---
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