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type: cosine_accuracy value: 85.89474909768309 - type: cosine_accuracy_threshold value: 66.7208384412081 - type: cosine_ap value: 79.33674860685535 - type: cosine_f1 value: 71.67923542148885 - type: cosine_f1_threshold value: 61.64839965467019 - type: cosine_precision value: 69.57530076822728 - type: cosine_recall value: 73.91438250692947 - type: dot_accuracy value: 85.89474909768309 - type: dot_accuracy_threshold value: 66.72083970945883 - type: dot_ap value: 79.33671093343192 - type: dot_f1 value: 71.67923542148885 - type: dot_f1_threshold value: 61.648404274339356 - type: dot_precision value: 69.57530076822728 - type: dot_recall value: 73.91438250692947 - type: euclidean_accuracy value: 85.89474909768309 - type: euclidean_accuracy_threshold value: 81.58328528607446 - type: euclidean_ap value: 79.33675212612457 - type: euclidean_f1 value: 71.67923542148885 - type: euclidean_f1_threshold value: 87.58036675764342 - type: euclidean_precision value: 69.57530076822728 - type: euclidean_recall value: 73.91438250692947 - type: main_score value: 79.33675212612457 - type: manhattan_accuracy value: 85.89863003065938 - type: manhattan_accuracy_threshold value: 513.242605511914 - type: manhattan_ap value: 79.06488846026886 - type: manhattan_f1 value: 71.42752882790833 - type: manhattan_f1_threshold value: 563.6103289492894 - type: manhattan_precision value: 67.88984461709212 - type: manhattan_recall value: 75.3541730828457 - type: max_accuracy value: 85.89863003065938 - type: max_ap value: 79.33675212612457 - type: max_f1 value: 71.67923542148885 - type: max_precision value: 69.57530076822728 - type: max_recall value: 75.3541730828457 - type: similarity_accuracy value: 85.89474909768309 - type: similarity_accuracy_threshold value: 66.7208384412081 - type: similarity_ap value: 79.33674860685535 - type: similarity_f1 value: 71.67923542148885 - type: similarity_f1_threshold value: 61.64839965467019 - type: similarity_precision value: 69.57530076822728 - type: similarity_recall value: 73.91438250692947 --- # potion-base-2M Model Card
Model2Vec logo
This [Model2Vec](https://github.com/MinishLab/model2vec) model is pre-trained using [Tokenlearn](https://github.com/MinishLab/tokenlearn). It is a distilled version of the [baai/bge-base-en-v1.5](https://huggingface.co/baai/bge-base-en-v1.5) 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. ## Installation Install model2vec using pip: ``` pip install model2vec ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("minishlab/potion-base-2M") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ## How it works Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on [MTEB](https://huggingface.co/spaces/mteb/leaderboard). This model is pre-trained using [Tokenlearn](https://github.com/MinishLab/tokenlearn). It's created using the following steps: - Distillation: first, a model is distilled from a sentence transformer model using Model2Vec. - Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus. - Training: the distilled model is trained on the training data using Tokenlearn. - Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). The results for this model can be found on the [Model2Vec results page](https://github.com/MinishLab/model2vec/blob/main/results/README.md). ## Additional Resources - [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec) - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Tokenlearn repo](https://github.com/MinishLab/tokenlearn) - [Model2Vec Results](https://github.com/MinishLab/model2vec/blob/main/results/README.md) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) ## Library Authors 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). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @software{minishlab2024model2vec, authors = {Stephan Tulkens, Thomas van Dongen}, title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model}, year = {2024}, url = {https://github.com/MinishLab/model2vec}, } ```