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

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: 0.5238
- Accuracy: 0.8711
- F1: 0.8705
- Precision: 0.8706
- Recall: 0.8711

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6376        | 1.0   | 218  | 0.4370          | 0.8144   | 0.8157 | 0.8177    | 0.8144 |
| 0.4168        | 2.0   | 436  | 0.4208          | 0.8376   | 0.8356 | 0.8358    | 0.8376 |
| 0.2808        | 3.0   | 654  | 0.4520          | 0.8608   | 0.8606 | 0.8609    | 0.8608 |
| 0.0538        | 4.0   | 872  | 0.5238          | 0.8711   | 0.8705 | 0.8706    | 0.8711 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1