---
title: rag-sequence-nq
canonical_url: "https://www.modelscope.cn/models/facebook/rag-sequence-nq"
md_url: "https://www.modelscope.cn/models/facebook/rag-sequence-nq.md"
repository: facebook/rag-sequence-nq
last_updated: 2025-05-20
license: apache-2.0
model_type:
  - rag
architectures:
  - RagSequenceForGeneration
library_name:
  - transformer
  - tensorflow
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 334
stars: 0
---

# rag-sequence-nq

> rag-sequence-nq - facebook 在 ModelScope 开源的模型。This is the RAG-Sequence Model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.

facebook/rag-sequence-nq 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。

- **Repository**: facebook/rag-sequence-nq
- **License**: apache-2.0
- **Downloads**: 334
- **Stars**: 0
- **Last updated**: 2025-05-20

Source: https://www.modelscope.cn/models/facebook/rag-sequence-nq

---

## RAG

This is the RAG-Sequence Model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf) 
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.

The model is a *uncased* model, which means that capital letters are simply converted to lower-case letters.

The model consits of a *question_encoder*, *retriever* and a *generator*. The retriever extracts relevant passages from the *wiki_dpr* `train` datasets, which is linked above.
The question_encoder and retriever are based on `facebook/dpr-question_encoder-single-nq-base` and `facebook/bart-large`, which were jointly finetuned on 
on the *wiki_dpr* QA dataset in an end-to-end fashion.

## Usage:

**Note**: In the usage example below only the *dummy* retriever of *wiki_dpr* is used because the complete *lecagy* index requires over 75 GB of RAM.
The model can generate answers to any factoid question as follows:

```python
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration 
 
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq") 
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True) 
model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever) 
 
input_dict = tokenizer.prepare_seq2seq_batch("how many countries are in europe", return_tensors="pt") 

generated = model.generate(input_ids=input_dict["input_ids"]) 
print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0]) 

# should give 54 => google says either 44 or 51
```