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--- |
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base_model: ProsusAI/finbert |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: finBert |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# finBert |
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This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3623 |
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- Accuracy: 0.868 |
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- F1: 0.8666 |
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- Precision: 0.8671 |
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- Recall: 0.8685 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.2152 | 0.0820 | 50 | 0.6140 | 0.7004 | 0.6545 | 0.7339 | 0.7020 | |
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| 0.5357 | 0.1639 | 100 | 0.4736 | 0.8265 | 0.8216 | 0.8270 | 0.8268 | |
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| 0.4421 | 0.2459 | 150 | 0.4189 | 0.8501 | 0.8466 | 0.8494 | 0.8505 | |
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| 0.4591 | 0.3279 | 200 | 0.4177 | 0.8390 | 0.8336 | 0.8417 | 0.8395 | |
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| 0.4382 | 0.4098 | 250 | 0.3876 | 0.8549 | 0.8537 | 0.8538 | 0.8550 | |
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| 0.4002 | 0.4918 | 300 | 0.4063 | 0.8434 | 0.8454 | 0.8503 | 0.8433 | |
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| 0.4085 | 0.5738 | 350 | 0.3818 | 0.8576 | 0.8574 | 0.8572 | 0.8579 | |
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| 0.3692 | 0.6557 | 400 | 0.3700 | 0.8608 | 0.8592 | 0.8598 | 0.8611 | |
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| 0.3686 | 0.7377 | 450 | 0.3730 | 0.8610 | 0.8604 | 0.8600 | 0.8613 | |
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| 0.3966 | 0.8197 | 500 | 0.3760 | 0.8566 | 0.8573 | 0.8584 | 0.8569 | |
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| 0.3729 | 0.9016 | 550 | 0.3533 | 0.8673 | 0.8667 | 0.8664 | 0.8675 | |
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| 0.3605 | 0.9836 | 600 | 0.3582 | 0.8632 | 0.8637 | 0.8645 | 0.8632 | |
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| 0.3471 | 1.0656 | 650 | 0.3671 | 0.8644 | 0.8643 | 0.8646 | 0.8645 | |
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| 0.3397 | 1.1475 | 700 | 0.3632 | 0.8658 | 0.8658 | 0.8656 | 0.8660 | |
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| 0.2977 | 1.2295 | 750 | 0.3737 | 0.8631 | 0.8619 | 0.8620 | 0.8634 | |
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| 0.3047 | 1.3115 | 800 | 0.3609 | 0.8656 | 0.8664 | 0.8673 | 0.8657 | |
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| 0.2817 | 1.3934 | 850 | 0.3816 | 0.8682 | 0.8661 | 0.8675 | 0.8685 | |
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| 0.2667 | 1.4754 | 900 | 0.3734 | 0.8690 | 0.8680 | 0.8686 | 0.8692 | |
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| 0.2809 | 1.5574 | 950 | 0.3523 | 0.8702 | 0.8706 | 0.8710 | 0.8703 | |
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| 0.2964 | 1.6393 | 1000 | 0.3621 | 0.8709 | 0.8693 | 0.8705 | 0.8711 | |
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| 0.2993 | 1.7213 | 1050 | 0.3502 | 0.8731 | 0.8728 | 0.8729 | 0.8732 | |
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| 0.3153 | 1.8033 | 1100 | 0.3532 | 0.8723 | 0.8703 | 0.8718 | 0.8726 | |
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| 0.3089 | 1.8852 | 1150 | 0.3581 | 0.8721 | 0.8710 | 0.8711 | 0.8724 | |
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| 0.3027 | 1.9672 | 1200 | 0.3513 | 0.8725 | 0.8712 | 0.8719 | 0.8727 | |
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| 0.2294 | 2.0492 | 1250 | 0.3673 | 0.8714 | 0.8715 | 0.8715 | 0.8715 | |
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| 0.2321 | 2.1311 | 1300 | 0.3630 | 0.8716 | 0.8718 | 0.8727 | 0.8716 | |
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| 0.2003 | 2.2131 | 1350 | 0.3951 | 0.872 | 0.8714 | 0.8711 | 0.8722 | |
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| 0.1948 | 2.2951 | 1400 | 0.3912 | 0.8724 | 0.8721 | 0.8719 | 0.8726 | |
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| 0.1939 | 2.3770 | 1450 | 0.3871 | 0.8733 | 0.8734 | 0.8734 | 0.8735 | |
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| 0.1897 | 2.4590 | 1500 | 0.3937 | 0.8738 | 0.8732 | 0.8730 | 0.8741 | |
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| 0.1951 | 2.5410 | 1550 | 0.3915 | 0.8711 | 0.8702 | 0.8704 | 0.8713 | |
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| 0.193 | 2.6230 | 1600 | 0.3902 | 0.8732 | 0.8729 | 0.8727 | 0.8734 | |
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| 0.1982 | 2.7049 | 1650 | 0.3903 | 0.8734 | 0.8734 | 0.8733 | 0.8736 | |
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| 0.1836 | 2.7869 | 1700 | 0.3929 | 0.8734 | 0.8732 | 0.8731 | 0.8736 | |
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| 0.2112 | 2.8689 | 1750 | 0.3927 | 0.8742 | 0.8738 | 0.8738 | 0.8743 | |
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| 0.1937 | 2.9508 | 1800 | 0.3899 | 0.8738 | 0.8739 | 0.8741 | 0.8740 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Tokenizers 0.19.1 |
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