---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-char-classification-e15
  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-finetuned-char-classification-e15

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1233
- F1: 0.5572
- Roc Auc: 0.7345
- Accuracy: 0.3761

## 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: 0.0002
- train_batch_size: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.1881        | 1.2   | 500  | 0.1694          | 0.0104 | 0.5024  | 0.0007   |
| 0.1563        | 2.39  | 1000 | 0.1415          | 0.3037 | 0.5948  | 0.1221   |
| 0.1374        | 3.59  | 1500 | 0.1290          | 0.3997 | 0.6353  | 0.1958   |
| 0.127         | 4.78  | 2000 | 0.1231          | 0.4467 | 0.6579  | 0.2376   |
| 0.1194        | 5.98  | 2500 | 0.1188          | 0.4801 | 0.6748  | 0.2665   |
| 0.1114        | 7.18  | 3000 | 0.1183          | 0.5071 | 0.6944  | 0.3016   |
| 0.1048        | 8.37  | 3500 | 0.1177          | 0.5202 | 0.7032  | 0.3216   |
| 0.0983        | 9.57  | 4000 | 0.1172          | 0.5344 | 0.7123  | 0.3371   |
| 0.0917        | 10.77 | 4500 | 0.1164          | 0.5414 | 0.7175  | 0.3458   |
| 0.0852        | 11.96 | 5000 | 0.1175          | 0.5495 | 0.7240  | 0.3580   |
| 0.0787        | 13.16 | 5500 | 0.1226          | 0.5545 | 0.7328  | 0.3742   |
| 0.0738        | 14.35 | 6000 | 0.1233          | 0.5572 | 0.7345  | 0.3761   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3