---
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