Life & fun · alternatives · 2026
24 alternatives to Sense2vec model trained on all 2015 Reddit comments
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… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier. Sense2vec model trained on all 2015 Reddit comments launched in 2016; newer entries below may have overtaken it.
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2018 · wikipedia2vec.github.io · its alternatives →
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We parsed every comment posted Reddit in 2015 and 2019, and trained different word2vec models for each year.
2017 · demos.explosion.ai · its alternatives →
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We’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…
2024 · github.com · its alternatives →
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Google EmbeddingGemma 2free▲318An open, lightweight multimodal embedding model
16d ago · blog.google · its alternatives →
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Hey 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:…
2025 · github.com · its alternatives →
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Here's a small demonstration of the fundamental aspects of the word-to-vec algorithm. It's implemented in a single python script and depends only on a single text file for training. It's not meant to be blazingly fast or anything, just a toy example to aid my understanding of how word vectors might be learnt from a corpus.
2023 · github.com · its alternatives →
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2016 · ioloop.io · its alternatives →
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Commenter.ai▲402Your ecosystem for effective LinkedIn engagements
2023 · commenter.ai · its alternatives →
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Much improved new version. Search for words similar to the query. For example, "death" will find "death", "dying", "dead", "killing"... Incredibly useful for exploring large text datasets where exact matches are too restrictive.
2024 · github.com · its alternatives →
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I've been playing with embeddings and wanted to try out what results the embedding layer will produce based on just word-by-word input and addition / subtraction, beyond what many videos / papers mention (like the obvious king-man+woman=queen). So I built something that doesn't just give the first answer, but ranks the matches based on distance / cosine symmetry. I polished it a bit so that others can try it out, too. For now, I only have nouns (and some proper nouns) in the dataset, and pick the most common interpretation among the homographs. Also, it's case sensitive.
2025 · calc.datova.ai · its alternatives →
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2022 · ezpersonality.com · its alternatives →
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Learn how to build semantic search systems. From machine transition to question-answering.
2021 · pinecone.io · its alternatives →
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Second Brain▲281Hi HN, I'm Dom, a solo founder. I built Second Brain to solve a common issue I had with AI chats: the knowledge disappears. There’s no structure, no memory, and no way to reuse or build on them. So I built a tool that combines AI chat with a visual, interconnected knowledge system. Key features: - Save AI responses into editable docs (Notion-style Pages). - AI Visual board with multiple content & chat nodes (like Miro meets ChatGPT). - Add and chat with YouTubes, TikToks, Audio, PDFs, GitHub repos, Looms, websites, etc. - Organize using Projects and Smart Tags — then chat with full contexts.…
2025 · second-brain-lac-ten.vercel.app · its alternatives →
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Get a real-time feed of product opportunities with Sprig AI
2023 · app.sprig.com · its alternatives →
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2018 · modeldepot.io · its alternatives →
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We built Valohai LLM for tracking and comparing LLM evaluation results. Whether your evals live in notebooks and spreadsheets, or you're using an observability tool that wasn't built for comparison, this gives you a purpose-built eval comparison dashboard. Run evals with a Python library (pip install valohai-llm), results stream in, and you can compare up to 6 configurations side by side. Group by any dimension (model, category, difficulty) to see where each model excels. It doesn't do tracing or production observability, for now just eval tracking and comparison. What's cool is that you can…
2020 · valohai.com · its alternatives →
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Hi HN! We (Thomas and Stéphan, hello!) recently released Model2Vec, a Python library for distilling any sentence transformer into a small set of static embeddings. This makes inference with such a model up to 500x faster, and reduces model size by a factor of 15 (7.5M params or 15/30MB on disk, depending on whether you use float16 or float32). This allows you to embed 50-100k documents per second on a cpu on a macbook. This reduction of course comes at a cost: distilled models are worse than their parent models. Even so, they are actually a lot better than large sets of conventional…
2024 · github.com · its alternatives →
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Hi HN! We (Thomas and Stéphan, hello!) recently released Model2Vec, a Python library for distilling any sentence transformer into a small set of static embeddings. This makes inference with such a model up to 500x faster, and reduces model size by a factor of 15 (7.5M params or 15/30MB on disk, depending on whether you use float16 or float32). This reduction of course comes at a cost: distilled models are a lot worse than their parent models. Even so, they are actually a lot better than large sets of conventional static embeddings, such as GLoVe or word2vec-based models, which are many…
2024 · github.com · its alternatives →
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A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017). - benedekrozemberczki/graph2vec
2019 · github.com · its alternatives →
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2020 · colab.research.google.com · its alternatives →
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Hi HN! I've found this visualization tool immensely helpful over the years for getting an intuition for how an LLM "sees" some piece of text, and with a bit of elbow grease decided to move all compute to client side so I could make it publicly available. I've found it particularly useful for - Understanding exactly how repetition and patterns affect a small LM's ability to predict correctly - Understanding different tokenization patterns and how it affects model output - Getting a general sense of how "hard" different prediction tasks are for GPT-style models Known problems (that I probably…
2023 · perplexity.vercel.app · its alternatives →
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Dive deep into the world of social media listening with our comprehensive guide, where we explore strategies, tools, and best practices to help you effectively navigate online feedback, build stronger customer relationships, and enhance your brand reputation.
2014 · trackreddit.com · its alternatives →
Also compare
- Wikipedia2Vec – A tool for learning embeddings of words and entities alternatives
- Sense2vc – Semantic Analysis of the Reddit Hivemind alternatives
- Model2vec – Lightning-fast Static Embeddings for RAG/Semantic Search alternatives
- Google EmbeddingGemma 2 alternatives
- Model2vec-Rs – Fast Static Text Embeddings in Rust alternatives
- RedCom.ai alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →