model/Engessay_grading_ML.py

from typing import *
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "KevSun/Engessay_grading_ML"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)


@torch.no_grad()
def grade_Engessay_grading_ML(question: str, answer: str) -> Tuple[float, str]:
    text = f"{question} {answer}"

    inputs = tokenizer(text, return_tensors="pt")

    outputs = model(**inputs)
    predictions = outputs.logits.squeeze()

    predicted_scores = predictions.numpy()
    scaled_scores = 2.25 * predicted_scores - 1.25
    rounded_scores = [round(score * 2) / 2 for score in scaled_scores]

    labels = [
        "cohesion",
        "syntax",
        "vocabulary",
        "phraseology",
        "grammar",
        "conventions",
    ]
    overall_score = round(sum(rounded_scores) / len(rounded_scores) * 2) / 2

    comment = ""
    for label, score in zip(labels, rounded_scores):
        comment += f"{label}: {score}\n"

    return overall_score, comment