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---
base_model: ProsusAI/finbert
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finBERT_sentiment_analysis_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_sentiment_analysis_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: 0.8337
- Accuracy: 0.9040
- F1: 0.9040
- Precision: 0.9038
- Recall: 0.9044
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2898 | 0.1323 | 50 | 0.5537 | 0.7885 | 0.7864 | 0.7889 | 0.7896 |
| 0.5067 | 0.2646 | 100 | 0.4689 | 0.8270 | 0.8216 | 0.8290 | 0.8281 |
| 0.4212 | 0.3968 | 150 | 0.4237 | 0.8467 | 0.8458 | 0.8475 | 0.8476 |
| 0.3969 | 0.5291 | 200 | 0.4118 | 0.8502 | 0.8477 | 0.8500 | 0.8511 |
| 0.3891 | 0.6614 | 250 | 0.3767 | 0.8545 | 0.8559 | 0.8586 | 0.8546 |
| 0.3927 | 0.7937 | 300 | 0.3542 | 0.8696 | 0.8695 | 0.8692 | 0.8701 |
| 0.378 | 0.9259 | 350 | 0.3563 | 0.8703 | 0.8689 | 0.8694 | 0.8710 |
| 0.3537 | 1.0582 | 400 | 0.3472 | 0.8686 | 0.8696 | 0.8709 | 0.8689 |
| 0.32 | 1.1905 | 450 | 0.3591 | 0.8721 | 0.8710 | 0.8710 | 0.8727 |
| 0.3119 | 1.3228 | 500 | 0.3545 | 0.8709 | 0.8704 | 0.8707 | 0.8714 |
| 0.2992 | 1.4550 | 550 | 0.3378 | 0.8753 | 0.8763 | 0.8777 | 0.8756 |
| 0.3025 | 1.5873 | 600 | 0.3320 | 0.8785 | 0.8779 | 0.8778 | 0.8790 |
| 0.2913 | 1.7196 | 650 | 0.3835 | 0.8729 | 0.8702 | 0.8737 | 0.8738 |
| 0.3103 | 1.8519 | 700 | 0.3321 | 0.8812 | 0.8805 | 0.8804 | 0.8817 |
| 0.2847 | 1.9841 | 750 | 0.3337 | 0.8832 | 0.8836 | 0.8839 | 0.8835 |
| 0.2037 | 2.1164 | 800 | 0.3848 | 0.8809 | 0.8811 | 0.8812 | 0.8812 |
| 0.2199 | 2.2487 | 850 | 0.4087 | 0.8708 | 0.8690 | 0.8720 | 0.8714 |
| 0.2103 | 2.3810 | 900 | 0.3562 | 0.8794 | 0.8784 | 0.8787 | 0.8799 |
| 0.2178 | 2.5132 | 950 | 0.3473 | 0.8848 | 0.8847 | 0.8846 | 0.8853 |
| 0.2067 | 2.6455 | 1000 | 0.3554 | 0.8864 | 0.8863 | 0.8861 | 0.8869 |
| 0.2327 | 2.7778 | 1050 | 0.3667 | 0.8761 | 0.8778 | 0.8826 | 0.8760 |
| 0.2139 | 2.9101 | 1100 | 0.3657 | 0.8813 | 0.8825 | 0.8847 | 0.8814 |
| 0.1998 | 3.0423 | 1150 | 0.3761 | 0.8802 | 0.8815 | 0.8843 | 0.8804 |
| 0.1387 | 3.1746 | 1200 | 0.3918 | 0.8870 | 0.8874 | 0.8876 | 0.8873 |
| 0.1355 | 3.3069 | 1250 | 0.4323 | 0.8810 | 0.8791 | 0.8818 | 0.8818 |
| 0.1546 | 3.4392 | 1300 | 0.4122 | 0.8853 | 0.8842 | 0.8848 | 0.8860 |
