0.8943 | 0.8943 | 0.8943 |
| 0.0707 | 5.5556 | 2100 | 0.4983 | 0.8934 | 0.8935 | 0.8933 | 0.8938 |
| 0.0745 | 5.6878 | 2150 | 0.4946 | 0.8901 | 0.8903 | 0.8902 | 0.8905 |
| 0.0706 | 5.8201 | 2200 | 0.5088 | 0.8964 | 0.8958 | 0.8960 | 0.8970 |
| 0.0935 | 5.9524 | 2250 | 0.4664 | 0.8923 | 0.8917 | 0.8917 | 0.8927 |
| 0.0649 | 6.0847 | 2300 | 0.5200 | 0.8896 | 0.8899 | 0.8902 | 0.8897 |
| 0.0528 | 6.2169 | 2350 | 0.5412 | 0.8937 | 0.8941 | 0.8944 | 0.8939 |
| 0.0638 | 6.3492 | 2400 | 0.5140 | 0.8968 | 0.8962 | 0.8964 | 0.8973 |
| 0.0639 | 6.4815 | 2450 | 0.5087 | 0.8960 | 0.8957 | 0.8955 | 0.8964 |
| 0.0597 | 6.6138 | 2500 | 0.5272 | 0.8958 | 0.8951 | 0.8952 | 0.8963 |
| 0.0507 | 6.7460 | 2550 | 0.5685 | 0.8958 | 0.8950 | 0.8958 | 0.8965 |
| 0.062 | 6.8783 | 2600 | 0.5272 | 0.8944 | 0.8942 | 0.8940 | 0.8949 |
| 0.0604 | 7.0106 | 2650 | 0.5083 | 0.8988 | 0.8990 | 0.8989 | 0.8991 |
| 0.044 | 7.1429 | 2700 | 0.5663 | 0.8951 | 0.8959 | 0.8973 | 0.8953 |
| 0.0471 | 7.2751 | 2750 | 0.5610 | 0.8963 | 0.8964 | 0.8963 | 0.8967 |
| 0.0526 | 7.4074 | 2800 | 0.5725 | 0.8930 | 0.8937 | 0.8944 | 0.8932 |
| 0.0487 | 7.5397 | 2850 | 0.5943 | 0.8982 | 0.8981 | 0.8982 | 0.8986 |
| 0.0548 | 7.6720 | 2900 | 0.5556 | 0.9001 | 0.9003 | 0.9004 | 0.9004 |
| 0.0461 | 7.8042 | 2950 | 0.5452 | 0.9007 | 0.9004 | 0.9003 | 0.9011 |
| 0.041 | 7.9365 | 3000 | 0.5505 | 0.8978 | 0.8971 | 0.8974 | 0.8984 |
| 0.0388 | 8.0688 | 3050 | 0.6078 | 0.8981 | 0.8971 | 0.8980 | 0.8987 |
| 0.0318 | 8.2011 | 3100 | 0.6324 | 0.8947 | 0.8950 | 0.8953 | 0.8949 |
| 0.033 | 8.3333 | 3150 | 0.6211 | 0.8953 | 0.8956 | 0.8957 | 0.8956 |
| 0.0459 | 8.4656 | 3200 | 0.6161 | 0.8988 | 0.8990 | 0.8992 | 0.8992 |
| 0.0462 | 8.5979 | 3250 | 0.5925 | 0.8953 | 0.8954 | 0.8952 | 0.8956 |
| 0.0321 | 8.7302 | 3300 | 0.6416 | 0.8920 | 0.8914 | 0.8916 | 0.8925 |
| 0.0452 | 8.8624 | 3350 | 0.5777 | 0.8968 | 0.8967 | 0.8966 | 0.8972 |
| 0.0468 | 8.9947 | 3400 | 0.5743 | 0.8959 | 0.8964 | 0.8970 | 0.8962 |
| 0.0294 | 9.1270 | 3450 | 0.5977 | 0.8996 | 0.8992 | 0.8991 | 0.9000 |
