Feature Extraction
Model2Vec
Safetensors
sentence-transformers
English
embeddings
static-embeddings
sentence-similarity
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Add library authors and citation

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@@ -376,3 +376,23 @@ Again following Erik's work, we then perform hard-negative fine-tuning on [light
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  ### 4. Weight averaging
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  We found that weight averaging then caused the model to outperform either of the stages separately: the final model is 30% of the contrastive model from step 2 and 70% of the hard-negative model from step 3, giving us a boost on MTEB without sacrificing retrieval quality.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### 4. Weight averaging
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  We found that weight averaging then caused the model to outperform either of the stages separately: the final model is 30% of the contrastive model from step 2 and 70% of the hard-negative model from step 3, giving us a boost on MTEB without sacrificing retrieval quality.
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+ ## Library Authors
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+ Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
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+ ## Citation
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+ Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
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+ ```bibtex
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+ @software{minishlab2024model2vec,
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+ author = {Stephan Tulkens and {van Dongen}, Thomas},
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+ title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
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+ year = {2024},
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+ publisher = {Zenodo},
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+ doi = {10.5281/zenodo.17270888},
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+ url = {https://github.com/MinishLab/model2vec},
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+ license = {MIT}
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+ }
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+ ```