---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: newly_fine_tuned_bert_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. -->

# newly_fine_tuned_bert_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: 0.0273
- F1: 0.5517
- Roc Auc: 0.6994
- Accuracy: 0.4

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 300

### Training results

| Training Loss | Epoch    | Step  | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:--------:|:-----:|:---------------:|:------:|:-------:|:--------:|
| 0.0339        | 11.3636  | 500   | 0.0355          | 0.0    | 0.5     | 0.0      |
| 0.028         | 22.7273  | 1000  | 0.0327          | 0.0    | 0.5     | 0.0      |
| 0.0255        | 34.0909  | 1500  | 0.0327          | 0.0    | 0.5     | 0.0      |
| 0.0234        | 45.4545  | 2000  | 0.0316          | 0.0    | 0.5     | 0.0      |
| 0.0202        | 56.8182  | 2500  | 0.0309          | 0.0    | 0.5     | 0.0      |
| 0.0174        | 68.1818  | 3000  | 0.0291          | 0.0    | 0.5     | 0.0      |
| 0.0151        | 79.5455  | 3500  | 0.0281          | 0.0    | 0.5     | 0.0      |
| 0.013         | 90.9091  | 4000  | 0.0274          | 0.0    | 0.5     | 0.0      |
| 0.0109        | 102.2727 | 4500  | 0.0271          | 0.0    | 0.5     | 0.0      |
| 0.0095        | 113.6364 | 5000  | 0.0267          | 0.0    | 0.5     | 0.0      |
| 0.0081        | 125.0    | 5500  | 0.0262          | 0.0    | 0.5     | 0.0      |
| 0.007         | 136.3636 | 6000  | 0.0262          | 0.0952 | 0.525   | 0.05     |
| 0.0062        | 147.7273 | 6500  | 0.0267          | 0.4    | 0.625   | 0.25     |
| 0.0053        | 159.0909 | 7000  | 0.0262          | 0.4    | 0.625   | 0.25     |
| 0.0048        | 170.4545 | 7500  | 0.0266          | 0.4615 | 0.65    | 0.3      |
| 0.0043        | 181.8182 | 8000  | 0.0259          | 0.5    | 0.6744  | 0.35     |
| 0.0039        | 193.1818 | 8500  | 0.0264          | 0.5714 | 0.7     | 0.4      |
| 0.0036        | 204.5455 | 9000  | 0.0268          | 0.5517 | 0.6994  | 0.4      |
| 0.0032        | 215.9091 | 9500  | 0.0270          | 0.5517 | 0.6994  | 0.4      |
| 0.003         | 227.2727 | 10000 | 0.0272          | 0.5517 | 0.6994  | 0.4      |
| 0.0028        | 238.6364 | 10500 | 0.0269          | 0.5517 | 0.6994  | 0.4      |
| 0.0027        | 250.0    | 11000 | 0.0267          | 0.5333 | 0.6988  | 0.4      |
| 0.0026        | 261.3636 | 11500 | 0.0271          | 0.5333 | 0.6988  | 0.4      |
| 0.0025        | 272.7273 | 12000 | 0.0272          | 0.5333 | 0.6988  | 0.4      |
| 0.0025        | 284.0909 | 12500 | 0.0272          | 0.5517 | 0.6994  | 0.4      |
| 0.0024        | 295.4545 | 13000 | 0.0273          | 0.5517 | 0.6994  | 0.4      |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1