How to Use RDR: A Guide to Enhanced Retrieval

September 5, 2024

Welcome to our friendly guide on the Retriever Distilled Reader (RDR), a powerful model that enhances the answer recall rates for various tasks! In this article, we’ll dive into how to effectively use RDR and what makes it stand out from prior models.

Understanding RDR: What’s All the Hype?

The RDR model, as proposed in the paper by Sohee Yang and Minjoon Seo, tantalizingly integrates the competent qualities of a reader into the retriever model through knowledge distillation. Think of it like a university student who not only read well but also absorbed the essentials from better-performing peers to boost their overall understanding. This process significantly improves the recall rate of answers, especially when querying with a small number of top selections.

Performance Overview

Here’s how RDR performs against the prior model, DPR, in terms of answer recall rates:

TriviaQA Dev
- DPR: 54.27 (1), 71.11 (5), 79.53 (20), 82.72 (50), 85.07 (100)
- RDR: 61.84 (1), 75.93 (5), 82.56 (20), 85.35 (50), 87.00 (100)
TriviaQA Test
- DPR: 54.41 (1), 70.99 (5), 79.31 (20), 82.90 (50), 84.99 (100)
- RDR: 62.56 (1), 75.92 (5), 82.52 (20), 85.64 (50), 87.26 (100)

The numbers speak for themselves! RDR consistently outshines its predecessor, indicating its increased efficacy.

How to Use RDR

Using RDR is seamless, thanks to its architecture sharing similarities with DPR. Here’s how you can get started:

First, ensure you have the required libraries installed, including PyTorch and transformers.
You will use DPRContextEncoder as your model class. AutoModel isn’t reliable for detecting the checkpoint types.

Follow this code snippet to utilize RDR:

python
from transformers import DPRContextEncoder, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('soheeyang/rdr-ctx_encoder-single-trivia-base')
ctx_encoder = DPRContextEncoder.from_pretrained('soheeyang/rdr-ctx_encoder-single-trivia-base')

data = tokenizer('context comes here', return_tensors='pt')
ctx_embedding = ctx_encoder(**data).pooler_output  # embedding vector for context

Troubleshooting Tips

If you encounter issues while implementing RDR, here are some helpful troubleshooting steps:

Ensure that you specify the exact model class as AutoModel can lead to misconfigurations.
Double-check your context string; it should be valid and fit the input requirements.
Verify that you are using compatible versions of PyTorch and transformers, as discrepancies can lead to unexpected behavior.

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Why RDR Matters

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Conclusion

RDR stands as a promising enhancement in the field of retrieval models, embodying the strengths of both readers and retrievers. By following the guide above, you’re now equipped to make the most of this powerful tool!