bert turkish question answering

Introduction

The BERT-Turkish-Question-Answering model is designed for question-answering tasks in the Turkish language. It utilizes Transformers and is compatible with both PyTorch and JAX frameworks.

Architecture

This model is based on the BERT architecture, specifically fine-tuned for the Turkish language to perform question-answering tasks. It leverages pre-trained BERT capabilities to understand and process Turkish text, answering questions based on given contexts.

Training

The model has been fine-tuned for question-answering, using a dataset of Turkish text that allows it to generate accurate answers to questions based on contextual input.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install Transformers Library:

   pip install transformers
   

2. Load the Model and Tokenizer:

   from transformers import pipeline
   nlp = pipeline('question-answering', model='lserinol/bert-turkish-question-answering', tokenizer='lserinol/bert-turkish-question-answering')
   

3. Use the Model:

   nlp({
       'question': "Ankara'da kaç ilçe vardır?",
       'context': "Türkiye'nin başkenti Ankara'dır. ..."
   })
   

4. Advanced Usage with PyTorch:

   from transformers import AutoTokenizer, AutoModelForQuestionAnswering
   import torch
   
   tokenizer = AutoTokenizer.from_pretrained("lserinol/bert-turkish-question-answering")
   model = AutoModelForQuestionAnswering.from_pretrained("lserinol/bert-turkish-question-answering")
   
   text = "Ankara'nın başkent ilan edilmesinin ardından ..."
   questions = ["Ankara kaç yılında başkent oldu?", ...]
   
   for question in questions:
       inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")
       answer_start_scores, answer_end_scores = model(**inputs)
       ...
   

Cloud GPUs: Consider using cloud services like AWS, Google Cloud, or Azure for GPU support if processing large datasets or requiring faster computation.

License

The BERT-Turkish-Question-Answering model is available under the MIT License, allowing for broad use and modification.