README.md

---
library_name: transformers
tags:
- Dissonant Detection
- transformers
- bert
language:
- tr
metrics:
- accuracy
base_model:
- ytu-ce-cosmos/turkish-base-bert-uncased
pipeline_tag: text-classification
---
# **Sengil/ytu-bert-base-dissonance-tr** 🇹🇷

A Turkish BERT-based model fine-tuned for three-way sentiment classification on single-sentence discourse.
This model categorizes input sentences into one of the following classes:

**Dissonance:** The sentence contains conflicting or contradictory sentiments  
    _e.g.,_ "Telefon çok kaliteli ve hızlı bitiyor şarjı"  
**Consonance:** The sentence expresses harmonizing or mutually reinforcing sentiments  
    _e.g.,_ "Yemeklerde çok güzel manzarada mükemmel"  
**Neither:** The sentence is neutral or does not clearly reflect either dissonance or consonance  
    _e.g.,_ "Bu gün hava çok güzel"


The model was trained on 37,368 Turkish samples and evaluated on two separate sets of 4,671 samples each.
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.

|**Model Details**     |                                                       |
| -------------------- | ----------------------------------------------------- |
| **Developed by**     | Mert Şengil                                           |
| **Model type**       | `BertForSequenceClassification`                       |
| **Base model**       | `ytu-ce-cosmos/turkish-base-bert-uncased`             |
| **Languages**        | `tr` (Turkish)                                        |
| **License**          | Apache-2.0                                            |
| **Fine-tuning task** | 3-class sentiment (dissonance / consonance / neither) |

## Uses

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "Sengil/ytu-bert-base-dissonance-tr"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "onu çok seviyorum ve güvenmiyorum."
text = text.replace("I", "ı").lower()
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
with torch.no_grad():
    logits = model(**inputs).logits

label_id = int(logits.argmax())
id2label = {0: "Dissonance", 1: "Consonance", 2: "Neither"}

print(f"{{'label': '{id2label[label_id]}','score':{logits.argmax()}}}")
```
output:
```
{'label': 'Dissonance','score':0}
```
|**Training Details**    |                                                |
| ---------------------- | ---------------------------------------------- |
| **Training samples**   | 37 368                                         |
| **Validation samples** | 4 671                                          |
| **Test samples**       | 4 671                                          |
| **Epochs**             | 4                                              |
| **Batch size**         | 32 (train) / 16 (eval)                          |
| **Optimizer**          | `AdamW` (lr = 2 × 10⁻⁵, weight\_decay = 0.005) |
| **Scheduler**          | Linear with 10 % warm-up                       |
| **Precision**          | FP32                                           |
| **Hardware**           | 1× GPU P100                           |

### Training Loss Progression
| Epoch | Train Loss |   Val Loss |
| ----: | ---------: | ---------: |
|     1 |     0.2661 |     0.0912 |
|     2 |     0.0784 |     0.0812 |
|     3 |     0.0520 |     0.0859 |
|     4 | **0.0419** | **0.0859** |

## Evaluation

| Metric              |      Value |
| ------------------- | ---------: |
| **Accuracy (test)** | **0.9750** |
| **Macro-F1 (test)** | **0.9749** |

|**Environmental Impact** |                      |
| ----------------------- | -------------------- |
| **Hardware**            | 1× A100-40 GB        |
| **Training time**       | ≈ 4 × 7 min ≈ 0.47 h |


## Citation

```
@misc{Sengil2025DisConBERT,
  title   = {Sengil/ytu-bert-base-dissonance-tr: A Three-way Dissonance/Consonance Classifier},
  author  = {Şengil, Mert},
  year    = {2025},
  url     = {https://huggingface.co/Sengil/ytu-bert-base-dissonance-tr}
}
```

---
I would like to thank YTU for their open-source contributions that supported the development of this model.
For issues or questions, please open an issue on the Hub repo or contact **[mert sengil](https://www.linkedin.com/in/mertsengil/)**.