--- language: tr license: other license_name: siriusai-premium-v1 license_link: LICENSE tags: - turkish - text-classification - bert - nlp - transformers - siriusai - production-ready - enterprise base_model: dbmdz/bert-base-turkish-uncased datasets: - custom metrics: - f1 - precision - recall - accuracy - mcc library_name: transformers pipeline_tag: text-classification model-index: - name: emotion-tr results: - task: type: text-classification name: Text Classification metrics: - type: f1 value: 0.9744976471619214 name: Macro F1 - type: mcc value: 0.9610214790438847 --- # emotion-tr - Turkish Emotion Classification Model

Hugging Face Production Ready Turkish Text Classification

This model is designed for the **classification of emotional sentiments** in Turkish text. *Developed by SiriusAI Tech Brain Team* --- ## Mission > **To provide advanced sentiment analysis capabilities for Turkish text, empowering businesses and researchers to understand emotional tones effectively.** The `emotion-tr` model leverages the **BERT architecture** to deliver high-performance text classification, specifically tailored for the Turkish language. By analyzing sentiments as negative, neutral, or positive, this model facilitates a deeper understanding of customer feedback, social media interactions, and other textual data, proving essential for sentiment-driven applications in various domains. ### Why This Model Matters - **High Accuracy**: Achieves over **97% accuracy**, making it reliable for various applications. - **Robust Performance**: Exhibits superior performance across all sentiment categories. - **Enterprise-Ready**: Designed to meet the demands of production environments with efficient response times. - **Customizable**: Can be fine-tuned for specific applications beyond emotion classification. - **Comprehensive Documentation**: Provides extensive guidance for integration and usage. --- ## Model Overview | Property | Value | |----------|-------| | **Architecture** | BertForSequenceClassification | | **Base Model** | `dbmdz/bert-base-turkish-uncased` | | **Task** | Text Classification | | **Language** | Turkish (tr) | | **Categories** | 3 labels | | **Model Size** | ~110M parameters | | **Inference Time** | ~10-15ms (GPU) / ~40-50ms (CPU) | --- ## Performance Metrics ### Final Evaluation Results | Metric | Score | Description | |--------|-------|-------------| | **Macro F1** | **0.9744976471619214** | Harmonic mean of precision and recall | | **MCC** | **0.9610214790438847** | Matthews Correlation Coefficient | | **Accuracy** | **97.5557461406518%** | Overall accuracy of the model | ### Per-Class Performance | Category | Accuracy | Correct | Total | |----------|----------|---------|-------| | **negatif** | 97.0% | 700 | 722 | | **notr** | 98.0% | 1,069 | 1,091 | | **pozitif** | 97.5% | 506 | 519 | --- ## Dataset ### Dataset Statistics | Split | Samples | Purpose | |-------|---------|---------| | **Train** | 9,322 | Model training | | **Test** | 2,332 | Model evaluation | | **Total** | 11,654 | Complete dataset | ### Category Distribution | Category | Samples | Percentage | Description | |----------|---------|------------|-------------| | **sentiment_3class** | 11,654 | 100.0% | sentiment_3class category | ### Subcategory Breakdown | Category | Subcategories | |----------|---------------| | **sentiment_3class** | pozitif, negatif, notr | --- ## Label Definitions | Label | ID | Description | Turkish Examples | |-------|-----|-------------|------------------| | **negatif** | 0 | Indicates negative sentiment | "Bu çok kötü bir film." "Hizmet berbattı." | | **notr** | 1 | Indicates neutral sentiment | "Bugün hava güzel." "Toplantı yapıldı." | | **pozitif** | 2 | Indicates positive sentiment | "Harika bir deneyim!" "Çok memnun kaldım." | ### Important: Category Boundaries When classifying sentiments, the distinction between **notr** and **negatif** can be subtle; for instance, "Bu film sıradan" might be interpreted as neutral, while "Bu film kötü" is clearly negative. --- ## Training Procedure ### Hyperparameters | Parameter | Value | |-----------|-------| | **Base Model** | `dbmdz/bert-base-turkish-uncased` | | **Max Sequence Length** | 128 tokens | | **Batch Size** | 16 | | **Learning Rate** | 2e-5 | | **Epochs** | 3 | | **Optimizer** | AdamW | | **Weight Decay** | 0.01 | | **Loss Function** | CrossEntropyLoss / Focal Loss | | **Problem Type** | Single-label Classification | ### Training Environment | Resource | Specification | |----------|---------------| | **Hardware** | Apple Silicon (MPS) / CUDA GPU | | **Framework** | PyTorch + Transformers | | **Training Time** | Varies based on dataset size | --- ## Usage ### Installation ```bash pip install transformers torch ``` ### Quick Start ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model_name = "hayatiali/emotion-tr" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) model.eval() LABELS = ["negatif", "notr", "pozitif"] def