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type: similarity_accuracy_threshold value: 70.92338800430298 - type: similarity_ap value: 59.782593612460154 - type: similarity_f1 value: 56.536101934874935 - type: similarity_f1_threshold value: 63.08456063270569 - type: similarity_precision value: 51.13102859581733 - type: similarity_recall value: 63.21899736147757 task: type: PairClassification - 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: 65.62072038650513 - type: cosine_ap value: 79.86335548414907 - type: cosine_f1 value: 72.03383314109958 - type: cosine_f1_threshold value: 62.21768856048584 - type: cosine_precision value: 71.93979419444018 - type: cosine_recall value: 72.12811826301201 - type: dot_accuracy value: 82.84045484534482 - type: dot_accuracy_threshold value: 35566.6259765625 - type: dot_ap value: 69.69122370934686 - type: dot_f1 value: 64.93162154619034 - type: dot_f1_threshold value: 28885.33935546875 - type: dot_precision value: 59.36463383516203 - type: dot_recall value: 71.65075454265477 - type: euclidean_accuracy value: 83.63022470601933 - type: euclidean_accuracy_threshold value: 1693.5800552368164 - type: euclidean_ap value: 71.735550250604 - type: euclidean_f1 value: 63.85325230699978 - type: euclidean_f1_threshold value: 1923.9212036132812 - type: euclidean_precision value: 62.26042428675933 - type: euclidean_recall value: 65.52971974129966 - type: main_score value: 79.86335865098036 - type: manhattan_accuracy value: 83.70978383203322 - type: manhattan_accuracy_threshold value: 21349.06005859375 - type: manhattan_ap value: 72.01852759909437 - type: manhattan_f1 value: 64.3416426973474 - type: manhattan_f1_threshold value: 23114.75830078125 - type: manhattan_precision value: 66.86316838259715 - type: manhattan_recall value: 62.003387742531565 - type: max_accuracy value: 86.35269918888501 - type: max_ap value: 79.86335865098036 - 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: 65.62072038650513 - type: similarity_ap value: 79.86335865098036 - type: similarity_f1 value: 72.03383314109958 - type: similarity_f1_threshold value: 62.21768856048584 - type: similarity_precision value: 71.93979419444018 - type: similarity_recall value: 72.12811826301201 task: type: PairClassification --- # 🪲 brown-beetle-small-v1 Model Card
Beetle logo
> [!TIP] > Beetles are some of the most diverse and interesting creatures on Earth. They are found in every environment, from the deepest oceans to the highest mountains. They are also known for their ability to adapt to a wide range of habitats and lifestyles. They are small, fast and powerful! The beetle series of models are made as good starting points for Static Embedding training (via TokenLearn or Fine-tuning), as well as decent Static Embedding models. Each beetle model is made to be an improvement over the original **M2V_base_output** model in some way, and that's the threshold we set for each model (except the brown beetle series, which is the original model). This model has been distilled from `baai/bge-base-en-v1.5`, with PCA with 256 dimensions and applying Zipf. > [!NOTE] > The brown beetle series is made for convinience in loading and using the model instead of having to run it, though it is pretty fast to reproduce anyways. If you want to use the original model by the folks from the Minish Lab, you can use the **M2V_base_output** model. ## Version Information - **brown-beetle-base-v0**: The original model, without using PCA or Zipf. The lack of PCA and Zipf also makes this a decent model for further training. - **brown-beetle-base-v0.1**: The original model, with PCA but of the same size as the original model. This model is great if you want to experiment with Zipf or other weighting methods. - **brown-beetle-base-v1**: The original model, with PCA and Zipf. - **brown-beetle-small-v1**: A smaller version of the original model, with PCA and Zipf. Equivalent to **M2V_base_output**. - **brown-beetle-tiny-v1**: A tiny version of the original model, with PCA and Zipf. - **brown-beetle-base-v1.1**: The original model, with PCA with 768 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model. - **brown-beetle-small-v1.1**: A smaller version of the original model, with PCA with 256 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model but slightly worse than the brown-beetle-base-v1.1 model. - **brown-beetle-tiny-v1.1**: A tiny version of the original model, with PCA with 128 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model but slightly worse than the brown-beetle-small-v1.1 model. ## Installation Install model2vec using pip: ```bash 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("bhavnicksm/brown-beetle-small-v1") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` Read more about the Model2Vec library [here](https://github.com/MinishLab/model2vec). ## Reproduce this model To reproduce this model, you must install the `model2vec[distill]` package and use the following code: ```python from model2vec.distill import distill # Distill the model m2v_model = distill( model_name="bge-base-en-v1.5", pca_dims=256, apply_zipf=True, ) # Save the model m2v_model.save_pretrained("brown-beetle-small-v1") ``` ## Comparison with other models Coming soon... ## Acknowledgements This model is made using the [Model2Vec](https://github.com/MinishLab/model2vec) library. Credit goes to the [Minish Lab](https://github.com/MinishLab) team for developing this library. ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ```bibtex @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}, } ```