Update README.md

README.md

@@ -2,34 +2,31 @@
 license: apache-2.0
 ---
 
-## Usage Code
+# Deita-Quality-Scorer
+
+Deita-Quality-Scorer is a tool for automatically annotating the Instruction Quality of SFT data.
+
+
+## Uses
+
 ```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 = {
+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",
@@ -37,12 +34,22 @@ id2score = {
         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)
-```
+	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"
+output_text = "User-friendly or intuitive UI"
+quality_score = infer_quality(model, tokenizer, input_text)
+
+print(quality_score)
+
+
+```
+