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This Model2Vec model is a distilled version of the qwen/qwen3-embedding-4B Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers. The Model2Vec library is the fastest and most lightweight way to run Model2Vec models. You can also use the Sentence Transformers library to load and use the model: You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code: Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using SIF weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence. - Model2Vec Repo - Model2Vec Base Models - Model2Vec Results - Model2Vec Tutorials - Website Model2Vec was developed by the Minish Lab team consisting of Stephan Tulkens and Thomas van Dongen. Please cite the Model2Vec repository if you use this model in your work.

This Model2Vec model is a distilled version of the qwen/qwen3-embedding-0.6B Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers. The Model2Vec library is the fastest and most lightweight way to run Model2Vec models. You can also use the Sentence Transformers library to load and use the model: You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code: Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using SIF weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence. - Model2Vec Repo - Model2Vec Base Models - Model2Vec Results - Model2Vec Tutorials - Website Model2Vec was developed by the Minish Lab team consisting of Stephan Tulkens and Thomas van Dongen. Please cite the Model2Vec repository if you use this model in your work.

XLM-RoBERTa OVOS intent classifier (base-sized model) XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository. This model was fine-tuned to classify intents based on the dataset Jarbas/ovosintentstrain You can use the raw model for intent classification in the Open Voice OS project context.