predict(text): inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128) with torch.no_grad(): outputs = model(**inputs) probs = torch.softmax(outputs.logits, dim=-1)[0] scores = {label: float(prob) for label, prob in zip(LABELS, probs)} primary = max(scores, key=scores.get) return {"category": primary, "confidence": scores[primary], "all_scores": scores} # Examples print(predict("Bu film harika!")) ``` ### Production Class ```python class EmotionClassifier: LABELS = ["negatif", "notr", "pozitif"] def __init__(self, model_path="hayatiali/emotion-tr"): self.tokenizer = AutoTokenizer.from_pretrained(model_path) self.model = AutoModelForSequenceClassification.from_pretrained(model_path) self.device = "cuda" if torch.cuda.is_available() else "cpu" self.model.to(self.device).eval() def predict(self, text: str) -> dict: inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=128) inputs = {k: v.to(self.device) for k, v in inputs.items()} with torch.no_grad(): logits = self.model(**inputs).logits probs = torch.softmax(logits, dim=-1)[0].cpu().numpy() scores = dict(zip(self.LABELS, probs)) return {"category": max(scores, key=scores.get), "confidence": max(scores.values()), "scores": scores} ``` ### Batch Inference ```python def predict_batch(texts: list, batch_size: int = 32) -> list: results = [] for i in range(0, len(texts), batch_size): batch = texts[i:i + batch_size] inputs = tokenizer(batch, return_tensors="pt", truncation=True, max_length=128, padding=True) inputs = {k: v.to(device) for k, v in inputs.items()} with torch.no_grad(): probs = torch.softmax(model(**inputs).logits, dim=-1).cpu().numpy() for prob in probs: scores = dict(zip(LABELS, prob)) results.append(scores) return results ``` --- ## Limitations & Known Issues ### ⚠️ Model Limitations | Limitation | Details | Impact | |------------|---------|--------| | **Context Sensitivity** | The model may misclassify sentiments in ambiguous contexts | Potentially inaccurate predictions | | **Domain Adaptability** | Performance may vary across different domains (e.g., social media vs. formal texts) | Requires further fine-tuning for specific applications | | **Language Nuances** | Subtle linguistic features unique to Turkish may not be perfectly captured | May lead to classification errors in nuanced cases | ### ⚠️ Production Deployment Considerations | Consideration | Details | Recommendation | |---------------|---------|----------------| | **Model Size** | The model is approximately 110M parameters | Ensure adequate resources for deployment | | **Latency** | Inference time may vary with input length and server load | Optimize batch sizes for improved performance | ### Not Suitable For - Legal document analysis - Medical diagnosis based on text - Any critical decision-making without human oversight --- ## Ethical Considerations ### Intended Use - Sentiment analysis in customer feedback - Emotional tone detection in social media posts - Market research and analysis ### Risks - **Bias in Data**: The model may reflect biases present in the training data, leading to skewed results. - **Misinterpretation of Sentiments**: Incorrect sentiment classification could misguide businesses in decision-making. ### Recommendations 1. **Human Oversight**: Always accompany model predictions with human judgment. 2. **Monitoring**: Regularly assess model performance and retrain as necessary. 3. **Updates**: Stay informed about updates to the model and fine-tune based on new data. --- ## Technical Specifications ### Model Architecture ``` BertForSequenceClassification( (bert): BertModel( (embeddings): BertEmbeddings (encoder): BertEncoder (12 layers) (pooler): BertPooler ) (dropout): Dropout(p=0.1) (classifier): Linear(in_features=768, out_features=3) ) Total Parameters: ~110M ``` ### Input/Output - **Input**: Turkish text (max 128 tokens) - **Output**: 3-dimensional probability vector - **Tokenizer**: BERTurk WordPiece (32k vocab) --- ## Citation ```bibtex @misc{emotion-tr-2025, title={emotion-tr - Turkish Text Classification Model}, author={SiriusAI Tech Brain Team}, year={2025}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/hayatiali/emotion-tr}}, note={Fine-tuned from dbmdz/bert-base-turkish-uncased} } ``` --- ## Model Card Authors **SiriusAI Tech Brain Team** ## Contact - **Email**: info@siriusaitech.com - **Repository**: [GitHub](https://github.com/sirius-tedarik) --- ## Changelog ### v1.0 (Current) - Initial release - 3-category text classification - Macro F1: 0.9744976471619214, MCC: 0.9610214790438847 --- **License**: SiriusAI Tech Premium License v1.0 **Commercial Use**: Requires Premium License. Contact: info@siriusaitech.com **Free Use Allowed For**: - Academic research and education - Non-profit organizations (with approval) - Evaluation (30 days) **Disclaimer**: This model is designed for text classification applications. Always implement with appropriate safeguards and human oversight. Model predictions should inform decisions, not replace human judgment.