---
license: mit
base_model: indobenchmark/indobert-large-p2
tags:
- generated_from_trainer
model-index:
- name: classification-hate-speech-DE-7epoch-idblp2
  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. -->

# classification-hate-speech-DE-7epoch-idblp2

This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1842
- F1 macro: 0.3914
- Weighted: 0.5669
- Balanced accuracy: 0.5343

## 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-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 macro | Weighted | Balanced accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|
| 1.4198        | 1.0   | 38   | 1.4182          | 0.3423   | 0.4674   | 0.4787            |
| 0.9738        | 2.0   | 76   | 1.1739          | 0.4094   | 0.6456   | 0.5125            |
| 0.4162        | 3.0   | 114  | 1.5137          | 0.4162   | 0.6046   | 0.5367            |
| 0.1499        | 4.0   | 152  | 1.9714          | 0.3981   | 0.5371   | 0.5527            |
| 0.0389        | 5.0   | 190  | 2.1858          | 0.3819   | 0.5532   | 0.5416            |
| 0.0144        | 6.0   | 228  | 2.1318          | 0.3890   | 0.5780   | 0.5369            |
| 0.0195        | 7.0   | 266  | 2.1842          | 0.3914   | 0.5669   | 0.5343            |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1