README.md

metadata

license: apache-2.0

Usage Code

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import numpy as np
from scipy.special import softmax
# 选择模型和模型名称（例如，这里使用GPT-2模型）
model_name = "hkust-nlp/Deita-Complexity-Scorer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)


complexity_template = ("You are a helpful assistant. Please identify the complexity score of the following user query. \n##Query: {instruction}  \n##Complexity: ")
# 输入文本
input_text = "write a performance review for a junior data scientist"


user_input = complexity_template.format(instruction=input_text)

# 将输入文本编码为tokens
input_ids = tokenizer.encode(user_input, return_tensors="pt")

# 生成文本
max_length = 512  # 设置生成文本的最大长度
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)