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type: main_score value: 39.18863241150203 - type: v_measure value: 39.18863241150203 - type: v_measure_std value: 4.7194058271386705 task: type: Clustering --- # 🪲 brown-beetle-small-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 `baai/bge-base-en-v1.5`, 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. > [!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-v1.1") # 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}, } ```