python 模型的rad-score如何计算，并画图

Rad-Score是一种用于评估文本生成模型的质量的指标，它考虑了生成文本的多样性和相关性。在Python中，可以使用以下步骤计算Rad-Score，并画出相应的图表：

1. 安装必要的库：pip install transformers, nltk, numpy, matplotlib

2. 导入库和模型：

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from nltk.tokenize import sent_tokenize
import numpy as np
import matplotlib.pyplot as plt

tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2', output_hidden_states=True)

3. 定义计算Rad-Score的函数：

def rad_score(text, num_samples=3):
    input_ids = tokenizer.encode(text, return_tensors='pt')
    with torch.no_grad():
        outputs = model.generate(
            input_ids=input_ids,
            max_length=50,
            do_sample=True,
            num_return_sequences=num_samples
        )

    generated_texts = []
    for i in range(num_samples):
        generated_text = tokenizer.decode(outputs[i], skip_special_tokens=True)
        generated_texts.append(generated_text)

    reference_texts = sent_tokenize(text)

    scores = []
    for generated_text in generated_texts:
        generated_sentences = sent_tokenize(generated_text)
        score = 0
        for reference_sentence in reference_texts:
            reference_words = reference_sentence.split()
            for generated_sentence in generated_sentences:
                generated_words = generated_sentence.split()
                common_words = set(reference_words).intersection(generated_words)
                score += len(common_words) / len(generated_words)
        score /= len(reference_texts)
        scores.append(score)

    return np.mean(scores)

该函数输入文本和生成的样本数，输出Rad-Score。

4. 绘制Rad-Score随样本数变化的图表：

sample_sizes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
rad_scores = []
for sample_size in sample_sizes:
    rad_score_avg = 0
    for i in range(10):
        rad_score_avg += rad_score(text, sample_size)
    rad_score_avg /= 10
    rad_scores.append(rad_score_avg)

plt.plot(sample_sizes, rad_scores)
plt.xlabel('Number of Samples')
plt.ylabel('Rad-Score')
plt.show()

该代码会生成一个随着样本数变化的Rad-Score图表。