In today’s digital age, integrating effective chatbots for answering frequently asked questions (FAQs) is essential for enhancing customer service. This article will guide you through using a fine-tuned Turkish NLI model for FAQ retrieval.

Model Overview

This model is a fine-tuned version of mysbert-base-turkish-cased-nli-mean, designed specifically for extracting answers to common questions. It utilizes the powerful architecture of BERT, specifically the dbmdzbert-base-turkish-cased model, and has been trained on the Turkish subset of the clipsmqa dataset. The training involved cleaning and filtering data, using a Multiple Negatives Symmetric Ranking loss for optimization.

Necessary Additions Before Using the Model

Before finetuning, two special tokens were added: Q for questions and A for answers. It is essential to prepend these tokens to the sequences before passing them into the model for accurate processing.

Usage Guide

Utilizing this fine-tuned model for FAQ retrieval involves a series of steps that we’ll go through in detail. Let’s break it down with an analogy:

Imagine you’re in a library where books represent different questions and each book contains the answers you’re seeking. The model acts like a librarian who helps you find the right book based on your question.

Step 1: Setting Up Your Questions and Answers

First, define your array of questions and answers as follows:

questions = [
    "Merhaba",
    "Nasılsın?",
    "Bireysel araç kiralama yapıyor musunuz?",
    "Kurumsal araç kiralama yapıyor musunuz?"
]
answers = [
    "Merhaba, size nasıl yardımcı olabilirim?",
    "İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?",
    "Hayır, sadece Kurumsal Araç Kiralama operasyonları gerçekleştiriyoruz. Size başka nasıl yardımcı olabilirim?",
    "Evet, kurumsal araç kiralama hizmetleri sağlıyoruz. Size nasıl yardımcı olabilirim?"
]

Step 2: Prepend Tokens

Next, prepend the special tokens to your questions and answers:

questions = [Q + q for q in questions]
answers = [A + a for a in answers]

Step 3: Implement the Answer Retrieval Function

Define the function to retrieve answers based on the similarity scores:

def answer_faq(model, tokenizer, questions, answers, return_similarities=False):
    # Tokenize the input questions and answers
    tokens = tokenizer(questions + answers, padding=True, return_tensors='tf')
    embs = model(**tokens)[0]
    ...
    return sorted_results

Step 4: Querying the Model

Finally, you can loop through your list of questions to find answers:

for question in questions:
    results = answer_faq(model, tokenizer, [question], answers)
    print(question.replace(Q, ''))
    print(results)
    print('---------------------')

Sample Output

When you run the model with sample questions, you will receive outputs that provide both the answer and a score indicating its relevance:

Merhaba
[answer: Merhaba, size nasıl yardımcı olabilirim?, score: 0.2931, ...]
---------------------
Nasılsın?
[answer: İyiyim, teşekkür ederim. Size nasıl yardımcı olabilirim?, score: 0.2808, ...]
---------------------

Troubleshooting

If you encounter issues during implementation, consider the following solutions:

• Ensure that your environment is set up correctly with the required libraries such as TensorFlow and Hugging Face Transformers.
• Verify that the model and tokenizer are compatible; mismatched versions can lead to unexpected behavior.
• Check your training dataset to ensure it is cleaned and formatted correctly.
• If you need more help or insights, feel free to reach out for collaboration on AI development projects. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.