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