semhash

Have you compared this to https://github.com/MinishLab/semhash (or considered using that for the deduplication backend)?

True, I think that's also a great usecase! Though these algorithms likely won't scale to very large datasets (e.g. millions of samples), but for smaller datasets, like fine-tuning sets, I think this would work very well. I've worked on something similar in the past that works for larger datasets (semantic deduplication: https://github.com/MinishLab/semhash).

We’ve just released SemHash v0.3.0, a major rework of our open-source text pre-processing library. We’ve added two new functionalities: outlier filtering & representative sampling. The core API has been reworked to make sure all of these features can be used together in an intuitive way. Our new features use the existing approximate nearest neighbors index that we already used for semantic deduplication, so they can be ran very quickly after building the index on your dataset. The core package can now be used for: - Semantic Deduplication: Remove semantic duplicates from your dataset. This can prevent train/test set overlap in classification tasks, or prevent duplicate samples in RAG/semantic search. - Outlier Filtering: Surface and filter the most anomalous samples from your dataset. This can help with automated removal of low quality d

We’ve just open-sourced SemHash, a lightweight package for semantic text deduplication. It lets you effortlessly clean up your datasets and avoid pitfalls caused by duplicate samples in semantic search, RAG, and machine learning. Main Features: - Fast and hardware friendly: Deduplicate datasets with millions of records in minutes, on a CPU. - Flexible: Works on single or multiple datasets (e.g., train/test deduplication), and multi-column data (e.g., Question-Answering datasets). - Lightweight: Minimal dependencies (largest is NumPy). - Explainable: Easily inspect duplicates and what caused them, and view the lowest similarity duplicates to adjust the threshold based on your dataset. We found that text deduplication is more complex than it appears, so we built SemHash to simplify the process. Duplicate samples can skew model training, red

We’ve just open-sourced SemHash, a lightweight package for semantic text deduplication. It lets you effortlessly clean up your datasets and avoid pitfalls caused by duplicate samples in semantic search, RAG, and machine learning. Main Features: - Fast and hardware friendly: Deduplicate datasets with millions of records in minutes, on a CPU. - Flexible: Works on single or multiple datasets (e.g., train/test deduplication), and multi-column data (e.g., Question-Answering datasets). - Lightweight: Minimal dependencies (largest is NumPy). - Explainable: Easily inspect duplicates and what caused them, and view the lowest similarity duplicates to adjust the threshold based on your dataset. We found that text deduplication is more complex than it appears, so we built SemHash to simplify the process. Duplicate samples can skew model training, red