Update README.md

README.md

@@ -1,3 +1,48 @@
 ---
 license: apache-2.0
 ---
+
+## Usage Code
+```python
+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-Quality-Scorer"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+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: ")
+# 输入文本
+input_text = "word to describe UI with helpful tooltips"
+output_text = "User-friendly or intuitive UI"
+
+user_input = quality_template.format(instruction=input_text, output=output_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)
+```