Improve ConceptEdit dataset card: Add metadata, links, and sample usage

README.md

@@ -1,8 +1,13 @@
 ---
 license: cc-by-nc-sa-4.0
+task_categories:
+- text-generation
+tags:
+- knowledge-editing
+- llm
+- conceptual-knowledge
 ---
 
-
 <div align="center">
 
 **Editing Conceptual Knowledge for Large Language Models**
@@ -10,14 +15,15 @@ license: cc-by-nc-sa-4.0
 ---
 
 <p align="center">
-  <a href="#-conceptual-knowledge-editing">Overview</a> •
-  <a href="#-usage">How To Use</a> •
-    <a href="#-citation">Citation</a> •
-    <a href="https://arxiv.org/abs/2403.06259">Paper</a> •
-    <a href="https://zjunlp.github.io/project/ConceptEdit">Website</a> 
+  <a href="https://huggingface.co/papers/2403.06259">Dataset Paper (ConceptEdit)</a> •
+  <a href="https://zjunlp.github.io/project/ConceptEdit">Dataset Website</a> •
+  <a href="https://github.com/zjunlp/EasyEdit">EasyEdit GitHub</a> •
+  <a href="https://huggingface.co/papers/2504.15133">Framework Paper (EasyEdit2)</a> •
+  <a href="https://zjunlp.github.io/project/EasyEdit2/">Framework Website (EasyEdit2)</a> 
 </p>
 </div>
 
+This repository contains the `ConceptEdit` dataset, which facilitates the evaluation of Large Language Models (LLMs) in open-domain natural language-driven molecule generation tasks. It is part of the `EasyEdit` framework, designed for plug-and-play adjustability in controlling LLM behaviors.
 
 ## 💡 Conceptual Knowledge Editing
 
@@ -30,37 +36,37 @@ license: cc-by-nc-sa-4.0
 **Concept** is a generalization of the world in the process of cognition, which represents the shared features and essential characteristics of a class of entities.
 Therefore, the endeavor of concept editing aims to modify the definition of concepts, thereby altering the behavior of LLMs when processing these concepts.
 
-
 ### Evaluation
 
-To analyze conceptual knowledge modification, we adopt the  metrics for factual editing (the target is the concept $C$ rather than factual instance $t$).
+To analyze conceptual knowledge modification, we adopt the metrics for factual editing (the target is the concept $C$ rather than factual instance $t$).
 
 - `Reliability`: the success rate of editing with a given editing description
 - `Generalization`: the success rate of editing **within** the editing scope
 - `Locality`: whether the model's output changes after editing for unrelated inputs
 
-
 Concept Specific Evaluation Metrics
 
 - `Instance Change`: capturing the intricacies of these instance-level changes
 - `Concept Consistency`: the semantic similarity of generated concept definition
 
+## 🚀 Sample Usage
 
-## 🌟 Usage
+You can load the ConceptEdit dataset using the `EasyEdit` framework as follows:
 
-### 🎍 Current Implementation
-As the main Table of our paper, four editing methods are supported for conceptual knowledge editing.
-| **Method** |  GPT-2 | GPT-J | LlaMA2-13B-Chat | Mistral-7B-v0.1
-| :--------------: | :--------------: | :--------------: | :--------------: | :--------------: | 
-| FT | ✅ | ✅ | ✅ | ✅ | 
-| ROME | ✅ | ✅ |✅ | ✅ | 
-| MEMIT | ✅ | ✅ | ✅| ✅ | 
-| PROMPT | ✅ | ✅ | ✅ | ✅ |
+```python
+from easyeditor import ConceptEditDataset
 
+# For example, to load the ConceptEdit dataset
+# Assuming 'data/concept_data.json' is the path to your dataset file
+concept_config = ConceptEditDataset.get_hparams()
+concept_train_ds = ConceptEditDataset('./data/concept_data.json', config=concept_config)
+concept_eval_ds = ConceptEditDataset('./data/concept_data.json', config=concept_config)
 
-### 💻 Run
- You can follow [EasyEdit](https://github.com/zjunlp/EasyEdit/edit/main/examples/ConceptEdit.md) to run the experiments.
-
+print(f"Loaded {len(concept_train_ds)} training samples.")
+print(f"Loaded {len(concept_eval_ds)} evaluation samples.")
+# You can inspect a sample:
+# print(concept_train_ds[0])
+```
 
 ## 📖 Citation
 
@@ -81,4 +87,4 @@ Please cite our paper if you use **ConceptEdit** in your work.
 
 We would like to express our sincere gratitude to [DBpedia](https://www.dbpedia.org/resources/ontology/)，[Wikidata](https://www.wikidata.org/wiki/Wikidata:Introduction)，[OntoProbe-PLMs](https://github.com/vickywu1022/OntoProbe-PLMs) and [ROME](https://github.com/kmeng01/rome).
 
-Their contributions are invaluable to the advancement of our work.
+Their contributions are invaluable to the advancement of our work.