Life & fun · February 15, 2016
Sense2vec model trained on all 2015 Reddit comments
Sense2vec model trained on all 2015 Reddit comments launched on February 15, 2016 with 225 votes, #103 of 4,386 launches that month and more than 95% of that year's launches.
In plain words
Sense2vec is a machine learning model trained on Reddit comments from 2015, designed to understand relationships between words and their meanings in context. It uses word embeddings to help identify semantic similarities and patterns in language. The model is useful for developers and researchers working on natural language processing tasks who need to analyze or extract meaning from text data.
written from the facts on this page · September 2026
Does a similar job
all alternatives →- WAWikipedia2Vec – A tool for learning embeddings of words and entities2018 · wikipedia2vec.github.io · ▲105
Sense2vc – Semantic Analysis of the Reddit Hivemind2017 · demos.explosion.ai · ▲7We parsed every comment posted Reddit in 2015 and 2019, and trained different word2vec models for each year.
Model2vec – Lightning-fast Static Embeddings for RAG/Semantic Search2024 · github.com · ▲28We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…

Model2vec-Rs – Fast Static Text Embeddings in Rust2025 · github.com · ▲60Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance:…

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