Update README.md

README.md

@@ -136,6 +136,8 @@ print(output)
 ```
 
 
+For more detailed inference, please refer to [DeepKE-llm/InstructKGC/6.1.2IE专用模型](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README_CN.md/#612ie%E4%B8%93%E7%94%A8%E6%A8%A1%E5%9E%8B).
+
 ### Advanced Use of OneKE
 
 
@@ -334,7 +336,7 @@ def get_instruction(language, task, schema, input):
 ```
 
 
-For more detailed data conversion, please refer to [InstructKGC/README_CN.md/2.3 Testing Data Conversion](./InstructKGC/README_CN.md/#23测试数据转换).
+
 
 Below is an example using the aforementioned simple script:
 
@@ -355,6 +357,9 @@ for split_schema in split_schemas:
 > '{"instruction": "You are an expert in named entity recognition. Please extract entities that match the schema definition from the input. Return an empty list if the entity type does not exist. Please respond in the format of a JSON string.", "schema": ["person", "organization", "else", "location"], "input": "284 Robert Allenby ( Australia ) 69 71 71 73 , Miguel Angel Martin ( Spain ) 75 70 71 68 ( Allenby won at first play-off hole )"}'
 
 
+For more detailed data conversion, please refer to [DeepKE-llm/InstructKGC/README_CN.md/2.3测试数据转换](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README_CN.md/#23%E6%B5%8B%E8%AF%95%E6%95%B0%E6%8D%AE%E8%BD%AC%E6%8D%A2)
+
+
 ### Customized Schema Description Instructions
 
 
@@ -616,11 +621,11 @@ Given that example instances can often be lengthy, and due to the limited maximu
 
 ## Evaluation
 
-To extract structured content from the output text and to assess it, please refer to [InstructKGC/README_CN.md/7. Evaluation](./InstructKGC/README_CN.md/#🧾-7评估).
+To extract structured content from the output text and to assess it, please refer to [DeepKE-llm/InstructKGC/README_CN.md/7.评估](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README_CN.md/#-7%E8%AF%84%E4%BC%B0).
 
 
 ## Continue Training
-To continue training OneKE, refer to [InstructKGC/4.9 Domain-specific Data Continual Training](./InstructKGC/README_CN.md/#49领域内数据继续训练).
+To continue training OneKE, refer to [DeepKE-llm/InstructKGC/4.9领域内数据继续训练](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README_CN.md/#49%E9%A2%86%E5%9F%9F%E5%86%85%E6%95%B0%E6%8D%AE%E7%BB%A7%E7%BB%AD%E8%AE%AD%E7%BB%83).
 
 
 ## Citation
@@ -652,5 +657,6 @@ If you have used OneKE in your work, please kindly cite the following paper:
 }
 ```
 
-
+## Acknowledgements
+We are very grateful for the inspiration provided by the MathPile and KnowledgePile projects. Special thanks are due to the builders and maintainers of the following datasets: AnatEM、BC2GM、BC4CHEMD、NCBI-Disease、BC5CDR、HarveyNER、CoNLL2003、GENIA、ACE2005、MIT Restaurant、MIT Movie、FabNER、MultiNERD、Ontonotes、FindVehicle、CrossNER、MSRA NER、Resume NER、CLUE NER、Weibo NER、Boson、ADE Corpus、GIDS、CoNLL2004、SciERC、Semeval-RE、NYT11-HRL、KBP37、NYT、Wiki-ZSL、FewRel、CMeIE、DuIE、COAE2016、IPRE、SKE2020、CASIE、PHEE、CrudeOilNews、RAMS、WikiEvents、DuEE、DuEE-Fin、FewFC、CCF law, and more. These datasets have significantly contributed to the advancement of this research. We are also grateful for the valuable contributions in the field of information extraction made by InstructUIE and YAYI-UIE, both in terms of data and model innovation. Our research results have benefitted from their creativity and hard work as well. Additionally, our heartfelt thanks go to hiyouga/LLaMA-Factory; our fine-tuning code implementation owes much to their work. The assistance provided by these academic resources has been instrumental in the completion of our research, and for this, we are deeply appreciative.