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causal-strength 衡量因果关系的强度

causal-strength 是一个用于评估陈述之间因果强度的 Python 包，使用如 CESAR（带注意力重加权的因果嵌入相似性）等多种指标。该包利用 Hugging Face Transformers 上的预训练模型，提供高效和可扩展的计算。

目录

• 📜 引用 *
• 🌟 特性 *
• 🚀 安装 *
  • 前提条件
  • 步骤
• 🛠️ 使用 *
  • 快速开始
  • 评估因果强度
  • 生成因果热力图
• 参考文献

📜 引用

如果你觉得这个包有帮助，请给我们的仓库 causal-strength 和相关仓库 defeasibility-in-causality 点星。如果用于学术目的，请引用我们的论文：

@inproceedings{cui-etal-2024-exploring,
    title = "Exploring Defeasibility in Causal Reasoning",
    author = "Cui, Shaobo  and
      Milikic, Lazar  and
      Feng, Yiyang  and
      Ismayilzada, Mete  and
      Paul, Debjit  and
      Bosselut, Antoine  and
      Faltings, Boi",
    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.384",
    doi = "10.18653/v1/2024.findings-acl.384",
    pages = "6433--6452",
}

🛠️ 使用方法

评估因果强度

evaluate 函数用于计算两个陈述之间的因果强度。

1. 使用 CESAR 模型:

   from causalstrength import evaluate
   
   # Test CESAR Model
   s1_cesar = "Tom is very hungry now."
   s2_cesar = "He goes to McDonald for some food."
   
   print("Testing CESAR model:")
   cesar_score = evaluate(s1_cesar, s2_cesar, model_name='CESAR', model_path='shaobocui/cesar-bert-large')
   print(f"CESAR Causal strength between \"{s1_cesar}\" and \"{s2_cesar}\": {cesar_score:.4f}")

   输出如下:

   Testing CESAR model:
   CESAR Causal strength between "Tom is very hungry now." and "He goes to McDonald for some food.": 0.4482
   

2. 使用 CEQ 模型:

    from causalstrength import evaluate
   
    # Test CEQ Model
    s1_ceq = "Tom is very hungry now."
    s2_ceq = "He goes to McDonald for some food."
    
    print("\nTesting CEQ model:")
    ceq_score = evaluate(s1_ceq, s2_ceq, model_name='CEQ')
    print(f"CEQ Causal strength between \"{s1_ceq}\" and \"{s2_ceq}\": {ceq_score:.4f}")

   输出如下:

   Testing CEQ model:
   CEQ Causal strength between "Tom is very hungry now." and "He goes to McDonald for some food.": 0.0168
   

参数说明：

• s1 (str): 原因陈述。
• s2 (str): 结果陈述。
• model_name (str): 使用的模型名称（如 'CESAR', 'CEQ' 等）。
• model_path (str): Hugging Face 模型标识符或模型的本地路径。

生成因果热力图

使用热力图可视化 word-level 的 因果强度:

from causalstrength import evaluate

# Test CESAR Model
s1_cesar = "Fire starts quickly."
s2_cesar = "House burns to ashes."

print("Testing CESAR model:")
cesar_score = evaluate(s1_cesar, s2_cesar, model_name='CESAR', model_path='shaobocui/cesar-bert-large',
                       plot_heatmap_flag=True, heatmap_path=f'./figures/causal_heatmap.png')

输出如下:

Testing CESAR model:
The causal heatmap is saved to ./figures/causal_heatmap.png

The causal heatmap is as follows:

📚 参考文献

1. Cui, Shaobo, et al. "Exploring Defeasibility in Causal Reasoning." Findings of the Association for Computational Linguistics ACL 2024. 2024.
2. Du, Li, et al. "e-CARE: a New Dataset for Exploring Explainable Causal Reasoning." Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.