---
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-eval-256
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-eval-256

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2605
- F1: 0.7522
- Roc Auc: 0.8283
- Accuracy: 0.3007

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4097        | 1.0   | 751  | 0.3153          | 0.6628 | 0.7598  | 0.1638   |
| 0.2603        | 2.0   | 1502 | 0.2751          | 0.7205 | 0.7998  | 0.2328   |
| 0.2103        | 3.0   | 2253 | 0.2594          | 0.7507 | 0.8239  | 0.2837   |
| 0.1581        | 4.0   | 3004 | 0.2605          | 0.7522 | 0.8283  | 0.3007   |
| 0.1342        | 5.0   | 3755 | 0.2591          | 0.7513 | 0.8279  | 0.2897   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3