hkust-nlp

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deita-quality-scorer

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HKUST NLP Group 15

• infer_Quality(model, tokenizer, input_text, resp_text)
• demo code not how to get resp_text

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, resp_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=resp_text)
    input_ids = tokenizer.encode(user_input, return_tensors="pt")
    max_length = 512
    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" # Example Input
output_text = "User-friendly or intuitive UI" # Example Output
quality_score = infer_quality(model, tokenizer, input_text)

print(quality_score)