Update README.md

README.md

@@ -22,7 +22,26 @@ To fairly evaluate the legal judgment prediction capabilities of large models, w
 from datasets import load_dataset
 dataset = load_dataset("knockknock404/PCJD", "all", split="test")
 # Load "Original Set"(ori)
-dataset_math = load_dataset("knockknock404/PCJD", "ori", split="test")
+dataset_ori = load_dataset("knockknock404/PCJD", "ori", split="test")
 # Load "Adversarial Set"
-dataset_math = load_dataset("knockknock404/PCJD", "adv", split="test")
-```
+dataset_adv = load_dataset("knockknock404/PCJD", "adv", split="test")
+```
+
+## composition
+The PCJD consists of two components: the "Original Set"(ori) and the "Adversarial Set"(adv).
+- **ori**: Contains 803 test samples and 176 training samples covering 176 different criminal charges. By default, "Procuratorate" is selected as the text input.
+- **ori**: Contains 803 test samples and 176 training samples covering 176 different criminal charges. Based on ori, the samples with the highest cosine similarity to high-frequency charges are selected as adversarial examples.
+
+## format
+The input for baseline testing meets the following format:
+```python
+根据案情描述和已有步骤仅给出一个推理。如果是结论则直接输出<e></e>，例如<e>盗窃罪</e>。如果是步骤则直接输出<p></p>，例如<p>步骤1：…</p>\n案情描述:input\n已有推理步骤：\nsteps\n
+```
+In this regard, we have provided a simple processing method in the ‘\code’:
+```python
+from main import get_args
+args = get_args()
+warp = WarpLJP(args=args)
+for data in dataset:
+  x,_,y = warp.processing_single(data)
+```