---
base_model: ProsusAI/finbert
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finBert
  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

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: 0.3623
- Accuracy: 0.868
- F1: 0.8666
- Precision: 0.8671
- Recall: 0.8685

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2152        | 0.0820 | 50   | 0.6140          | 0.7004   | 0.6545 | 0.7339    | 0.7020 |
| 0.5357        | 0.1639 | 100  | 0.4736          | 0.8265   | 0.8216 | 0.8270    | 0.8268 |
| 0.4421        | 0.2459 | 150  | 0.4189          | 0.8501   | 0.8466 | 0.8494    | 0.8505 |
| 0.4591        | 0.3279 | 200  | 0.4177          | 0.8390   | 0.8336 | 0.8417    | 0.8395 |
| 0.4382        | 0.4098 | 250  | 0.3876          | 0.8549   | 0.8537 | 0.8538    | 0.8550 |
| 0.4002        | 0.4918 | 300  | 0.4063          | 0.8434   | 0.8454 | 0.8503    | 0.8433 |
| 0.4085        | 0.5738 | 350  | 0.3818          | 0.8576   | 0.8574 | 0.8572    | 0.8579 |
| 0.3692        | 0.6557 | 400  | 0.3700          | 0.8608   | 0.8592 | 0.8598    | 0.8611 |
| 0.3686        | 0.7377 | 450  | 0.3730          | 0.8610   | 0.8604 | 0.8600    | 0.8613 |
| 0.3966        | 0.8197 | 500  | 0.3760          | 0.8566   | 0.8573 | 0.8584    | 0.8569 |
| 0.3729        | 0.9016 | 550  | 0.3533          | 0.8673   | 0.8667 | 0.8664    | 0.8675 |
| 0.3605        | 0.9836 | 600  | 0.3582          | 0.8632   | 0.8637 | 0.8645    | 0.8632 |
| 0.3471        | 1.0656 | 650  | 0.3671          | 0.8644   | 0.8643 | 0.8646    | 0.8645 |
| 0.3397        | 1.1475 | 700  | 0.3632          | 0.8658   | 0.8658 | 0.8656    | 0.8660 |
| 0.2977        | 1.2295 | 750  | 0.3737          | 0.8631   | 0.8619 | 0.8620    | 0.8634 |
| 0.3047        | 1.3115 | 800  | 0.3609          | 0.8656   | 0.8664 | 0.8673    | 0.8657 |
| 0.2817        | 1.3934 | 850  | 0.3816          | 0.8682   | 0.8661 | 0.8675    | 0.8685 |
| 0.2667        | 1.4754 | 900  | 0.3734          | 0.8690   | 0.8680 | 0.8686    | 0.8692 |
| 0.2809        | 1.5574 | 950  | 0.3523          | 0.8702   | 0.8706 | 0.8710    | 0.8703 |
| 0.2964        | 1.6393 | 1000 | 0.3621          | 0.8709   | 0.8693 | 0.8705    | 0.8711 |
| 0.2993        | 1.7213 | 1050 | 0.3502          | 0.8731   | 0.8728 | 0.8729    | 0.8732 |
| 0.3153        | 1.8033 | 1100 | 0.3532          | 0.8723   | 0.8703 | 0.8718    | 0.8726 |
| 0.3089        | 1.8852 | 1150 | 0.3581          | 0.8721   | 0.8710 | 0.8711    | 0.8724 |
| 0.3027        | 1.9672 | 1200 | 0.3513          | 0.8725   | 0.8712 | 0.8719    | 0.8727 |
| 0.2294        | 2.0492 | 1250 | 0.3673          | 0.8714   | 0.8715 | 0.8715    | 0.8715 |
| 0.2321        | 2.1311 | 1300 | 0.3630          | 0.8716   | 0.8718 | 0.8727    | 0.8716 |
| 0.2003        | 2.2131 | 1350 | 0.3951          | 0.872    | 0.8714 | 0.8711    | 0.8722 |
| 0.1948        | 2.2951 | 1400 | 0.3912          | 0.8724   | 0.8721 | 0.8719    | 0.8726 |
| 0.1939        | 2.3770 | 1450 | 0.3871          | 0.8733   | 0.8734 | 0.8734    | 0.8735 |
| 0.1897        | 2.4590 | 1500 | 0.3937          | 0.8738   | 0.8732 | 0.8730    | 0.8741 |
| 0.1951        | 2.5410 | 1550 | 0.3915          | 0.8711   | 0.8702 | 0.8704    | 0.8713 |
| 0.193         | 2.6230 | 1600 | 0.3902          | 0.8732   | 0.8729 | 0.8727    | 0.8734 |
| 0.1982        | 2.7049 | 1650 | 0.3903          | 0.8734   | 0.8734 | 0.8733    | 0.8736 |
| 0.1836        | 2.7869 | 1700 | 0.3929          | 0.8734   | 0.8732 | 0.8731    | 0.8736 |
| 0.2112        | 2.8689 | 1750 | 0.3927          | 0.8742   | 0.8738 | 0.8738    | 0.8743 |
| 0.1937        | 2.9508 | 1800 | 0.3899          | 0.8738   | 0.8739 | 0.8741    | 0.8740 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Tokenizers 0.19.1