Create README.md

README.md

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+---
+license: mit
+---
+
+<div align="center">
+<h1>Enabling Discriminative Reasoning in Large Language Models for Legal Judgment Prediction[<a href="">Paper</a>]</h1>
+</div>
+
+## Released Resources
+- 🤗The Huggingface model: Based on Qwen2-7B, we trained a model using the CAIL2018 dataset. [Qwen2-7B-CAIL2018-step-8765](https://huggingface.co/ChenlongDeng/ADAPT-Qwen2-7B-CAIL2018-step-8765)
+- The training trajectories: We release the 80,141 training trajectories of the CAIL2018 dataset in [this link](https://pan.baidu.com/s/1HkLTedi1r6WB0CBvH5dtrA?pwd=p9ex)
+
+## Supported Prompts
+❗️Note: Our released model needs the `Qwen chat_template` to conduct correct generation.
+
+We support the following four prompts to enable reasoning. You should use `the same input format and prompt` to achieve the best performance.
+
+### Prompt 1: ADAPT Reasoning
+```python
+case_input = f"案件描述：{description}\n被告人姓名：{defendant_name}"
+prompt = "请你采用ADAPT框架分析以上案件中该被告人可能被判处的罪名、适用法条和刑期"
+model_input_str = '\n'.join(case_input, prompt)
+```
+
+
+### Prompt 2: Ask
+```python
+case_input = f"案件描述：{description}\n被告人姓名：{defendant_name}"
+prompt = "请你用法律理论分析以上案件中该被告人在行为主体，起因、行为和结果，行为对象，犯罪主观四个方面的信息"
+model_input_str = '\n'.join(case_input, prompt)
+```
+
+### Prompt 3: Article
+```python
+case_input = f"案件描述：{description}\n被告人姓名：{defendant_name}"
+prompt = "请你依次列出以上案件中被告人适用的法条具体内容，以及适用该法条的原因"
+model_input_str = '\n'.join(case_input, prompt)
+```
+
+### Prompt 4: Sentencing factors
+```python
+case_input = f"案件描述：{description}\n被告人姓名：{defendant_name}\n罪名：{crimes}" # e.g., 污染环境罪
+prompt = "请你分析以上案件中的量刑区间和量刑因素，并给出最后的量刑预测结果"
+model_input_str = '\n'.join(case_input, prompt)
+```
+
+
+
+## Citation