---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: BERT_FPB_finetuned_v2
  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. -->

# BERT_FPB_finetuned_v2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5430
- Accuracy: 0.8789
- F1: 0.8785
- Precision: 0.8784
- Recall: 0.8789

## 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: 16
- 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: 500
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.611         | 1.0   | 218  | 0.4727          | 0.8170   | 0.8078 | 0.8356    | 0.8170 |
| 0.4036        | 2.0   | 436  | 0.4077          | 0.8557   | 0.8525 | 0.8546    | 0.8557 |
| 0.2149        | 3.0   | 654  | 0.4153          | 0.8711   | 0.8715 | 0.8723    | 0.8711 |
| 0.067         | 4.0   | 872  | 0.5430          | 0.8789   | 0.8785 | 0.8784    | 0.8789 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Tokenizers 0.19.1