Update README.md

README.md

@@ -3,6 +3,8 @@ license: apache-2.0
 tags:
 - Automated Peer Reviewing
 - SFT
+datasets:
+- ECNU-SEA/SEA_data
 ---
 
 ## Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis
@@ -17,46 +19,7 @@ Project Page: https://ecnu-sea.github.io/
 - 🔥🔥🔥 We have made SEA series models (7B) public !
 
 ## Model Description
-The SEA-E model utilizes [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as its backbone. It is derived by performing supervised fine-tuning (SFT) on a high-quality peer review instruction dataset, standardized through the SEA-S model. This model can provide comprehensive and insightful review feedback for submitted papers.
-  
-## Review Paper With SEA-E
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-instruction = system_prompt_dict['instruction_e']
-paper = read_txt_file(mmd_file_path)
-idx = paper.find("## References")
-paper = paper[:idx].strip()
-
-messages = [
-    {"role": "system", "content": instruction},
-    {"role": "user", "content": paper},
-]
-
-encodes = tokenizer.apply_chat_template(messages, return_tensors="pt")
-encodes = encodes.to("cuda:0")
-len_input = encodes.shape[1]
-generated_ids = chat_model.generate(encodes,max_new_tokens=8192,do_sample=True)
-# response = chat_model.chat(messages)[0].response_text
-response = tokenizer.batch_decode(generated_ids[: , len_input:])[0]
-
-```
-The code provided above is an example. For detailed usage instructions, please refer to https://github.com/ecnu-sea/sea.
-
-## Additional Clauses
-
-The additional clauses for this project are as follows:
-
-- The SEA-E model is intended solely to provide informative reviews for authors to polish their papers instead of directly recommending acceptance/rejection on papers.
-- Currently, the SEA-E model is only applicable within the field of machine learning and does not guarantee insightful comments for other disciplines.
-
-
-## Citation
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-If you find our paper or models helpful, please consider cite as follows:
+The SEA-A model aims to integrate all reviews for each paper into one to eliminate redundancy and errors, focusing on the major advantages and disadvantages of the paper. Specifically, we first utilize GPT-4 to integrate multiple reviews of a paper into one (From [ECNU-SEA/SEA_data](https://huggingface.co/datasets/ECNU-SEA/SEA_data)) that is in a unified format and criterion with constructive contents, and form an instruction dataset for SFT. After that, we fine-tune [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) to distill the knowledge of GPT-4. Therefore, SEA-S provides a novel paradigm for integrating peer review data in an unified format across various conferences.
 
 ```bibtex
 @misc{yu2024sea,