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README.md

@@ -113,17 +113,15 @@ language:
 - zu
 library_name: model2vec
 license: mit
-model_name: M2V_base_multilingual
+model_name: minishlab/m2v_multilingual_output
 tags:
 - embeddings
 - static-embeddings
 ---
 
-# M2V_base_multilingual Model Card
-
-Model2Vec distills a Sentence Transformer into a small, static model.
-This model is ideal for applications requiring fast, lightweight embeddings.
+# minishlab/m2v_multilingual_output Model Card
 
+This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) 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
@@ -134,10 +132,14 @@ pip install model2vec
 ```
 
 ## Usage
-A StaticModel can be loaded using the `from_pretrained` method:
+Load this model using the `from_pretrained` method:
 ```python
 from model2vec import StaticModel
-model = StaticModel.from_pretrained("minishlab/M2V_base_output")
+
+# Load a pretrained Model2Vec model
+model = StaticModel.from_pretrained("minishlab/m2v_multilingual_output")
+
+# Compute text embeddings
 embeddings = model.encode(["Example sentence"])
 ```
 
@@ -161,16 +163,25 @@ Model2vec creates a small, fast, and powerful model that outperforms other stati
 
 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.
 
-## Citation
-
-Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
-
 ## 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)
 
-## Model Authors
+## 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
 
-Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of Stephan Tulkens and Thomas van Dongen.
+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},
+}
+```

model.safetensors

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