Beta

Explore
Marketplace
Neural Labs
Chat
Wallet
Docs

Beta

Explore Marketplace Neural Labs Wallet Docs

Build

Manage

My assets Manage wallet Organization

Community

Discord X (Twitter)Telegram

Docs Privacy Policy Terms and Conditions

Proud member of

Proud member of

Docs Cookie Policy

Privacy Policy Terms and Conditions

© 2026 AlphaNeural AI. All rights reserved.

bert-turkish-sentiment-analysis – AI Model by yusufalt46 | AlphaNeural AI

You can deploy this model and start earning money today!

yusufalt46/
bert-turkish-sentiment-analysis

like

0

text-classification

winvoker/turkish-sentiment-analysis-dataset

dbmdz/bert-base-turkish-cased

text-embeddings-inference

endpoints_compatible

Views

No views yet

Model Description

This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on the winvoker/turkish-sentiment-analysis-dataset.

Shuffle function was applied during preprocessing.
Training dataset: 229,483 samples
Test dataset: 57,371 samples
Total dataset: 286,854 samples

This model is designed for Turkish sentiment analysis, classifying texts as either Positive (1) or Negative (0).

Training Details:

Training dataset

Dataset: winvoker/turkish-sentiment-analysis-dataset
Training: 229,483 samples
Testing: 57,371 samples
Preprocessing: shuffle and truncate/pad to 128 tokens

Training Procedure

Fine-tuned using Hugging Face Transformers Trainer

Learning rate: 2e-5
Batch size: 16
Epochs: 2
Optimizer: AdamW
Weight decay: 0.01

Evaluation

Eval Loss: 0.1505
Accuracy: 0.9614
F1 Score: 0.9767
Epoch: 2

Label Mapping

LABEL_1 = Positive (1)
LABEL_0 = Negative (0)
Developed by: Yusuf Altunbaş
Model type: Sequence Classification
Language(s): Turkish
License: MIT
Finetuned from model: dbmdz/bert-base-turkish-cased

Model Sources

Repository: GitHub Notebook

Uses

Direct Use

The model can be directly used for predicting the sentiment of Turkish text using the Hugging Face transformers library.

Downstream Use

Can be integrated into larger NLP pipelines, sentiment analysis dashboards, or recommendation systems.

Out-of-Scope Use

This model is not trained for neutral or multi-class sentiment beyond Positive/Negative.
Not suitable for languages other than Turkish.

Bias, Risks, and Limitations

The model is trained on winvoker/turkish-sentiment-analysis-dataset.
It may reflect biases present in the dataset.
Misuse or misinterpretation of predictions can occur; always review results in context.

How to Get Started with the Model

Using Hugging Face Pipeline

python

from transformers import pipeline

text = "Bugün çok mutluyum!"
model_id = "yusufalt46/bert-turkish-sentiment-analysis"

classifier = pipeline("text-classification", model=model_id, tokenizer=model_id)
preds = classifier(text)
print(preds)

# Example output:
# [{'label': 'LABEL_1', 'score': 0.9823}]  # 1 = Positive

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("yusufalt46/bert-turkish-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("yusufalt46/bert-turkish-sentiment-analysis")

text = "Bu ürün harika!"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
outputs = model(**inputs)
pred = torch.argmax(outputs.logits, dim=-1)
print(pred.item())  # 1: Positive, 0: Negative

Yusuf Altunbaş, bert-turkish-sentiment-analysis, Hugging Face Model Hub, 2025