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--- |
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library_name: transformers |
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license: mit |
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base_model: dbmdz/bert-base-turkish-cased |
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tags: |
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- generated_from_trainer |
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- sentiment |
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- turkish |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: turkish-sentiment |
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results: [] |
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datasets: |
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- winvoker/turkish-sentiment-analysis-dataset |
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language: |
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- tr |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# turkish-sentiment |
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This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on winvoker/turkish-sentiment-analysis-dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0880 |
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- Accuracy: 0.9688 |
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- F1 Macro: 0.9454 |
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- F1 Weighted: 0.9685 |
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- Precision: 0.9683 |
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- Recall: 0.9688 |
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## Model description |
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A BERT-based(dbmdz Turkish BERT) model fine-tuned on a large-scale Turkish sentiment analysis dataset. This model classifies Turkish text into three sentiment classes: Negative, Notr (Neutral), and Positive. |
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- Model type: BertForSequenceClassification |
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- Base model: dbmdz/bert-base-turkish-cased |
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- Language(s): Turkish |
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## Intended uses & limitations |
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- Turkish text classification tasks involving sentiment analysis. |
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- Suitable for social media data, product reviews, or general-purpose sentiment detection in Turkish. |
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## Usage |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="kaixkhazaki/turkish-sentiment") |
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pipe("Kargo geç geldi ve ürün beklentimi pek karşılamadı.") |
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>> [{'label': 'Negative', 'score': 0.984860897064209}] |
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pipe("Yemek lezzetliydi ancak servis yavaş ve çalışanlar ilgisizdi, pek anlayamadım nasıl hissettiğimi.") |
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>> [{'label': 'Notr', 'score': 0.9881975054740906}] |
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pipe("Gerçekten müthiş bir deneyimdi, keşke hep burda kalabilsem.") |
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>> [{'label': 'Positive', 'score': 0.9942901134490967}] |
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``` |
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## Training and evaluation data |
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Fine-tuned on a combined dataset with 440,679 training samples and 48,965 validation samples. |
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## Training procedure |
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Trained on using the entire dataset on a single gpu for apx. 25 mins(1600 steps). |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 400 |
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- training_steps: 1600 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------:|:------:| |
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| 0.3538 | 0.0581 | 400 | 0.1162 | 0.9582 | 0.9243 | 0.9568 | 0.9572 | 0.9582 | |
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| 0.1131 | 0.1162 | 800 | 0.1034 | 0.9639 | 0.9369 | 0.9635 | 0.9633 | 0.9639 | |
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| 0.1026 | 0.1743 | 1200 | 0.0940 | 0.9649 | 0.9411 | 0.9652 | 0.9657 | 0.9649 | |
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| 0.0936 | 0.2324 | 1600 | 0.0880 | 0.9688 | 0.9454 | 0.9685 | 0.9683 | 0.9688 | |
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### Framework versions |
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- Transformers 4.48.0.dev0 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.21.0 |
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### Citation |
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```bibtex |
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@misc{turkish-sentiment, |
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title={Turkish Sentiment Analysis using Turkish BERT}, |
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author={Fatih Demrici}, |
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year={2025}, |
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howpublished={\url{https://huggingface.co/kaixkhazaki/turkish-sentiment}}, |
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} |
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``` |
