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--- |
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language: |
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- tr |
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library_name: transformers |
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license: mit |
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metrics: |
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- f1 |
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- accuracy |
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- recall |
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tags: |
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- ner |
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- token-classification |
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- turkish |
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--- |
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# Model Card for Turkish Named Entity Recognition Model |
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<!-- Provide a quick summary of what the model is/does. --> |
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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. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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. |
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- **Developed by:** Ezel Bayraktar (ai@bayraktarlar.dev) |
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- **Model type:** Token Classification (Named Entity Recognition) |
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- **Language(s) (NLP):** Turkish |
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- **License:** MIT |
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- **Finetuned from model:** dbmdz/bert-base-turkish-uncased |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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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). |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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The model can be integrated into larger natural language processing pipelines for Turkish, such as information extraction systems, question answering, or text summarization. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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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. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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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. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import pipeline |
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nert = pipeline('ner', model='TerminatorPower/nerT', tokenizer='TerminatorPower/nerT') |
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answer = nert("Mustafa Kemal Atatürk, 19 Mayıs 1919'da Samsun'a çıktı.") |
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print(answer) |
