Seeking Advice: Developing an Open-Source AI Model for Semantic Analysis and Grading of Textual Responses

Hello everyone,

My goal is to develop an open-source, AI-powered application using a model from Hugging Face that can semantically analyze textual responses to specific questions.

Here’s a breakdown of the application’s requirements:

Input and Training: The application should allow the input of a question along with multiple possible answers. For each answer, I need to assign a grade and provide feedback explaining why it did or did not achieve the highest score.

Semantic Analysis Capability: The AI model should be capable of understanding and analyzing the text at a semantic level, going beyond just keyword matching.

User Interaction: After training, the model should be able to evaluate responses given by users to the same questions, assigning scores and providing feedback based on its training.

Open-Source and Free: Preferably, I’m looking for an open-source and free solution on Hugging Face that can be adapted for this purpose.

I’ve considered models like BERT, GPT, or RoBERTa for their advanced text understanding capabilities. However, I’m unsure how to proceed with training these models specifically for grading and providing feedback.


Which model would be most suitable for this kind of task?
What would be the best approach to train the model with the provided answers, scores, and feedback so it can accurately evaluate and score new user responses?
Are there any existing projects or resources that I could refer to or learn from?
Any advice, suggestions, or pointers to relevant resources would be immensely appreciated. I’m particularly interested in hearing about anyone’s experiences with similar projects or any challenges you might foresee with this kind of application.

Thanks in advance for your help!