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

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: 1.5026
- Accuracy: 0.7856
- F1-score: 0.7175
- Precision: 0.7058
- Recall: 0.7369
- Auc: 0.7369

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.271         | 1.0   | 174  | 0.4648          | 0.7954   | 0.7005   | 0.7070    | 0.6950 | 0.6950 |
| 0.2246        | 2.0   | 348  | 0.5229          | 0.7987   | 0.7053   | 0.7119    | 0.6997 | 0.6997 |
| 0.3814        | 3.0   | 522  | 0.7043          | 0.7676   | 0.7018   | 0.6896    | 0.7278 | 0.7278 |
| 0.1343        | 4.0   | 696  | 0.8843          | 0.7938   | 0.7217   | 0.7124    | 0.7346 | 0.7346 |
| 0.0063        | 5.0   | 870  | 1.0890          | 0.7807   | 0.7040   | 0.6955    | 0.7159 | 0.7159 |
| 0.063         | 6.0   | 1044 | 1.1208          | 0.8101   | 0.7378   | 0.7316    | 0.7452 | 0.7452 |
| 0.0022        | 7.0   | 1218 | 1.1989          | 0.8249   | 0.7318   | 0.7543    | 0.7166 | 0.7166 |
| 0.0356        | 8.0   | 1392 | 1.5295          | 0.7758   | 0.7151   | 0.7016    | 0.7457 | 0.7457 |
| 0.0002        | 9.0   | 1566 | 1.4269          | 0.8003   | 0.7202   | 0.7171    | 0.7236 | 0.7236 |
| 0.0004        | 10.0  | 1740 | 1.5026          | 0.7856   | 0.7175   | 0.7058    | 0.7369 | 0.7369 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3