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

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.