| 0.135 | 3.5714 | 1350 | 0.3948 | 0.8862 | 0.8857 | 0.8857 | 0.8867 |
| 0.1827 | 3.7037 | 1400 | 0.3676 | 0.8846 | 0.8852 | 0.8858 | 0.8848 |
| 0.1551 | 3.8360 | 1450 | 0.3910 | 0.8893 | 0.8887 | 0.8888 | 0.8898 |
| 0.1415 | 3.9683 | 1500 | 0.3669 | 0.8913 | 0.8919 | 0.8926 | 0.8916 |
| 0.1122 | 4.1005 | 1550 | 0.4385 | 0.8876 | 0.8882 | 0.8897 | 0.8879 |
| 0.0996 | 4.2328 | 1600 | 0.4151 | 0.8926 | 0.8922 | 0.8921 | 0.8931 |
| 0.1077 | 4.3651 | 1650 | 0.4277 | 0.8902 | 0.8902 | 0.8899 | 0.8906 |
| 0.1207 | 4.4974 | 1700 | 0.4166 | 0.8859 | 0.8852 | 0.8853 | 0.8864 |
| 0.0993 | 4.6296 | 1750 | 0.4141 | 0.8931 | 0.8934 | 0.8934 | 0.8933 |
| 0.1172 | 4.7619 | 1800 | 0.4173 | 0.8929 | 0.8933 | 0.8937 | 0.8931 |
| 0.121 | 4.8942 | 1850 | 0.4067 | 0.8934 | 0.8926 | 0.8928 | 0.8939 |
| 0.1134 | 5.0265 | 1900 | 0.4496 | 0.8936 | 0.8930 | 0.8931 | 0.8942 |
| 0.0677 | 5.1587 | 1950 | 0.4808 | 0.8896 | 0.8901 | 0.8906 | 0.8899 |
| 0.0722 | 5.2910 | 2000 | 0.4848 | 0.8881 | 0.8889 | 0.8903 | 0.8881 |
| 0.0917 | 5.4233 | 2050 | 0.4863 | 0.8927 | 0.8931 | 0.8937 | 0.8928 |
| 0.0871 | 5.5556 | 2100 | 0.4359 | 0.8973 | 0.8969 | 0.8967 | 0.8977 |
| 0.078 | 5.6878 | 2150 | 0.4410 | 0.8926 | 0.8925 | 0.8924 | 0.8931 |
| 0.0761 | 5.8201 | 2200 | 0.4724 | 0.8949 | 0.8945 | 0.8944 | 0.8954 |
| 0.0925 | 5.9524 | 2250 | 0.4932 | 0.8953 | 0.8944 | 0.8950 | 0.8958 |
| 0.076 | 6.0847 | 2300 | 0.5118 | 0.8885 | 0.8885 | 0.8889 | 0.8887 |
| 0.049 | 6.2169 | 2350 | 0.5233 | 0.8928 | 0.8930 | 0.8930 | 0.8930 |
| 0.0628 | 6.3492 | 2400 | 0.5108 | 0.9001 | 0.8998 | 0.8997 | 0.9006 |
| 0.0661 | 6.4815 | 2450 | 0.5096 | 0.8952 | 0.8952 | 0.8950 | 0.8955 |
| 0.0625 | 6.6138 | 2500 | 0.5538 | 0.8917 | 0.8920 | 0.8921 | 0.8919 |
| 0.0689 | 6.7460 | 2550 | 0.5341 | 0.8929 | 0.8931 | 0.8930 | 0.8932 |
| 0.0614 | 6.8783 | 2600 | 0.5080 | 0.8966 | 0.8968 | 0.8968 | 0.8969 |
| 0.0699 | 7.0106 | 2650 | 0.5037 | 0.8987 | 0.8987 | 0.8986 | 0.8991 |
| 0.0527 | 7.1429 | 2700 | 0.5176 | 0.9002 | 0.9002 | 0.9001 | 0.9006 |
| 0.0553 | 7.2751 | 2750 | 0.5412 | 0.8973 | 0.8980 | 0.8988 | 0.8976 |
| 0.0601 | 7.4074 | 2800 | 0.5279 | 0.8905 | 0.8916 | 0.8939 | 0.8906 |
| 0.0519 | 7.5397 | 2850 | 0.5628 | 0.9008 | 0.9006 | 0.9008 | 0.9013 |
| 0.0418 | 7.6720 | 2900 | 0.5653 | 0.8977 | 0.8974 | 0.8973 | 0.8982 |
