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--- |
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language: |
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- tr |
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base_model: |
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- dbmdz/bert-base-turkish-cased |
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pipeline_tag: text-classification |
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tags: |
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- text-classification |
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- multi-label-classification |
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- personality |
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- bert |
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- pytorch |
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- transformers |
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- turkish |
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- classification |
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- human-resources |
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- custom-trained |
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license: apache-2.0 |
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--- |
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# bert\_turkish\_personality\_analysis |
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This repository hosts a **Turkish BERT model fine-tuned for multi-label personality trait classification**. |
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Built on top of `dbmdz/bert-base-turkish-cased`, this model predicts psychological and professional personality traits from Turkish text input. |
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## 🎯 Task: Multi-label Personality Trait Detection |
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Given a CV, personal statement, or written expression, the model assigns **zero or more traits** from the following set: |
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### 🏷️ Supported Labels |
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* `özgüvenli` – confident |
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* `içe kapanık` – introverted |
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* `lider` – leader |
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* `takım oyuncusu` – team player |
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* `kararsız` – indecisive |
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* `abartılı` – exaggerated |
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* `profesyonel` – professional |
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* `deneyimli` – experienced |
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The model supports **multi-label classification** using a sigmoid activation and thresholding logic. |
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## 🔧 Usage Example |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# Load tokenizer and model |
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model_name = "MUR55/bert_turkish_personality_analysis" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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# Sample text |
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text = "5 yıllık yöneticilik tecrübemle liderlik becerilerimi geliştirdim, aynı zamanda ekip çalışmalarına önem veririm." |
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# Tokenize and predict |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
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outputs = model(**inputs) |
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probs = torch.sigmoid(outputs.logits) |
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# Threshold to determine label presence |
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threshold = 0.5 |
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labels = ["özgüvenli", "içe kapanık", "lider", "takım oyuncusu", "kararsız", "abartılı", "profesyonel", "deneyimli"] |
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predicted = [label for label, prob in zip(labels, probs[0]) if prob >= threshold] |
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print("Predicted traits:", predicted) |
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``` |
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## 🧠 Model Details |
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* **Base model:** [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) |
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* **Architecture:** BERT with a linear classification head |
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* **Task type:** Multi-label classification |
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* **Loss Function:** Binary Cross Entropy with Logits |
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* **Training Data:** Custom Turkish dataset with personality trait annotations (e.g., CVs, social texts) |
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## 📈 Performance |
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Model was evaluated on a held-out portion of the dataset. Replace below with your real metrics: |
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| Metric | Value | |
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| --------- | ----- | |
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| Accuracy | 0.92 | |
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| F1-Score | 0.94 | |
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| Precision | 0.91 | |
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| Recall | 0.96 | |
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## 🔍 Applications |
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* CV analysis and candidate profiling |
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* Smart recruiting and HR systems |
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* Social media or forum persona evaluation |
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* Turkish personality-aware recommendation systems |
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## 📁 Files Included |
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* `pytorch_model.bin` – fine-tuned model weights |
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* `config.json` – model configuration |
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* `tokenizer_config.json`, `vocab.txt` – tokenizer files |
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## 🤝 Acknowledgments |
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This project builds upon [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased). Thanks to the Turkish NLP community for contributions and datasets. |
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## 📬 Contact |
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If you have questions or suggestions, feel free to open an issue on the [model page](https://huggingface.co/MUR55/bert_turkish_personality_analysis) or contact the author. |