| 0.0373 | 9.2593 | 3500 | 0.6051 | 0.8935 | 0.8944 | 0.8959 | 0.8937 |
| 0.035 | 9.3915 | 3550 | 0.6218 | 0.8986 | 0.8983 | 0.8982 | 0.8990 |
| 0.0304 | 9.5238 | 3600 | 0.6784 | 0.8926 | 0.8931 | 0.8938 | 0.8927 |
| 0.0464 | 9.6561 | 3650 | 0.6534 | 0.8968 | 0.8954 | 0.8973 | 0.8974 |
| 0.031 | 9.7884 | 3700 | 0.5966 | 0.8987 | 0.8986 | 0.8984 | 0.8990 |
| 0.0252 | 9.9206 | 3750 | 0.6065 | 0.8991 | 0.8988 | 0.8986 | 0.8994 |
| 0.0385 | 10.0529 | 3800 | 0.6120 | 0.8953 | 0.8945 | 0.8948 | 0.8958 |
| 0.0141 | 10.1852 | 3850 | 0.6305 | 0.8967 | 0.8971 | 0.8975 | 0.8969 |
| 0.0325 | 10.3175 | 3900 | 0.6163 | 0.9006 | 0.9002 | 0.9001 | 0.9010 |
| 0.0188 | 10.4497 | 3950 | 0.6286 | 0.9005 | 0.9002 | 0.9000 | 0.9009 |
| 0.0153 | 10.5820 | 4000 | 0.6769 | 0.8985 | 0.8988 | 0.8989 | 0.8987 |
| 0.03 | 10.7143 | 4050 | 0.6473 | 0.8970 | 0.8969 | 0.8969 | 0.8973 |
| 0.0286 | 10.8466 | 4100 | 0.6644 | 0.8991 | 0.8993 | 0.8994 | 0.8993 |
| 0.0311 | 10.9788 | 4150 | 0.6566 | 0.8989 | 0.8992 | 0.8994 | 0.8992 |
| 0.024 | 11.1111 | 4200 | 0.6562 | 0.9007 | 0.9011 | 0.9016 | 0.9010 |
| 0.0241 | 11.2434 | 4250 | 0.6290 | 0.9007 | 0.9008 | 0.9007 | 0.9011 |
| 0.0094 | 11.3757 | 4300 | 0.6739 | 0.9019 | 0.9015 | 0.9015 | 0.9023 |
| 0.0184 | 11.5079 | 4350 | 0.6819 | 0.8992 | 0.8994 | 0.8994 | 0.8995 |
| 0.017 | 11.6402 | 4400 | 0.6907 | 0.9037 | 0.9034 | 0.9033 | 0.9041 |
| 0.0275 | 11.7725 | 4450 | 0.6652 | 0.8983 | 0.8985 | 0.8984 | 0.8986 |
| 0.0138 | 11.9048 | 4500 | 0.6829 | 0.9013 | 0.9009 | 0.9008 | 0.9017 |
| 0.0173 | 12.0370 | 4550 | 0.6910 | 0.9016 | 0.9014 | 0.9012 | 0.9019 |
| 0.0129 | 12.1693 | 4600 | 0.7063 | 0.9018 | 0.9018 | 0.9017 | 0.9022 |
| 0.0173 | 12.3016 | 4650 | 0.7244 | 0.9015 | 0.9011 | 0.9011 | 0.9019 |
| 0.0223 | 12.4339 | 4700 | 0.7097 | 0.9013 | 0.9012 | 0.9010 | 0.9017 |
| 0.0179 | 12.5661 | 4750 | 0.7458 | 0.8967 | 0.8964 | 0.8963 | 0.8970 |
| 0.0162 | 12.6984 | 4800 | 0.7249 | 0.8987 | 0.8989 | 0.8988 | 0.8990 |
