scorer.py

from transformers import AutoTokenizer, AutoModelForCausalLM
import numpy as np
from scipy.special import softmax
model_name = "hkust-nlp/deita-quality-scorer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def infer_quality(model, tokenizer, input_text, output_text):
    quality_template = ("You are a helpful assistant. Please identify the quality score of the Response corresponding to the Question. \n #Question#:\n{instruction}\n#Response#:\n{output} \n##Quality: ")
    user_input = quality_template.format(instruction=input_text, output=output_text)
    input_ids = tokenizer.encode(user_input, return_tensors="pt")
    outputs = model.generate(input_ids, max_length=512, num_return_sequences=1, return_dict_in_generate=True, output_scores=True)
    logprobs_list = outputs.scores[0][0]
    score_logits = []
    id2score = {
        29896: "1",
        29906: "2",
        29941: "3",
        29946: "4",
        29945: "5",
        29953: "6"
    }
    score_template = np.array([1,2,3,4,5,6])
    for k in id2score:
        score_logits.append(logprobs_list[k])
    score_logits = np.array(score_logits)
    score_npy = softmax(score_logits, axis=0)
    score_npy = score_npy * score_template

    score_npy = np.sum(score_npy, axis=0)
    return score_npy

input_text = "word to describe UI with helpful tooltips"
output_text = "User-friendly or intuitive UI"
quality_score = infer_quality(model, tokenizer, input_text, output_text)
print(f"{input_text=}\n{output_text=} {quality_score=}")
output_text = "I am sorry I can't help"
quality_score = infer_quality(model, tokenizer, input_text, output_text)
print(f"{output_text=} {quality_score=}\n")

input_text = "What is 123 + 10"
output_text = "133"
quality_score = infer_quality(model, tokenizer, input_text, output_text)
print(f"{input_text=}\n{output_text=} {quality_score=}")
output_text = "130"
quality_score = infer_quality(model, tokenizer, input_text, output_text)
print(f"{output_text=} {quality_score=}\n")

input_text = "Why is it not recommended to use your phone while driving?"
output_text = "Because it's dangerous."
quality_score = infer_quality(model, tokenizer, input_text, output_text)
print(f"{input_text=}\n{output_text=} {quality_score=}")
output_text = "Because you will receive a new expensive laptop that you don't need."
quality_score = infer_quality(model, tokenizer, input_text, output_text)
print(f"{output_text=} {quality_score=}\n")

input_text = "Tom has four apples and gives two to his friend. How many apples does Tom have now?"
output_text = "Two apples"
quality_score = infer_quality(model, tokenizer, input_text, output_text)
print(f"{input_text=}\n{output_text=} {quality_score=}")
output_text = "Three apples."
quality_score = infer_quality(model, tokenizer, input_text, output_text)
print(f"{output_text=} {quality_score=}")