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from typing import *
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from .common import Grader
model_name = "KevSun/Engessay_grading_ML"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
class Engessay_grading_ML(Grader):
def info(self) -> str:
return "[KevSun/Engessay_grading_ML](https://huggingface.co/KevSun/Engessay_grading_ML)"
@torch.no_grad()
def grade(self, 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
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