| 0.0144 | 12.8307 | 4850 | 0.7354 | 0.8990 | 0.8990 | 0.8987 | 0.8993 |
| 0.0189 | 12.9630 | 4900 | 0.7119 | 0.8999 | 0.8996 | 0.8994 | 0.9003 |
| 0.0097 | 13.0952 | 4950 | 0.7425 | 0.9012 | 0.9011 | 0.9009 | 0.9016 |
| 0.0122 | 13.2275 | 5000 | 0.7447 | 0.8991 | 0.8990 | 0.8990 | 0.8995 |
| 0.0171 | 13.3598 | 5050 | 0.7508 | 0.8980 | 0.8983 | 0.8986 | 0.8981 |
| 0.013 | 13.4921 | 5100 | 0.7380 | 0.9015 | 0.9016 | 0.9014 | 0.9017 |
| 0.0141 | 13.6243 | 5150 | 0.7380 | 0.9025 | 0.9026 | 0.9024 | 0.9028 |
| 0.0092 | 13.7566 | 5200 | 0.7636 | 0.8987 | 0.8992 | 0.8998 | 0.8989 |
| 0.0151 | 13.8889 | 5250 | 0.7474 | 0.9004 | 0.9008 | 0.9009 | 0.9006 |
| 0.0064 | 14.0212 | 5300 | 0.7812 | 0.8989 | 0.8992 | 0.8993 | 0.8992 |
| 0.0112 | 14.1534 | 5350 | 0.7392 | 0.9025 | 0.9022 | 0.9020 | 0.9029 |
| 0.008 | 14.2857 | 5400 | 0.7737 | 0.9035 | 0.9029 | 0.9034 | 0.9041 |
| 0.0042 | 14.4180 | 5450 | 0.7880 | 0.9012 | 0.9013 | 0.9012 | 0.9015 |
| 0.0084 | 14.5503 | 5500 | 0.7928 | 0.9025 | 0.9025 | 0.9023 | 0.9028 |
| 0.0054 | 14.6825 | 5550 | 0.8009 | 0.8990 | 0.8993 | 0.8993 | 0.8992 |
| 0.0099 | 14.8148 | 5600 | 0.7738 | 0.9020 | 0.9019 | 0.9017 | 0.9023 |
| 0.0087 | 14.9471 | 5650 | 0.8047 | 0.9023 | 0.9019 | 0.9021 | 0.9028 |
| 0.0136 | 15.0794 | 5700 | 0.7985 | 0.9018 | 0.9020 | 0.9020 | 0.9021 |
| 0.0048 | 15.2116 | 5750 | 0.8070 | 0.9027 | 0.9029 | 0.9030 | 0.9030 |
| 0.0083 | 15.3439 | 5800 | 0.8263 | 0.9025 | 0.9022 | 0.9026 | 0.9030 |
| 0.0038 | 15.4762 | 5850 | 0.8046 | 0.9040 | 0.9037 | 0.9036 | 0.9044 |
| 0.0098 | 15.6085 | 5900 | 0.7831 | 0.9028 | 0.9027 | 0.9025 | 0.9031 |
| 0.0107 | 15.7407 | 5950 | 0.7760 | 0.9034 | 0.9033 | 0.9031 | 0.9038 |
| 0.0099 | 15.8730 | 6000 | 0.8014 | 0.9015 | 0.9016 | 0.9016 | 0.9018 |
| 0.0087 | 16.0053 | 6050 | 0.7972 | 0.9022 | 0.9019 | 0.9018 | 0.9026 |
| 0.0034 | 16.1376 | 6100 | 0.8133 | 0.9001 | 0.9004 | 0.9006 | 0.9003 |
| 0.0063 | 16.2698 | 6150 | 0.7995 | 0.9028 | 0.9028 | 0.9026 | 0.9031 |
| 0.004 | 16.4021 | 6200 | 0.8010 | 0.9035 | 0.9033 | 0.9031 | 0.9038 |
