SemHash – Fast Semantic Text Deduplication for Cleaner Datasets
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…
What it does
In the maker’s words, at launch
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, reduce generalization, and cause train-test leakage—leading to unreliable results. Techniques like minhash handle exact or near-exact duplicates, but semantic deduplication also catches semantically redundant samples, which we believe is an important aspect of deduplication. Furthermore, it’s not trivial to see why something was removed with minhash, which we also believe is important. We already found some interesting results on some well known datasets in our benchmarks which are included in the repo. We are curious to hear your feedback! Do you currently deduplicate your datasets before training, and what techniques do you use?
Does the same job
all alternatives →- SSSemHash – Semantic Text Deduplication, Outlier Filtering and Sampling2025 · github.com · ▲7
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…
- ALA labelling tool to easily extract and label Wikipedia data2021 · ▲116
Hi HN! I am Maria, solo founder of DataQA (https://dataqa.ai/), a tool to search and label documents for various NLP tasks (e.g. entity extraction, entity linking, etc). I have worked as a data scientist and ML engineer for the better part of a decade, and over that time have specialised mainly in applications involving natural language processing (NLP). One of the key questions I have always had at the back of my mind is whether my time was well spent. Whenever I spent more time on feature engineering or trying different models, I always wondered whether I would get better…
- PAPydb – a lightweight database with Python syntax queries, using ZeroMQ2017 · github.com · ▲130
- OAOmnihash, a multi-hashing utility2016 · github.com · ▲34
- DCDatasets.co – Share and discover new machine learning datasets2016 · datasets.co · ▲81
- DFDiscohash – Fast Hash2020 · github.com · ▲75
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, January 2025
the whole month →- IM
Hello! I'm Byran. I spent the past ~6 months engineering a laptop from scratch. It's fully open-source on GH at: https://github.com/Hello9999901/laptop
Dev tools · 2025 · byran.ee
- TITetris in a PDF▲1,289
I realized that the PDF engines of modern desktop browsers (PDFium and PDF.js) support JavaScript with enough I/O primitives to make a basic game like Tetris. It was a bit tricky to find a union of features that work in both engines, but in the end it turns out that showing/hiding annotation "fields" works well to make monochrome pixels, and keyboard input can be achieved by typing in a text input box. All in all it's quite janky but a nice reminder of how general purpose PDF scripting can be. The linked PDF is all ASCII so you can just open it in a text editor, or have a look at…
Life & fun · 2025 · th0mas.nl
Create lifelike, personalized AI avatars from text prompts
AI · 2025 · jogg.ai
