Selecting a Chatbot Model for Question Answering (QA)
Hi All!I find myself in search of a suitable model for addressing frequently asked questions in a generative manner. In order to make an informed choice, I am reaching out for recommendations on the appropriate model types to consider for this purpose.To provide a bit more context, I am particularly interested in models that can effectively respond to questions with detailed, coherent, and contextually relevant answers. (I have a json file similar to QA dataset consists of the questions and corresponding answers). These questions may pertain to a broad range of topics, and I want a chatbot that can adapt to various domains seamlessly.While I am aware that Hugging Face offers a plethora of pre-trained models, each designed with its unique strengths and capabilities, I would greatly appreciate insights from the community to help narrow down my options. Any advice on specific models, fine-tuning strategies, or relevant resources would be highly valued.To summarize, my primary requirements are:Generative question-answering capability.Adaptability across diverse domains.Detailed, coherent, and contextually relevant responses.Please feel free to share your experiences and expertise in this domain, and thank you in advance for your valuable input. Your recommendations will play a pivotal role in my decision-making process as I strive to select the most suitable chatbot model for my project.
2023-09-14T02:23:18Z
I have the same need! Looking forward to the solutions.
2023-09-14T12:08:25Z
I am facing the same issue.
2024-04-30T04:50:03Z
Please check out my git page for…I had the same problem statement at institutional level. I have uploaded some demo code.Feel free to check out!
2024-10-17T12:22:45Z
Hi there!For addressing frequently asked questions with detailed, coherent, and contextually relevant responses, you’re on the right track by exploring generative models. Based on your requirements, I recommend considering GPT-based models, such as GPT-3.5 or GPT-4 from OpenAI. These models are known for their ability to generate natural, context-aware answers and perform well across diverse domains.Since you have a JSON file with a QA dataset, you could further fine-tune these models to align perfectly with your needs. Fine-tuning helps the chatbot become more accurate in understanding your specific topics and delivering precise answers. Hugging Face also offers several pre-trained models that can be fine-tuned using your data—if you are working with multiple topics, BERT-based or T5 models can be another good option for adaptability.If you are looking for professional support, I suggest exploringchatbot development services. Partnering with experts ensures the proper selection, fine-tuning, and integration of chatbot models tailored to your business needs. These services help in building robust chatbots that seamlessly integrate into your platform, providing exceptional responses to your customers’ queries.