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.