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---
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
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