msamilim/microsoft_mdeberta_v3_finetuned_optuna_turkish_sentiment_v02

Turkish Sentiment Analysis (3-class) — Fine-tuned

Overview

This model is a fine-tuned version of microsoft/mdeberta-v3-base for 3-class Turkish sentiment analysis. It was trained on an imbalanced dataset of e-commerce product reviews, and hyperparameters were optimized with Optuna to obtain the most effective fine-tuning configuration.

Bu model, üç sınıflı Türkçe duygu analizi için microsoft/mdeberta-v3-base taban alınarak ince ayar (fine-tuning) yapılmış bir sürümdür. Model, dengesiz bir e-ticaret ürün yorumları veri kümesi üzerinde eğitilmiş; en etkili ince ayar yapılandırmasını elde etmek için hiperparametreler Optuna ile optimize edilmiştir.

Intended Use

• Product reviews classification
• Social media analysis
• Customer feedback analysis
• Brand monitoring
• Market research
• Customer service optimization
• Competitive intelligence

Model Details

Field	Value
Model Name	msamilim/microsoft_mdeberta_v3_finetuned_optuna_turkish_sentiment_v02
Base Model	microsoft/mdeberta-v3-base
Task	Sentiment Analysis
Language	Turkish
Fine-Tuning Dataset	Turkish E-Commerce Product Reviews Dataset
Number of Labels	3
Problem Type	Single-label classification
License	apache-2.0
Fine-Tuning Framework	Hugging Face Transformers

Dataset

The dataset is a Turkish three-class sentiment corpus (negatif / notr / pozitif). Overall distribution and per-split distributions are shown below.

Dataset Distribution (Overall)

LabelID	LabelName	Count	Ratio (%)
0	negatif	9462	18.86
1	notr	746	1.49
2	pozitif	39952	79.65
—	Total	50160	100.00

Training Procedure

• Objective metric: eval_macro_f1
• Hyperparameter Optimization Techniques: Optuna

HPO Parameter Ranges

params = {
        "learning_rate": trial.suggest_float("learning_rate", 5e-6, 5e-5, log=True),
        "per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [16, 32]),
        "per_device_eval_batch_size":  trial.suggest_categorical("per_device_eval_batch_size",  [32]),
        "weight_decay": trial.suggest_float("weight_decay", 0.0, 0.1),
        "warmup_ratio": trial.suggest_float("warmup_ratio", 0.0, 0.2),
        "num_train_epochs": trial.suggest_int("num_train_epochs", 6, 8),
        "gradient_accumulation_steps": trial.suggest_categorical("gradient_accumulation_steps", [1, 2, 4]),
    }

Best Trial Hyperparameters

{
  "learning_rate": 1.730838540089638e-05,
  "per_device_train_batch_size": 32,
  "per_device_eval_batch_size": 32,
  "weight_decay": 0.0479900060506278,
  "warmup_ratio": 0.185600998141453,
  "num_train_epochs": 8,
  "gradient_accumulation_steps": 1
}

Evaluation Results

These results are the evaluations recorded during the final fine-tuning training process.

label	precision	recall	f1-score
negatif	0.8417	0.7965	0.8185
notr	0.1754	0.1111	0.1361
pozitif	0.9501	0.9687	0.9593
accuracy			0.9234
micro avg	0.9234	0.9234	0.9234
macro avg	0.6557	0.6254	0.6379
weighted avg	0.9181	0.9234	0.9205

Epoch-wise Metrics

epoch	train_loss	eval_loss	eval_macro_f1
1	0.4086	0.2327	0.5949
2	0.2072	0.2209	0.5970
3	0.1801	0.2176	0.5968
4	0.1572	0.2387	0.5966
5	0.1363	0.2606	0.6043
6	0.1172	0.2695	0.6273
7	0.1001	0.2986	0.6366
8	0.0883	0.3331	0.6379

How to use - Pipeline

from transformers import pipeline

# Load the classification pipeline with the specified model
model_name = "msamilim/microsoft_mdeberta_v3_finetuned_optuna_turkish_sentiment_v02"
pipe = pipeline("text-classification", model=model_name)

# Classify a new sentence
sentence = "Güzel ürün, tavsiye ederim."
result = pipe(sentence)

# Print the result
print(result)

# Example output : 
# [{'label': 'pozitif', 'score': 0.9998408555984497}]

How to use - Full Classification

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "msamilim/microsoft_mdeberta_v3_finetuned_optuna_turkish_sentiment_v02"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def predict_sentiment(texts):
    inputs = tokenizer(texts, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    id2label = {    0: "Negatif",    1: "Nötr",    2: "Pozitif"}
    return [id2label[p] for p in torch.argmax(probabilities, dim=-1).tolist()]

texts = [
     "Güzel ürün, tavsiye ederim kullanılır.", 
     "Ürün çok güzel ve kaliteli. Maalesef yüzüme uymadığı için iade etmek zorunda kaldım.", 
     "Keşke aldıktan sonra indirime girmeseydi.",
     "Daha soluk ve mat yapısı var beğenmedim .",
]

for text, sentiment in zip(texts, predict_sentiment(texts)):
    print(f"Text: {text}\nSentiment: {sentiment}\n")

# Example output : 
# Text: Güzel ürün, tavsiye ederim kullanılır.
# Sentiment: Pozitif
# Text: Ürün çok güzel ve kaliteli. Maalesef yüzüme uymadığı için iade etmek zorunda kaldım.
# Sentiment: Pozitif
# Text: Keşke aldıktan sonra indirime girmeseydi.
# Sentiment: Negatif
# Text: Daha soluk ve mat yapısı var beğenmedim .
# Sentiment: Negatif

Framework versions

• transformers==4.57.0
• torch==2.8.0+cu128
• datasets==4.2.0
• accelerate==1.10.1
• evaluate==0.4.6
• python==3.11.13