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--- |
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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-Token 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 consists 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, RagTokenForGeneration |
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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 = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever) |
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input_dict = tokenizer.prepare_seq2seq_batch("who holds the record in 100m freestyle", 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 michael phelps => sounds reasonable |
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``` |
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