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---
base_model: ProsusAI/finbert
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finbert_bert-base-uncased
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_bert-base-uncased
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: 0.8116
- Accuracy: 0.8752
- F1: 0.8758
- Precision: 0.8778
- Recall: 0.8752
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8465 | 1.0 | 91 | 0.7610 | 0.6817 | 0.6643 | 0.6806 | 0.6817 |
| 0.5154 | 2.0 | 182 | 0.4672 | 0.8066 | 0.8082 | 0.8203 | 0.8066 |
| 0.331 | 3.0 | 273 | 0.4259 | 0.8393 | 0.8396 | 0.8407 | 0.8393 |
| 0.2461 | 4.0 | 364 | 0.5386 | 0.8315 | 0.8311 | 0.8405 | 0.8315 |
| 0.163 | 5.0 | 455 | 0.5392 | 0.8518 | 0.8496 | 0.8554 | 0.8518 |
| 0.1193 | 6.0 | 546 | 0.5441 | 0.8565 | 0.8559 | 0.8590 | 0.8565 |
| 0.0935 | 7.0 | 637 | 0.6496 | 0.8253 | 0.8218 | 0.8306 | 0.8253 |
| 0.0536 | 8.0 | 728 | 0.5461 | 0.8612 | 0.8609 | 0.8609 | 0.8612 |
| 0.0809 | 9.0 | 819 | 0.6680 | 0.8362 | 0.8350 | 0.8394 | 0.8362 |
| 0.0986 | 10.0 | 910 | 0.6303 | 0.8596 | 0.8597 | 0.8645 | 0.8596 |
| 0.0765 | 11.0 | 1001 | 0.7653 | 0.8300 | 0.8310 | 0.8511 | 0.8300 |
| 0.0507 | 12.0 | 1092 | 0.5176 | 0.8690 | 0.8691 | 0.8701 | 0.8690 |
| 0.0633 | 13.0 | 1183 | 0.9141 | 0.8268 | 0.8261 | 0.8370 | 0.8268 |
| 0.0529 | 14.0 | 1274 | 0.7537 | 0.8549 | 0.8552 | 0.8621 | 0.8549 |
| 0.0418 | 15.0 | 1365 | 0.9200 | 0.8346 | 0.8342 | 0.8441 | 0.8346 |
| 0.0151 | 16.0 | 1456 | 0.8578 | 0.8565 | 0.8549 | 0.8622 | 0.8565 |
| 0.0154 | 17.0 | 1547 | 0.8116 | 0.8752 | 0.8758 | 0.8778 | 0.8752 |
| 0.0054 | 18.0 | 1638 | 0.8926 | 0.8736 | 0.8733 | 0.8751 | 0.8736 |
| 0.0259 | 19.0 | 1729 | 0.9026 | 0.8705 | 0.8705 | 0.8709 | 0.8705 |
| 0.0036 | 20.0 | 1820 | 0.9616 | 0.8721 | 0.8713 | 0.8716 | 0.8721 |
| 0.0012 | 21.0 | 1911 | 0.9985 | 0.8658 | 0.8656 | 0.8655 | 0.8658 |
| 0.002 | 22.0 | 2002 | 0.9833 | 0.8690 | 0.8689 | 0.8688 | 0.8690 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
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