| 0.0091 | 16.5344 | 6250 | 0.7946 | 0.9026 | 0.9023 | 0.9021 | 0.9029 |
| 0.0068 | 16.6667 | 6300 | 0.8044 | 0.9031 | 0.9032 | 0.9031 | 0.9035 |
| 0.0048 | 16.7989 | 6350 | 0.8205 | 0.9041 | 0.9038 | 0.9037 | 0.9045 |
| 0.0093 | 16.9312 | 6400 | 0.8196 | 0.9021 | 0.9020 | 0.9018 | 0.9024 |
| 0.0061 | 17.0635 | 6450 | 0.8313 | 0.9007 | 0.9009 | 0.9008 | 0.9010 |
| 0.0058 | 17.1958 | 6500 | 0.8315 | 0.9001 | 0.9003 | 0.9002 | 0.9005 |
| 0.0025 | 17.3280 | 6550 | 0.8407 | 0.9009 | 0.9010 | 0.9009 | 0.9012 |
| 0.0059 | 17.4603 | 6600 | 0.8447 | 0.8989 | 0.8992 | 0.8992 | 0.8992 |
| 0.0038 | 17.5926 | 6650 | 0.8379 | 0.9029 | 0.9027 | 0.9025 | 0.9033 |
| 0.0044 | 17.7249 | 6700 | 0.8374 | 0.9016 | 0.9017 | 0.9016 | 0.9019 |
| 0.0077 | 17.8571 | 6750 | 0.8314 | 0.9033 | 0.9029 | 0.9028 | 0.9037 |
| 0.0039 | 17.9894 | 6800 | 0.8312 | 0.9017 | 0.9013 | 0.9012 | 0.9021 |
| 0.0051 | 18.1217 | 6850 | 0.8277 | 0.9024 | 0.9021 | 0.9019 | 0.9028 |
| 0.0053 | 18.2540 | 6900 | 0.8340 | 0.9021 | 0.9021 | 0.9019 | 0.9024 |
| 0.0015 | 18.3862 | 6950 | 0.8395 | 0.9018 | 0.9018 | 0.9017 | 0.9021 |
| 0.0038 | 18.5185 | 7000 | 0.8436 | 0.9021 | 0.9022 | 0.9020 | 0.9025 |
| 0.0044 | 18.6508 | 7050 | 0.8463 | 0.9025 | 0.9023 | 0.9021 | 0.9028 |
| 0.0051 | 18.7831 | 7100 | 0.8470 | 0.9021 | 0.9020 | 0.9018 | 0.9025 |
| 0.0035 | 18.9153 | 7150 | 0.8476 | 0.9027 | 0.9027 | 0.9025 | 0.9031 |
| 0.0028 | 19.0476 | 7200 | 0.8485 | 0.9028 | 0.9027 | 0.9025 | 0.9032 |
| 0.0022 | 19.1799 | 7250 | 0.8495 | 0.9027 | 0.9027 | 0.9025 | 0.9031 |
| 0.0069 | 19.3122 | 7300 | 0.8527 | 0.9017 | 0.9019 | 0.9018 | 0.9020 |
| 0.0058 | 19.4444 | 7350 | 0.8535 | 0.9013 | 0.9015 | 0.9013 | 0.9016 |
| 0.0032 | 19.5767 | 7400 | 0.8536 | 0.9022 | 0.9022 | 0.9020 | 0.9025 |
| 0.004 | 19.7090 | 7450 | 0.8525 | 0.9023 | 0.9023 | 0.9021 | 0.9026 |
| 0.0039 | 19.8413 | 7500 | 0.8521 | 0.9021 | 0.9021 | 0.9019 | 0.9025 |
| 0.0017 | 19.9735 | 7550 | 0.8526 | 0.9024 | 0.9023 | 0.9021 | 0.9027 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1
|