AkariAsai / self-rag

This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.

Home Page:https://selfrag.github.io/

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请问有中文训练语料吗?

mawenju203 opened this issue · comments

Definitely not, self rag only provides English data. If you need Chinese training data, you need to go through the train data creation process

https://huggingface.co/datasets/selfrag/selfrag_train_data;

请问一下,这个数据的处理过程以及原始数据,有吗?

另外有个问题

image

伪代码中的实现是就是这种形式的吗?

from passage_retriever import Retriever
retriever = Retriever({})
retriever.setup_retriever_demo("facebook/contriever-msmarco", "enwiki_2020_intro_only/enwiki_2020_dec_intro_only.jsonl", "enwiki_2020_intro_only/enwiki_dec_2020_contriever_intro/*",  n_docs=5, save_or_load_index=False)
retrieved_documents = retriever.search_document_demo(query_3, 5)
prompts = [format_prompt(query_3, doc["title"] +"\n"+ doc["text"]) for doc in retrieved_documents]
preds = model.generate(prompts, sampling_params)
top_doc = retriever.search_document_demo(query_3, 1)[0]
print("Reference: {0}\nModel prediction: {1}".format(top_doc["title"] + "\n" + top_doc["text"], preds[0].outputs[0].text))

Definitely not, self rag only provides English data. If you need Chinese training data, you need to go through the train data creation process

谢谢,

Hi thank you so much for answering the question, @fate-ubw (I just answered your question, by the way!)

@mawenju203 Hi thanks for your interest. We don't have any Chinese training data. Would be exciting to see Self-RAG applications to other languages, though!

Regarding the second question (I used Google translate, and it said you asked if the demo code is the same as the pseudo-code), the code snippet is a simple interface to run Self-RAG, so it's not the same as the original inference logic. If you are interested, please take a look at the run_long_form_static.py script.

I have trained a Chinese version of Self-RAG based on Baichuan2-7B-Chat, which you can download from here. All the reflection tokens are the same as the English version. I hope you find this helpful :).

Thank you so much for the info! Great to hear people tested Self-RAG in other languages :) I'm closing this issue now but feel free to reopen it!

I have trained a Chinese version of Self-RAG based on Baichuan2-7B-Chat, which you can download from here. All the reflection tokens are the same as the English version. I hope you find this helpful :).

Thanks for your great work! Could you provide Chinese training data?

I have trained a Chinese version of Self-RAG based on Baichuan2-7B-Chat, which you can download from here. All the reflection tokens are the same as the English version. I hope you find this helpful :).

Thanks for your great work! Could you provide Chinese training data?

Yes! I just now uploaded a file containing 4w constructed data, which you can find and download from huggingface.

I have trained a Chinese version of Self-RAG based on Baichuan2-7B-Chat, which you can download from here. All the reflection tokens are the same as the English version. I hope you find this helpful :).

Thanks for your great work! Could you provide Chinese training data?

Yes! I just now uploaded a file containing 4w constructed data, which you can find and download from huggingface.

Thanks for your great work !
Could you provide the code you used to construct data and trian selfrag-zh_baichuan2_7b_chat ?

I have trained a Chinese version of Self-RAG based on Baichuan2-7B-Chat, which you can download from here. All the reflection tokens are the same as the English version. I hope you find this helpful :).

Thanks for your great work! Could you provide Chinese training data?

Yes! I just now uploaded a file containing 4w constructed data, which you can find and download from huggingface.

Thanks for your great work ! Could you provide the code you used to construct data and trian selfrag-zh_baichuan2_7b_chat ?

I just used the original data creation pipeline in this repo, by following which you can apply to your own sft datasets.