| import gradio as gr | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| import torch | |
| # Load model and tokenizer | |
| model_name = "cross-encoder/ms-marco-MiniLM-L-12-v2" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| model.eval() # Set model to evaluation mode | |
| # Function to get relevance score and relevant excerpt based on attention scores | |
| def get_relevance_score_and_excerpt(query, paragraph): | |
| if not query.strip() or not paragraph.strip(): | |
| return "Please provide both a query and a document paragraph.", "" | |
| # Tokenize the input | |
| inputs = t |