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--- |
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library_name: transformers |
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tags: |
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- Dissonant Detection |
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- transformers |
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- bert |
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language: |
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- tr |
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metrics: |
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- accuracy |
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base_model: |
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- ytu-ce-cosmos/turkish-base-bert-uncased |
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pipeline_tag: text-classification |
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--- |
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# **Sengil/ytu-bert-base-dissonance-tr** 🇹🇷 |
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A Turkish BERT-based model fine-tuned for three-way sentiment classification on single-sentence discourse. |
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This model categorizes input sentences into one of the following classes: |
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**Dissonance:** The sentence contains conflicting or contradictory sentiments |
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_e.g.,_ "Telefon çok kaliteli ve hızlı bitiyor şarjı" |
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**Consonance:** The sentence expresses harmonizing or mutually reinforcing sentiments |
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_e.g.,_ "Yemeklerde çok güzel manzarada mükemmel" |
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**Neither:** The sentence is neutral or does not clearly reflect either dissonance or consonance |
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_e.g.,_ "Bu gün hava çok güzel" |
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The model was trained on 37,368 Turkish samples and evaluated on two separate sets of 4,671 samples each. |
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It achieved 97.5% accuracy and 97.5% macro-F1 score on the test set, demonstrating strong performance in distinguishing subtle semantic contrasts in Turkish sentences. |
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|**Model Details** | | |
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| -------------------- | ----------------------------------------------------- | |
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| **Developed by** | Mert Şengil | |
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| **Model type** | `BertForSequenceClassification` | |
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| **Base model** | `ytu-ce-cosmos/turkish-base-bert-uncased` | |
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| **Languages** | `tr` (Turkish) | |
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| **License** | Apache-2.0 | |
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| **Fine-tuning task** | 3-class sentiment (dissonance / consonance / neither) | |
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## Uses |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_id = "Sengil/ytu-bert-base-dissonance-tr" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForSequenceClassification.from_pretrained(model_id) |
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text = "onu çok seviyorum ve güvenmiyorum." |
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text = text.replace("I", "ı").lower() |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=128) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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label_id = int(logits.argmax()) |
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id2label = {0: "Dissonance", 1: "Consonance", 2: "Neither"} |
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print(f"{{'label': '{id2label[label_id]}','score':{logits.argmax()}}}") |
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``` |
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output: |
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``` |
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{'label': 'Dissonance','score':0} |
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``` |
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|**Training Details** | | |
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| ---------------------- | ---------------------------------------------- | |
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| **Training samples** | 37 368 | |
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| **Validation samples** | 4 671 | |
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| **Test samples** | 4 671 | |
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| **Epochs** | 4 | |
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| **Batch size** | 32 (train) / 16 (eval) | |
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| **Optimizer** | `AdamW` (lr = 2 × 10⁻⁵, weight\_decay = 0.005) | |
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| **Scheduler** | Linear with 10 % warm-up | |
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| **Precision** | FP32 | |
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| **Hardware** | 1× GPU P100 | |
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### Training Loss Progression |
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| Epoch | Train Loss | Val Loss | |
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| ----: | ---------: | ---------: | |
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| 1 | 0.2661 | 0.0912 | |
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| 2 | 0.0784 | 0.0812 | |
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| 3 | 0.0520 | 0.0859 | |
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| 4 | **0.0419** | **0.0859** | |
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## Evaluation |
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| Metric | Value | |
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| ------------------- | ---------: | |
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| **Accuracy (test)** | **0.9750** | |
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| **Macro-F1 (test)** | **0.9749** | |
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|**Environmental Impact** | | |
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| ----------------------- | -------------------- | |
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| **Hardware** | 1× A100-40 GB | |
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| **Training time** | ≈ 4 × 7 min ≈ 0.47 h | |
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## Citation |
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``` |
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@misc{Sengil2025DisConBERT, |
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title = {Sengil/ytu-bert-base-dissonance-tr: A Three-way Dissonance/Consonance Classifier}, |
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author = {Şengil, Mert}, |
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year = {2025}, |
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url = {https://huggingface.co/Sengil/ytu-bert-base-dissonance-tr} |
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} |
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``` |
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--- |
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I would like to thank YTU for their open-source contributions that supported the development of this model. |
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For issues or questions, please open an issue on the Hub repo or contact **[mert sengil](https://www.linkedin.com/in/mertsengil/)**. |
