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--- |
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license: apache-2.0 |
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--- |
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## Usage Code |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import numpy as np |
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from scipy.special import softmax |
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# 选择模型和模型名称（例如，这里使用GPT-2模型） |
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model_name = "hkust-nlp/Deita-Complexity-Scorer" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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complexity_template = ("You are a helpful assistant. Please identify the complexity score of the following user query. \n##Query: {instruction} \n##Complexity: ") |
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# 输入文本 |
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input_text = "write a performance review for a junior data scientist" |
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user_input = complexity_template.format(instruction=input_text) |
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# 将输入文本编码为tokens |
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input_ids = tokenizer.encode(user_input, return_tensors="pt") |
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# 生成文本 |
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max_length = 512 # 设置生成文本的最大长度 |
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outputs = model.generate(input_ids, max_length=512, num_return_sequences=1, return_dict_in_generate=True, output_scores=True) |
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logprobs_list = outputs.scores[0][0] |
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score_logits = [] |
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id2score = { |
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29896: "1", |
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29906: "2", |
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29941: "3", |
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29946: "4", |
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29945: "5", |
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29953: "6" |
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} |
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score_template = np.array([1,2,3,4,5,6]) |
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for k in id2score: |
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score_logits.append(logprobs_list[k]) |
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score_logits = np.array(score_logits) |
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score_npy = softmax(score_logits, axis=0) |
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score_npy = score_npy * score_template |
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score_npy = np.sum(score_npy, axis=0) |
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``` |
