|
--- |
|
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} |
|
} |
|
``` |
