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38 lines
1.7 KiB
Markdown
38 lines
1.7 KiB
Markdown
---
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language: en
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license: apache-2.0
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datasets:
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- wiki_dpr
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thumbnail: https://huggingface.co/front/thumbnails/facebook.png
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---
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## RAG
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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)
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by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
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The model is a *uncased* model, which means that capital letters are simply converted to lower-case letters.
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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.
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The question_encoder and retriever are based on `facebook/dpr-question_encoder-single-nq-base` and `facebook/bart-large`, which were jointly finetuned on
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on the *wiki_dpr* QA dataset in an end-to-end fashion.
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## Usage:
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**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.
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The model can generate answers to any factoid question as follows:
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```python
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from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
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tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
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retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
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model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
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input_dict = tokenizer.prepare_seq2seq_batch("how many countries are in europe", return_tensors="pt")
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generated = model.generate(input_ids=input_dict["input_ids"])
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print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0])
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# should give 54 => google says either 44 or 51
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```
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