Model Card for emresallak/bert-turkish-biomedical

Model Description

This model is a Turkish biomedical BERT model fine-tuned for natural language processing tasks in the biomedical domain.

It is based on a pre-trained Turkish BERT model and adapted to better understand domain-specific terminology such as medical concepts, clinical language, and biomedical text.

• Developed by: emresallak
• Model type: BERT
• Language(s): Turkish
• License: apache-2.0 (recommended, change if different)
• Finetuned from model: dbmdz/bert-base-turkish-cased

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Model Sources

• Repository: https://huggingface.co/emresallak/bert-turkish-biomedical

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Uses

Direct Use

This model can be used for Turkish biomedical NLP tasks such as:

• Text classification
• Named entity recognition (NER)
• Semantic understanding of medical text

Downstream Use

The model can be further fine-tuned for specific tasks like:

• Clinical text classification
• Medical entity extraction
• Question answering in the biomedical domain

Out-of-Scope Use

This model should NOT be used for:

• Medical diagnosis
• Clinical decision-making
• Any real-world healthcare application without expert validation

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Bias, Risks, and Limitations

• The model inherits biases from the original training data
• Biomedical datasets may contain incomplete or domain-specific biases
• Performance may degrade on:
  • Non-biomedical Turkish text
  • Informal language
  • Out-of-distribution inputs

Recommendations

Users should:

• Validate outputs before real-world use
• Avoid using the model in high-stakes scenarios
• Perform task-specific fine-tuning when possible

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How to Get Started with the Model

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("emresallak/bert-turkish-biomedical")
model = AutoModel.from_pretrained("emresallak/bert-turkish-biomedical")