初始化项目，由ModelHub XC社区提供模型

Model: ECNU-SEA/SEA-E
Source: Original Platform

README.md

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+---
+license: apache-2.0
+tags:
+- Automated Peer Reviewing
+- SFT
+---
+
+## Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis
+
+Paper Link: https://arxiv.org/abs/2407.12857
+
+Project Page: https://ecnu-sea.github.io/
+
+
+## 🔥 News
+- 🔥🔥🔥 SEA is accepted by EMNLP 2024 !
+- 🔥🔥🔥 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()
+
+model_name = "/root/sea/"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+chat_model = AutoModelForCausalLM.from_pretrained(model_name)
+chat_model.to("cuda:0")
+
+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:
+
+- Commercial use is not allowed.
+- 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:
+
+```bibtex
+@inproceedings{yu2024automated,
+  title={Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis},
+  author={Yu, Jianxiang and Ding, Zichen and Tan, Jiaqi and Luo, Kangyang and Weng, Zhenmin and Gong, Chenghua and Zeng, Long and Cui, RenJing and Han, Chengcheng and Sun, Qiushi and others},
+  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2024},
+  pages={10164--10184},
+  year={2024}
+}
+```