| --- |
| base_model: ProsusAI/finbert |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: finbert_roberta-base |
| 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_roberta-base |
| |
| 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: 0.7907 |
| - Accuracy: 0.9033 |
| - F1: 0.9034 |
| - Precision: 0.9036 |
| - Recall: 0.9033 |
| |
| ## 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: 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: 1000 |
| - num_epochs: 25 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.8094 | 1.0 | 91 | 0.7239 | 0.6942 | 0.6824 | 0.6887 | 0.6942 | |
| | 0.439 | 2.0 | 182 | 0.4112 | 0.8471 | 0.8476 | 0.8527 | 0.8471 | |
| | 0.274 | 3.0 | 273 | 0.3978 | 0.8612 | 0.8596 | 0.8623 | 0.8612 | |
| | 0.2002 | 4.0 | 364 | 0.4319 | 0.8409 | 0.8399 | 0.8430 | 0.8409 | |
| | 0.123 | 5.0 | 455 | 0.4685 | 0.8674 | 0.8661 | 0.8685 | 0.8674 | |
| | 0.1251 | 6.0 | 546 | 0.4734 | 0.8690 | 0.8684 | 0.8689 | 0.8690 | |
| | 0.124 | 7.0 | 637 | 0.5604 | 0.8580 | 0.8574 | 0.8610 | 0.8580 | |
| | 0.0738 | 8.0 | 728 | 0.5583 | 0.8534 | 0.8546 | 0.8604 | 0.8534 | |
| | 0.1268 | 9.0 | 819 | 0.5665 | 0.8534 | 0.8524 | 0.8537 | 0.8534 | |
| | 0.0425 | 10.0 | 910 | 0.5959 | 0.8549 | 0.8561 | 0.8626 | 0.8549 | |
| | 0.1037 | 11.0 | 1001 | 0.4439 | 0.8752 | 0.8742 | 0.8760 | 0.8752 | |
| | 0.0762 | 12.0 | 1092 | 0.5998 | 0.8674 | 0.8668 | 0.8686 | 0.8674 | |
| | 0.0523 | 13.0 | 1183 | 0.5525 | 0.8783 | 0.8785 | 0.8792 | 0.8783 | |
| | 0.0291 | 14.0 | 1274 | 0.6588 | 0.8752 | 0.8747 | 0.8756 | 0.8752 | |
| | 0.0311 | 15.0 | 1365 | 0.6065 | 0.8830 | 0.8833 | 0.8839 | 0.8830 | |
| | 0.0146 | 16.0 | 1456 | 0.7469 | 0.8705 | 0.8701 | 0.8706 | 0.8705 | |
| | 0.0145 | 17.0 | 1547 | 0.6748 | 0.8861 | 0.8864 | 0.8872 | 0.8861 | |
| | 0.0013 | 18.0 | 1638 | 0.7708 | 0.8814 | 0.8815 | 0.8816 | 0.8814 | |
| | 0.0105 | 19.0 | 1729 | 0.8126 | 0.8908 | 0.8910 | 0.8918 | 0.8908 | |
| | 0.0025 | 20.0 | 1820 | 0.7727 | 0.8939 | 0.8938 | 0.8957 | 0.8939 | |
| | 0.0014 | 21.0 | 1911 | 0.8088 | 0.8939 | 0.8942 | 0.8958 | 0.8939 | |
| | 0.0015 | 22.0 | 2002 | 0.7766 | 0.9033 | 0.9033 | 0.9034 | 0.9033 | |
| | 0.0001 | 23.0 | 2093 | 0.7907 | 0.9033 | 0.9034 | 0.9036 | 0.9033 | |
| | 0.0002 | 24.0 | 2184 | 0.7945 | 0.9033 | 0.9034 | 0.9036 | 0.9033 | |
| | 0.0002 | 25.0 | 2275 | 0.7954 | 0.9033 | 0.9034 | 0.9036 | 0.9033 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.37.0 |
| - Pytorch 2.1.2 |
| - Datasets 2.1.0 |
| - Tokenizers 0.15.1 |
|
|
