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type: main_score value: 57.9645989101073 - type: manhattan_accuracy value: 79.41825117720688 - type: manhattan_accuracy_threshold value: 28691.644287109375 - type: manhattan_ap value: 47.06475509042154 - type: manhattan_f1 value: 46.79797145889845 - type: manhattan_f1_threshold value: 39006.52160644531 - type: manhattan_precision value: 42.31179355939433 - type: manhattan_recall value: 52.34828496042216 - type: max_accuracy value: 81.92763902962389 - type: max_ap value: 57.9645989101073 - type: max_f1 value: 55.27800161160354 - type: max_precision value: 49.030018378599145 - type: max_recall value: 64.35356200527704 - type: similarity_accuracy value: 81.92763902962389 - type: similarity_accuracy_threshold value: 71.09723091125488 - type: similarity_ap value: 57.9645989101073 - type: similarity_f1 value: 55.27800161160354 - type: similarity_f1_threshold value: 62.15031147003174 - type: similarity_precision value: 49.030018378599145 - type: similarity_recall value: 63.35092348284961 task: type: PairClassification - dataset: config: default name: MTEB TwitterURLCorpus (default) revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics: - type: cosine_accuracy value: 86.96588659913843 - type: cosine_accuracy_threshold value: 61.520421504974365 - type: cosine_ap value: 81.7361816102444 - type: cosine_f1 value: 73.97320448056227 - type: cosine_f1_threshold value: 55.81372380256653 - type: cosine_precision value: 70.50942079553386 - type: cosine_recall value: 77.79488758854328 - type: dot_accuracy value: 82.9413591027283 - type: dot_accuracy_threshold value: 22375.634765625 - type: dot_ap value: 70.36676649129193 - type: dot_f1 value: 67.16835375599362 - type: dot_f1_threshold value: 16350.4638671875 - type: dot_precision value: 59.176249706641634 - type: dot_recall value: 77.65629812134279 - type: euclidean_accuracy value: 82.51251600884852 - type: euclidean_accuracy_threshold value: 1394.6439743041992 - type: euclidean_ap value: 68.29382960739675 - type: euclidean_f1 value: 60.384865526607754 - type: euclidean_f1_threshold value: 1643.9237594604492 - type: euclidean_precision value: 60.01064557828302 - type: euclidean_recall value: 60.763781952571605 - type: main_score value: 81.73619060168708 - type: manhattan_accuracy value: 82.74149105444948 - type: manhattan_accuracy_threshold value: 27013.568115234375 - type: manhattan_ap value: 68.65648959722192 - type: manhattan_f1 value: 60.770683792973166 - type: manhattan_f1_threshold value: 31251.20849609375 - type: manhattan_precision value: 59.65732087227414 - type: manhattan_recall value: 61.926393594086846 - type: max_accuracy value: 86.96588659913843 - type: max_ap value: 81.73619060168708 - type: max_f1 value: 73.97320448056227 - type: max_precision value: 70.50942079553386 - type: max_recall value: 77.79488758854328 - type: similarity_accuracy value: 86.96588659913843 - type: similarity_accuracy_threshold value: 61.520421504974365 - type: similarity_ap value: 81.73619060168708 - type: similarity_f1 value: 73.97320448056227 - type: similarity_f1_threshold value: 55.81372380256653 - type: similarity_precision value: 70.50942079553386 - type: similarity_recall value: 77.79488758854328 task: type: PairClassification --- # 🪲 red-beetle-base-v1.1 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 `mixedbread-ai/mxbai-embed-2d-large-v1`, with PCA at 1024 dimensions, Zipf and SIF re-weighting, learnt from a subset of the FineWeb-Edu sample-10BT dataset. It outperforms the original M2V_base_output model in all tasks. ## Version Information - **red-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. - **red-beetle-base-v1**: The original model, with PCA at 1024 dimensions and (Zipf)^3 re-weighting. - **red-beetle-small-v1**: A smaller version of the original model, with PCA at 384 dimensions and (Zipf)^3 re-weighting. - **red-beetle-base-v1.1**: The original model, with PCA at 1024 dimensions, Zipf and SIF re-weighting, learnt from a subset of the FineWeb-Edu sample-10BT dataset. - **red-beetle-small-v1.1**: A smaller version of the original model, with PCA at 384 dimensions, Zipf and SIF re-weighting, learnt from a subset of the FineWeb-Edu sample-10BT dataset. ## 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/red-beetle-base-v1.1") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` Read more about the Model2Vec library [here](https://github.com/MinishLab/model2vec). ## 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}, } ```