add test script for fine-tuned model

README.md

@@ -86,6 +86,31 @@ The file of our checkpoint generated after we fine-tuned it is upload to [Google
 You can see the prompt we tried from the file  `prompt templates`. 
 Up to now, we are using the file `prompt templates/license_master_v4.json `.
 
+# Testing Fine-tuned Modelsc
+To test the fine-tuned model with a test dataset of question-answer pairs, where the question is a segment of license content and the answer is a human-annotated risk rating (high, medium, low risk), please follow these steps:
+
+
+1. install necessary libraries:
+```
+pip install transformers torch scikit-learn
+```
+2. prepate the test data as follows:
+```
+[
+  {
+    "instruction": ".......",
+    "input": ".....",
+    "output": "......"
+  },
+  ...
+]
+```
+3. run the test script
+``` 
+python test.py
+```
+This script will load the fine-tuned model and test dataset, generate answers for each question, calculate the semantic similarity between the predicted and standard answers using BERT, and finally compute the accuracy based on a predefined similarity threshold which can be modified by users.
+
 # Computing resources
 NVIDIA GeForce RTX 3090
 
@@ -101,6 +126,8 @@ Despite the smaller scale of LicenseGPT, its tailored fine-tuning process allowe
 
 The traditional way means software IP lawyers should manually invest a substantial amount of time consulting resources and legal literature, resulting in context-specific outcomes that can take anywhere from 10 minutes to an hour, with an average of 30 minutes per case. In stark contrast, the proposed LicenseGPT's inference time spans from a swift 1 millisecond to 70 seconds at the upper end, with an average of just 10 seconds. With the help of LicenseGPT, a substantial reduction translates to an average review time for software IP lawyers of 10 minutes, representing a notable efficiency gain and freeing up valuable time for legal professionals.
 
+
+
 # Case Study
 Details in CaseStudy.md
 

test.py

@@ -0,0 +1,56 @@
+import json
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, BertModel
+from sklearn.metrics.pairwise import cosine_similarity
+import torch
+
+# 加载微调后的模型
+model_path = 'path/to/your/fine-tuned-model'
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+# 加载BERT模型用于语义相似度比较
+bert_model_name = 'bert-base-uncased'
+bert_model = BertModel.from_pretrained(bert_model_name)
+bert_tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
+
+# 加载测试数据集
+test_data_path = 'path/to/your/test-data.json'
+
+with open(test_data_path, 'r', encoding='utf-8') as f:
+    test_data = json.load(f)
+
+# 生成预测结果
+predicted_answers = []
+
+for item in test_data:
+    question = item['input']
+    inputs = tokenizer(question, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    outputs = model(**inputs)
+    predicted_answer = tokenizer.decode(torch.argmax(outputs.logits, dim=-1), skip_special_tokens=True)
+    predicted_answers.append(predicted_answer)
+
+# 计算语义相似度
+def get_bert_embedding(text, tokenizer, model):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return outputs.last_hidden_state.mean(dim=1)
+
+similarities = []
+
+for idx, item in enumerate(test_data):
+    standard_answer = item['output']
+    predicted_answer = predicted_answers[idx]
+
+    standard_embedding = get_bert_embedding(standard_answer, bert_tokenizer, bert_model)
+    predicted_embedding = get_bert_embedding(predicted_answer, bert_tokenizer, bert_model)
+
+    similarity = cosine_similarity(standard_embedding.numpy(), predicted_embedding.numpy())[0][0]
+    similarities.append(similarity)
+
+# 计算准确率
+threshold = 0.8
+correct_predictions = [1 if sim >= threshold else 0 for sim in similarities]
+accuracy = sum(correct_predictions) / len(correct_predictions)
+
+print(f"Accuracy: {accuracy * 100:.2f}%")