| 0.0499 | 7.8042 | 2950 | 0.5412 | 0.8970 | 0.8972 | 0.8973 | 0.8974 |
| 0.0424 | 7.9365 | 3000 | 0.5626 | 0.8977 | 0.8969 | 0.8973 | 0.8982 |
| 0.0324 | 8.0688 | 3050 | 0.6073 | 0.9001 | 0.9000 | 0.8999 | 0.9005 |
| 0.0309 | 8.2011 | 3100 | 0.6108 | 0.8982 | 0.8983 | 0.8982 | 0.8984 |
| 0.03 | 8.3333 | 3150 | 0.6021 | 0.8975 | 0.8973 | 0.8971 | 0.8979 |
| 0.0429 | 8.4656 | 3200 | 0.6003 | 0.8953 | 0.8955 | 0.8955 | 0.8956 |
| 0.0455 | 8.5979 | 3250 | 0.6162 | 0.8947 | 0.8953 | 0.8961 | 0.8948 |
| 0.037 | 8.7302 | 3300 | 0.5923 | 0.8957 | 0.8961 | 0.8962 | 0.8959 |
| 0.0462 | 8.8624 | 3350 | 0.5522 | 0.8979 | 0.8977 | 0.8975 | 0.8983 |
| 0.0356 | 8.9947 | 3400 | 0.5926 | 0.9010 | 0.9009 | 0.9011 | 0.9014 |
| 0.0243 | 9.1270 | 3450 | 0.6353 | 0.8972 | 0.8968 | 0.8967 | 0.8976 |
| 0.0341 | 9.2593 | 3500 | 0.6161 | 0.8925 | 0.8931 | 0.8939 | 0.8926 |
| 0.0271 | 9.3915 | 3550 | 0.6381 | 0.9008 | 0.9007 | 0.9006 | 0.9012 |
| 0.0344 | 9.5238 | 3600 | 0.6282 | 0.9001 | 0.9000 | 0.8998 | 0.9005 |
| 0.0236 | 9.6561 | 3650 | 0.7047 | 0.8982 | 0.8968 | 0.8989 | 0.8988 |
| 0.035 | 9.7884 | 3700 | 0.6561 | 0.8975 | 0.8974 | 0.8974 | 0.8979 |
| 0.0308 | 9.9206 | 3750 | 0.6754 | 0.8973 | 0.8968 | 0.8968 | 0.8978 |
| 0.0404 | 10.0529 | 3800 | 0.6452 | 0.8994 | 0.8988 | 0.8989 | 0.8999 |
| 0.0176 | 10.1852 | 3850 | 0.6636 | 0.8993 | 0.8989 | 0.8988 | 0.8998 |
| 0.0233 | 10.3175 | 3900 | 0.6820 | 0.8953 | 0.8948 | 0.8947 | 0.8957 |
| 0.0192 | 10.4497 | 3950 | 0.6954 | 0.8989 | 0.8981 | 0.8986 | 0.8995 |
| 0.0175 | 10.5820 | 4000 | 0.6959 | 0.8985 | 0.8982 | 0.8980 | 0.8989 |
| 0.0339 | 10.7143 | 4050 | 0.6624 | 0.8990 | 0.8993 | 0.8995 | 0.8993 |
| 0.0259 | 10.8466 | 4100 | 0.6787 | 0.8997 | 0.8993 | 0.8994 | 0.9002 |
| 0.0236 | 10.9788 | 4150 | 0.6708 | 0.8987 | 0.8989 | 0.8992 | 0.8990 |
| 0.0175 | 11.1111 | 4200 | 0.6893 | 0.9021 | 0.9021 | 0.9023 | 0.9026 |
| 0.0233 | 11.2434 | 4250 | 0.6769 | 0.8999 | 0.8998 | 0.8996 | 0.9003 |
| 0.0112 | 11.3757 | 4300 | 0.6949 | 0.8990 | 0.8990 | 0.8988 | 0.8993 |
| 0.017 | 11.5079 | 4350 | 0.6952 | 0.9019 | 0.9015 | 0.9015 | 0.9023 |
| 0.0159 | 11.6402 | 4400 | 0.6913 | 0.9031 | 0.9032 | 0.9032 | 0.9035 |
| 0.0214 | 11.7725 | 4450 | 0.7120 | 0.8996 | 0.8988 | 0.8992 | 0.9001 |
