---
library_name: transformers
license: mit
base_model: dbmdz/bert-base-turkish-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: MyHateSpeechDetection
  results: []
---

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

This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3951
- F1: 0.7481
- Roc Auc: 0.8138
- Accuracy: 0.6111

## 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-06
- train_batch_size: 40
- eval_batch_size: 20
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.1319        | 0.3636 | 100  | 0.4029          | 0.7428 | 0.8106  | 0.6020   |
| 0.1821        | 0.7273 | 200  | 0.3951          | 0.7481 | 0.8138  | 0.6111   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3