---
language:
- tr
library_name: transformers
license: mit
metrics:
- f1
- accuracy
- recall
tags:
- ner
- token-classification
- turkish
---

# Model Card for Turkish Named Entity Recognition Model

<!-- Provide a quick summary of what the model is/does. -->

This model performs Named Entity Recognition (NER) for Turkish text, identifying and classifying entities such as person names, locations, and organizations. Model got 0.9599 F1 on validation set.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is a fine-tuned BERT model for Turkish Named Entity Recognition (NER). It is based on the `dbmdz/bert-base-turkish-uncased` model and has been trained on a custom Turkish NER dataset.

- **Developed by:** Ezel Bayraktar (ai@bayraktarlar.dev)
- **Model type:** Token Classification (Named Entity Recognition)
- **Language(s) (NLP):** Turkish
- **License:** MIT
- **Finetuned from model:** dbmdz/bert-base-turkish-uncased



### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

This model can be used directly for Named Entity Recognition tasks in Turkish text. It identifies and labels entities such as person names (PER), locations (LOC), and organizations (ORG).

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

The model can be integrated into larger natural language processing pipelines for Turkish, such as information extraction systems, question answering, or text summarization.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

This model should not be used for languages other than Turkish or for tasks beyond Named Entity Recognition. It may not perform well on domain-specific text or newly emerging named entities not present in the training data.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The model may inherit biases present in the training data or the pre-trained BERT model it was fine-tuned from. It may not perform consistently across different domains or types of Turkish text.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should evaluate the model's performance on their specific domain and use case. For critical applications, human review of the model's outputs is recommended.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import pipeline

nert = pipeline('ner', model='TerminatorPower/nerT', tokenizer='TerminatorPower/nerT')
answer = nert("Mustafa Kemal Atatürk, 19 Mayıs 1919'da Samsun'a çıktı.")
print(answer)