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dataset: config: default name: MTEB TwitterURLCorpus (default) revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics: - type: cosine_accuracy value: 86.35269918888501 - type: cosine_accuracy_threshold value: 65.62063097953796 - type: cosine_ap value: 79.86337146522463 - type: cosine_f1 value: 72.03383314109958 - type: cosine_f1_threshold value: 62.217533588409424 - type: cosine_precision value: 71.93979419444018 - type: cosine_recall value: 72.12811826301201 - type: dot_accuracy value: 82.84045484534482 - type: dot_accuracy_threshold value: 35566.62902832031 - type: dot_ap value: 69.69127356271262 - type: dot_f1 value: 64.93162154619034 - type: dot_f1_threshold value: 28885.244750976562 - type: dot_precision value: 59.36463383516203 - type: dot_recall value: 71.65075454265477 - type: euclidean_accuracy value: 83.63022470601933 - type: euclidean_accuracy_threshold value: 1693.5848236083984 - type: euclidean_ap value: 71.73555972139718 - type: euclidean_f1 value: 63.8556476722812 - type: euclidean_f1_threshold value: 1923.9103317260742 - type: euclidean_precision value: 62.26497914990124 - type: euclidean_recall value: 65.52971974129966 - type: main_score value: 79.86337146522463 - type: manhattan_accuracy value: 83.70978383203322 - type: manhattan_accuracy_threshold value: 21348.568725585938 - type: manhattan_ap value: 72.01847359087003 - type: manhattan_f1 value: 64.34136401773942 - type: manhattan_f1_threshold value: 23113.516235351562 - type: manhattan_precision value: 66.8715222988124 - type: manhattan_recall value: 61.99568832768709 - type: max_accuracy value: 86.35269918888501 - type: max_ap value: 79.86337146522463 - type: max_f1 value: 72.03383314109958 - type: max_precision value: 71.93979419444018 - type: max_recall value: 72.12811826301201 - type: similarity_accuracy value: 86.35269918888501 - type: similarity_accuracy_threshold value: 65.62063097953796 - type: similarity_ap value: 79.86337146522463 - type: similarity_f1 value: 72.03383314109958 - type: similarity_f1_threshold value: 62.217533588409424 - type: similarity_precision value: 71.93979419444018 - type: similarity_recall value: 72.12811826301201 task: type: PairClassification model_name: minishlab/M2V_base_output tags: - embeddings - static-embeddings - mteb --- # minishlab/M2V_base_output Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model 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/M2V_base_output") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` Alternatively, you can distill your own model using the `distill` method: ```python from model2vec.distill import distill # Choose a Sentence Transformer model model_name = "BAAI/bge-base-en-v1.5" # Distill the model m2v_model = distill(model_name=model_name, pca_dims=256) # Save the model m2v_model.save_pretrained("m2v_model") ``` ## How it works 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 zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence. ## Additional Resources - [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec) - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Results](https://github.com/MinishLab/model2vec?tab=readme-ov-file#results) - [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}, } ```