In our quest to bridge the gap in artificial intelligence for the Turkish language, we often turn to specific models like BERT. In this blog, we’re diving into the exciting journey of fine-tuning the Turkish-BERT model specifically tailored for Question-Answering tasks using the Turkish version of SQuAD – TQuAD. Let’s break it down step by step!

Step 1: Set Up Your Environment

Before you embark on your fine-tuning adventure, ensure that you have the necessary dependencies installed in your Python environment. You’ll need the Transformers library from Hugging Face, along with PyTorch.

• Install Transformers: pip install transformers
• Ensure PyTorch is installed: pip install torch

Step 2: Prepare Your Dataset

The TQuAD dataset, which you will use for training, is crucial. You can download it from here. This dataset consists of questions and passages in Turkish, enabling the model to learn how to provide answers based on context.

Step 3: Fine-Tuning the Model

Think of fine-tuning your model as teaching a child a new language using a storybook. Each page (or data sample) guides the child (the model) to learn how to answer questions (find meanings) based on what they’ve read (context). Here’s the code to fine-tune your model:

python3 run_squad.py 
   --model_type bert 
   --model_name_or_path dbmdz/bert-base-turkish-uncased  
   --do_train  
   --do_eval  
   --train_file trainQ.json  
   --predict_file dev1.json  
   --per_gpu_train_batch_size 12   
   --learning_rate 3e-5   
   --num_train_epochs 5.0   
   --max_seq_length 384   
   --doc_stride 128   
   --output_dir .model

This command will initiate the training process, adjusting the model’s weights to suit the Turkish language’s nuances present in the TQuAD dataset.

Step 4: Example Usage

Once your model is fine-tuned, you can apply it in your project. Loading the model is akin to opening your favorite book to read a story. Here’s how you can do it:

from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline

tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-squad")
model = AutoModelForQuestionAnswering.from_pretrained("savasy/bert-base-turkish-squad")

nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)

Step 5: Putting It to The Test

Now that the model is ready, test its abilities. Think of this as quizzing the child you’ve taught. Provide a context and a question to see if it can answer accurately.

sait = "ABASIYANIK, Sait Faik. Hikayeci (Adapazarı 23 Kasım 1906-İstanbul 11 Mayıs 1954). İlk öğrenimine Adapazarı’nda Rehber-i Terakki Mektebi’nde başladı..." 

print(nlp(question="Ne zaman avare bir hayata başladı?", context=sait))
print(nlp(question="Sait Faik hangi Lisede orta öğrenimini tamamladı?", context=sait))

Troubleshooting Tips

• If your model doesn’t seem to understand the context, double-check your training data. Ensure that it’s properly formatted.
• If you’re running out of memory, try reducing the per_gpu_train_batch_size.
• Make sure that your PyTorch version is compatible with your CUDA version if you’re using GPU acceleration.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.