Welcome to the intriguing world of automated question generation and answer extraction! In this article, we’ll walk through the process of creating a model that can generate questions from Turkish texts and offer answers using the mt5-base language model.

What You Need

Before we begin, make sure you have the following requirements in place:

• Python installed on your machine
• The core.api module available
• Access to the mt5-base language model
• Relevant training data (TQuADv2-train, TQuADv2-val, XQuAD.tr)

Setting Up Your Model

To start with, we will set up our model using the following Python script. Think of this script as the ‘recipe’ for our automated question-answering dish, where each function serves different purposes in preparing our final output.

from core.api import GenerationAPI

# Initialize the generation API with mt5-base model
generation_api = GenerationAPI(mt5-base-3task-highlight-combined3)

Automated Question Generation

Now that you’ve set up your model, let’s generate questions automatically! The process is akin to a chef creating a menu from a list of ingredients. In our case, we take our Turkish text (the ingredients) and produce a list of questions (the menu).

# Define the context from which questions will be generated
context = "Bu modelin eğitiminde, Türkçe soru cevap verileri kullanılmıştır..."

# Generate questions based on the given context
questions = generation_api(task='question-generation', context=context)

Answer Extraction

After generating questions, we can also extract answers from the text. This part is like a quality check in our cooking process, ensuring that our menu (questions) is well-informed with accurate ingredients (answers). Here’s how we do it:

# Define a question based on the context
question = "Bu model ne işe yarar?"

# Extract the answer for the defined question
answer = generation_api(task='question-answering', context=context, question=question)

Troubleshooting Common Issues

As with any recipe, things might not go exactly as planned. If you encounter issues, here are some troubleshooting ideas:

• Module Not Found: Ensure that all necessary modules are correctly installed.
• Training Data Issues: Verify that the training data files are correctly formatted and accessible.
• API Not Responding: Check your internet connection and ensure the API is powered on.

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Conclusion

By implementing the steps detailed in this article, you can harness the power of automated question generation and answer extraction in Turkish using the mt5-base model. The potential applications are vast, from educational tools to enhancing user interactions in applications.

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.