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README.md

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+# megashtein
+
+In their paper ["Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage"](https://arxiv.org/abs/2207.04684), Guo et al. explore techniques for using a neural network to embed sequences in such a way that the squared Euclidean distance between embeddings approximates the Levenshtein distance between the original sequences.
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+This is valuable because there are excellent libraries for doing fast GPU accelerated searches for the K nearest neighbors of vectors, like [faiss](https://github.com/facebookresearch/faiss). Algorithms like [HNSW](https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world) allow us to do these searches in logarithmic time, where a brute force levenshtein distance based fuzzy search would need to run in exponential time.
+
+This repo contains a PyTorch implementation of the core ideas from Guo's paper, adapted for ASCII sequences rather than DNA sequences. The implementation includes:
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+- A convolutional neural network architecture for sequence embedding
+- Training using Poisson regression loss (PNLL) as described in the paper
+- Synthetic data generation with controlled edit distance relationships
+- Model saving and loading functionality
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+The trained model learns to embed ASCII strings such that the squared Euclidean distance between embeddings approximates the true Levenshtein distance between the strings.