{"repo":"MinishLab/model2vec","free":true,"listed":false,"github":"https://github.com/MinishLab/model2vec","clone":"git clone https://github.com/MinishLab/model2vec.git","description":"Fast State-of-the-Art Static Embeddings","language":"Python","stars":2179,"topics":["embeddings","machine-learning","model2vec","nlp","python","sentence-transformers","ai","word-embeddings"],"license":"MIT","category":"machine-learning","readme_excerpt":"Fast State-of-the-Art Static Embeddings 🤗 Models 📖 Docs 🏆 Results 📚 Tutorials 🌐 Blog Model2Vec is a technique to turn any sentence transformer into a small, fast static embedding model. Model2Vec reduces model size by a factor up to 50 and makes models up to 500 times faster, with a small drop in performance. Our best model is the most performant static embedding model in the world. See our results, read our docs, or dive in to see how it works. Quickstart • Updates & Announcements • Main Features • Model List Quickstart Install the lightweight base package with: You can start using Model2Vec by loading one of our flagship models from the HuggingFace hub. These models are pre-trained and ready to use. The following code snippet shows how to load a model and make embeddings, which you can use for any task, such as text classification, retrieval, clustering, or building a RAG system: For advanced usage, see our inference docs. Instead of using one of our models, you can also distill your own Model2Vec model from a Sentence Transformer model. First, install the distillation extras with: Then, you can distill a model in 30 seconds on a CPU with the following code snippet: For advanced usage, see our distillation docs, which includes some distillation best practices. After distillation, you can also fine-tune your own classification models on top of the distilled model, or on a pre-trained model. First, make sure you install the training extras with: Then, you can fine-tune a","default_branch":null,"files":null,"tree":[],"storefront":"/r/MinishLab","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/MinishLab/model2vec/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}