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26 lines
1.1 KiB
Markdown
26 lines
1.1 KiB
Markdown
## 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 Aleksandra Piktus et al.
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## Usage:
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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 people live in Paris?", "In Paris, there are 10 million people.", return_tensors="pt")
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outputs = model(input_ids=input_dict["input_ids"], labels=input_dict["labels"])
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# outputs.loss should give 76.2978
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generated = model.generate(input_ids=input_dict["input_ids"], num_beams=4)
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generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)
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# generated_string should give 270,000,000 -> not quite correct the answer, but it also only uses a dummy index
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```
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