Upload constants.py

constants.py

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+# this is .py for store constants 
+
+DATA_DIR="./data/data.json"
+
+
+
+MODEL_INFO = ["Model Name", "Language Model"]
+AVG_INFO = ["Avg. All"]
+ME_INFO=["Method Name", "Language Model"]
+
+# KE 固定信息
+KE_Data_INFO = ["FewNERD", "FewRel", "InstructIE-en", "MAVEN","WikiEvents"]
+
+KE_TASK_INFO = ["Avg. All", "FewNERD", "FewRel", "InstructIE-en", "MAVEN","WikiEvents"]
+KE_CSV_DIR = "./ke_files/result-kgc.csv"
+DATA_COLUMN_NAMES =["locality","labels","concept","text"]
+KE_TABLE_INTRODUCTION = """In the table below, we summarize each task performance of all the models. We use F1 score(%) as the primary evaluation metric for each tasks. 
+    """
+RESULT_COLUMN_NAMES= ["DataSet","Metric","Metric","ICE","AdaLoRA","MEND","ROME","MEMIT","FT-L","FT"]
+DATA_STRUCT="""
+Datasets	ZsRE	Wikirecent	Wikicounterfact	WikiBio
+Train	10,000	570	1455	592
+Test	1230	1266	885	1392
+"""
+TITLE = """# KnowEdit: a dataset for knowledge editing"""
+
+BACKGROUND="""
+Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising from their extensive parameterization.There is an increasing interest in efficient, lightweight methods for onthe-fly model modifications. To this end, recent years have seen a burgeoning in the techniques of knowledge editing for LLMs, which aim to efficiently modify LLMs’ behaviors within specific domains while preserving overall performance across various inputs. 
+"""
+
+LEADERBORAD_INTRODUCTION = """
+     This is the dataset for knowledge editing. It contains six tasks: ZsRE, Wiki<sub>recent</sub>, Wiki<sub>counterfact</sub>, WikiBio, ConvSent and Sanitation. This repo shows the former 4 tasks and you can get the data for ConvSent and Sanitation from their original papers.
+    """
+DATA_SCHEMA =""" {
+  "subject": xxx,
+  "target_new": xxx,
+  "prompt": xxx,
+  "portability":{
+      "Logical_Generalization": [],
+      ...
+  }
+  "locality":{
+      "Relation_Specificity": [],
+      ...
+  }
+}"""
+
+
+
+CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
+CITATION_BUTTON_TEXT = r"""@article{tan2023evaluation,
+    title={Evaluation of ChatGPT as a question answering system for answering complex questions},
+    author={Yiming Tan and Dehai Min and Yu Li and Wenbo Li and Nan Hu and Yongrui Chen and Guilin Qi},
+    journal={arXiv preprint arXiv:2303.07992},
+    year={2023}
+}
+@article{gui2023InstructIE,
+  author       = {Honghao Gui and Jintian Zhang and Hongbin Ye and Ningyu Zhang},
+  title        = {InstructIE: {A} Chinese Instruction-based Information Extraction Dataset},
+  journal      = {arXiv preprint arXiv:2303.07992},
+  year         = {2023}
+}
+@article{yao2023edit,
+  author       = {Yunzhi Yao and Peng Wang and Bozhong Tian and Siyuan Cheng and Zhoubo Li and Shumin Deng and Huajun Chen and  Ningyu Zhang},
+  title        = {Editing Large Language Models: Problems, Methods, and Opportunities},
+  journal      = {arXiv preprint arXiv:2305.13172},
+  year         = {2023}
+}
+"""