metadata
language:
- tr
base_model:
- artiwise-ai/modernbert-base-tr-uncased
pipeline_tag: text-classification
library_name: transformers
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ModernBERT-Sentiment-Classifier
results: []
datasets:
- winvoker/turkish-sentiment-analysis-dataset
ModernBERT-Turkish-Sentiment-Classifier
This model is a fine-tuned version of artiwise-ai/modernbert-base-tr-uncased on a Sentiment Analysis dataset winvoker/turkish-sentiment-analysis-dataset. It achieves the following results on the evaluation set:
- Loss: 0.280550
- Accuracy: 0.956000
- Precision: 0.956370
- Recall: 0.956000
- F1: 0.956175
Training and evaluation data
The data cannot be shared.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Epoch | Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|---|---|
| 1 | 0.161400 | 0.163402 | 0.945000 | 0.945220 | 0.945000 | 0.945107 |
| 2 | 0.034400 | 0.286277 | 0.942000 | 0.949040 | 0.942000 | 0.944231 |
| 3 | 0.003100 | 0.280550 | 0.956000 | 0.956370 | 0.956000 | 0.956170 |
Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1