| 0.0257 | 11.9048 | 4500 | 0.6963 | 0.9032 | 0.9031 | 0.9031 | 0.9036 |
| 0.0189 | 12.0370 | 4550 | 0.6746 | 0.9032 | 0.9031 | 0.9031 | 0.9036 |
| 0.0138 | 12.1693 | 4600 | 0.7145 | 0.8996 | 0.9001 | 0.9008 | 0.8998 |
| 0.0095 | 12.3016 | 4650 | 0.7094 | 0.9018 | 0.9017 | 0.9017 | 0.9022 |
| 0.0189 | 12.4339 | 4700 | 0.7084 | 0.9000 | 0.9001 | 0.9000 | 0.9002 |
| 0.0159 | 12.5661 | 4750 | 0.7567 | 0.8937 | 0.8941 | 0.8948 | 0.8938 |
| 0.0127 | 12.6984 | 4800 | 0.7099 | 0.9013 | 0.9011 | 0.9009 | 0.9017 |
| 0.0147 | 12.8307 | 4850 | 0.7231 | 0.9032 | 0.9032 | 0.9030 | 0.9036 |
| 0.0134 | 12.9630 | 4900 | 0.7168 | 0.9008 | 0.9009 | 0.9008 | 0.9011 |
| 0.0121 | 13.0952 | 4950 | 0.7427 | 0.9030 | 0.9027 | 0.9027 | 0.9035 |
| 0.0114 | 13.2275 | 5000 | 0.7568 | 0.8998 | 0.8999 | 0.8998 | 0.9001 |
| 0.0157 | 13.3598 | 5050 | 0.7427 | 0.9024 | 0.9019 | 0.9020 | 0.9029 |
| 0.0104 | 13.4921 | 5100 | 0.7503 | 0.9020 | 0.9014 | 0.9015 | 0.9024 |
| 0.0129 | 13.6243 | 5150 | 0.7438 | 0.9020 | 0.9018 | 0.9017 | 0.9024 |
| 0.0152 | 13.7566 | 5200 | 0.7613 | 0.8984 | 0.8987 | 0.8987 | 0.8986 |
| 0.0072 | 13.8889 | 5250 | 0.7603 | 0.9030 | 0.9026 | 0.9026 | 0.9034 |
| 0.0103 | 14.0212 | 5300 | 0.7771 | 0.9000 | 0.9003 | 0.9004 | 0.9003 |
| 0.0115 | 14.1534 | 5350 | 0.7600 | 0.9031 | 0.9031 | 0.9030 | 0.9035 |
| 0.006 | 14.2857 | 5400 | 0.7614 | 0.9034 | 0.9031 | 0.9030 | 0.9038 |
| 0.0067 | 14.4180 | 5450 | 0.7912 | 0.9021 | 0.9023 | 0.9023 | 0.9023 |
| 0.0089 | 14.5503 | 5500 | 0.7771 | 0.9030 | 0.9031 | 0.9030 | 0.9034 |
| 0.0103 | 14.6825 | 5550 | 0.7795 | 0.9031 | 0.9031 | 0.9029 | 0.9035 |
| 0.0159 | 14.8148 | 5600 | 0.7478 | 0.9040 | 0.9039 | 0.9037 | 0.9043 |
| 0.0089 | 14.9471 | 5650 | 0.7904 | 0.8973 | 0.8978 | 0.8983 | 0.8974 |
| 0.0115 | 15.0794 | 5700 | 0.7904 | 0.8987 | 0.8990 | 0.8989 | 0.8990 |
| 0.0063 | 15.2116 | 5750 | 0.7864 | 0.9033 | 0.9032 | 0.9030 | 0.9037 |
| 0.0078 | 15.3439 | 5800 | 0.7965 | 0.9001 | 0.9005 | 0.9006 | 0.9004 |
| 0.0026 | 15.4762 | 5850 | 0.7972 | 0.9027 | 0.9026 | 0.9024 | 0.9030 |
| 0.0109 | 15.6085 | 5900 | 0.7800 | 0.9031 | 0.9029 | 0.9030 | 0.9036 |
| 0.0075 | 15.7407 | 5950 | 0.7770 | 0.9049 | 0.9047 | 0.9046 | 0.9053 |
| 0.008 | 15.8730 | 6000 | 0.7980 | 0.9013 | 0.9017 | 0.9019 | 0.9015 |
| 0.0039 | 16.0053 | 6050 | 0.7939 | 0.9045 | 0.9044 | 0.9043 | 0.9049 |
