Keigo_0

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Keigo_0

It achieves the following results on the evaluation set:

• Loss: 0.2091

Model description

The model is designed for sentence-level rewriting, specifically transforming rude, informal, aggressive, or slang-heavy Turkish sentences into polite, formal, and respectful expressions, while preserving the original meaning.

Keigo_0 focuses on tone transformation, not content moderation or censorship.

The goal is to provide a culturally appropriate, professional rephrasing aligned with formal Turkish usage.

Intended uses & limitations

Intended uses

-Rewriting rude, aggressive, or slang Turkish sentences into: Formal - Polite - Professional tone

-Improving user-facing messages in: Applications - Customer support systems - Feedback forms

Limitations

-The model is not a chatbot and should not be used for open-ended conversation. -It is optimized for short, single-sentence inputs; long paragraphs may produce degraded results. -Output quality depends on input clarity; ambiguous or incomplete inputs may lead to generic responses.

Training and evaluation data

The model was fine-tuned on a custom-curated Turkish dataset consisting of paired sentence examples.

Each training sample contains: Input: An informal, rude, slang-heavy, or emotionally aggressive Turkish sentence

Target: A rewritten version of the same sentence in a formal, polite, and professional tone, while preserving the original meaning Data characteristics

Language: Turkish

Format: Input–target sentence pairs

Style focus: Tone softening Formalization Politeness transformation Sentence length: Short, single-sentence inputs

Domain: Everyday user complaints Informal speech Emotional or frustrated expressions Data sourcing The dataset was manually created and curated for this project. No personally identifiable information (PII) was intentionally included.

-Evaluation-

Evaluation was performed using: A held-out validation split from the same dataset Cross-entropy loss as the primary metric

The evaluation focuses on: Meaning preservation Tone transformation quality Fluency and grammatical correctness

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0004
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 40

Training results

training loss=0.064679 eval loss: 0.209111

Framework versions

• Transformers 4.57.3
• Pytorch 2.9.1+cu128
• Datasets 4.4.2
• Tokenizers 0.22.1