| 0.0048 | 16.1376 | 6100 | 0.8197 | 0.9003 | 0.9006 | 0.9007 | 0.9005 |
| 0.0077 | 16.2698 | 6150 | 0.8159 | 0.9030 | 0.9028 | 0.9027 | 0.9035 |
| 0.0047 | 16.4021 | 6200 | 0.8150 | 0.9018 | 0.9019 | 0.9017 | 0.9021 |
| 0.0044 | 16.5344 | 6250 | 0.8150 | 0.9018 | 0.9020 | 0.9019 | 0.9021 |
| 0.0057 | 16.6667 | 6300 | 0.8151 | 0.9025 | 0.9024 | 0.9023 | 0.9028 |
| 0.0089 | 16.7989 | 6350 | 0.8155 | 0.9026 | 0.9022 | 0.9021 | 0.9030 |
| 0.0027 | 16.9312 | 6400 | 0.8215 | 0.9028 | 0.9029 | 0.9029 | 0.9031 |
| 0.0041 | 17.0635 | 6450 | 0.8356 | 0.9011 | 0.9011 | 0.9009 | 0.9015 |
| 0.0058 | 17.1958 | 6500 | 0.8291 | 0.9018 | 0.9018 | 0.9016 | 0.9022 |
| 0.003 | 17.3280 | 6550 | 0.8411 | 0.9017 | 0.9016 | 0.9014 | 0.9021 |
| 0.0086 | 17.4603 | 6600 | 0.8326 | 0.9010 | 0.9010 | 0.9008 | 0.9013 |
| 0.0041 | 17.5926 | 6650 | 0.8296 | 0.9015 | 0.9015 | 0.9013 | 0.9018 |
| 0.0055 | 17.7249 | 6700 | 0.8302 | 0.9014 | 0.9014 | 0.9012 | 0.9017 |
| 0.005 | 17.8571 | 6750 | 0.8357 | 0.9021 | 0.9019 | 0.9017 | 0.9025 |
| 0.0038 | 17.9894 | 6800 | 0.8310 | 0.9015 | 0.9014 | 0.9012 | 0.9018 |
| 0.0065 | 18.1217 | 6850 | 0.8276 | 0.9026 | 0.9027 | 0.9026 | 0.9029 |
| 0.005 | 18.2540 | 6900 | 0.8336 | 0.9011 | 0.9013 | 0.9012 | 0.9014 |
| 0.002 | 18.3862 | 6950 | 0.8343 | 0.9014 | 0.9014 | 0.9012 | 0.9017 |
| 0.0022 | 18.5185 | 7000 | 0.8368 | 0.9033 | 0.9033 | 0.9032 | 0.9036 |
| 0.0045 | 18.6508 | 7050 | 0.8339 | 0.9032 | 0.9032 | 0.9031 | 0.9036 |
| 0.0055 | 18.7831 | 7100 | 0.8346 | 0.9040 | 0.9038 | 0.9037 | 0.9044 |
| 0.0034 | 18.9153 | 7150 | 0.8320 | 0.9038 | 0.9035 | 0.9034 | 0.9042 |
| 0.0037 | 19.0476 | 7200 | 0.8382 | 0.9039 | 0.9035 | 0.9035 | 0.9043 |
| 0.0024 | 19.1799 | 7250 | 0.8398 | 0.9040 | 0.9037 | 0.9038 | 0.9045 |
| 0.0041 | 19.3122 | 7300 | 0.8356 | 0.9035 | 0.9034 | 0.9034 | 0.9040 |
| 0.0037 | 19.4444 | 7350 | 0.8332 | 0.9036 | 0.9036 | 0.9034 | 0.9040 |
| 0.0052 | 19.5767 | 7400 | 0.8342 | 0.9036 | 0.9036 | 0.9034 | 0.9040 |
| 0.0051 | 19.7090 | 7450 | 0.8331 | 0.9039 | 0.9038 | 0.9036 | 0.9043 |
| 0.0043 | 19.8413 | 7500 | 0.8334 | 0.9042 | 0.9041 | 0.9040 | 0.9046 |
| 0.0022 | 19.9735 | 7550 | 0.8337 | 0.9040 | 0.9040 | 0.9038 | 0.9044 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1
|