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README.md

@@ -10,7 +10,12 @@ tags:
 
 # potion-multilingual-128M Model Card
 
-This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of a 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. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
+<div align="center">
+  <img width="35%" alt="Model2Vec logo" src="https://raw.githubusercontent.com/MinishLab/model2vec/main/assets/images/logo_v2.png">
+</div>
+
+
+This [Model2Vec](https://github.com/MinishLab/model2vec) model is pre-trained using Tokenlearn. It is a distilled version of the [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) 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. It's a multilingual model, trained on 101 languages, and is capable of generating embeddings for any text in any language.
 
 
 ## Installation
@@ -37,47 +42,25 @@ model = StaticModel.from_pretrained("potion-multilingual-128M")
 embeddings = model.encode(["Example sentence"])
 ```
 
-### Using Sentence Transformers
-
-You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
-
-```python
-from sentence_transformers import SentenceTransformer
-
-# Load a pretrained Sentence Transformer model
-model = SentenceTransformer("potion-multilingual-128M")
-
-# Compute text embeddings
-embeddings = model.encode(["Example sentence"])
-```
-
-### Distilling a Model2Vec model
-
-You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code:
-
-```python
-from model2vec.distill import distill
-
-# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
-m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", 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.
+Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps:
+
+- Distillation: first, a model is distilled from a sentence transformer model using Model2Vec.
+- Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus.
+- Training: the distilled model is trained on the training data using Tokenlearn.
+- Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their 
+- frequency, applying PCA, and finally applying SIF weighting.
 
-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 [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence.
+The results for this model can be found on the [Model2Vec results page](https://github.com/MinishLab/model2vec/blob/main/results/README.md).
 
 ## Additional Resources
 
+- [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec)
 - [Model2Vec Repo](https://github.com/MinishLab/model2vec)
-- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
-- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
+- [Tokenlearn repo](https://github.com/MinishLab/tokenlearn)
+- [Model2Vec Results](https://github.com/MinishLab/model2vec/blob/main/results/README.md)
 - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
-- [Website](https://minishlab.github.io/)
 
 
 ## Library Authors
@@ -86,8 +69,9 @@ Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team c
 
 ## Citation
 
-Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
-```
+If you use Model2Vec in your research, please cite the following:
+
+```bibtex
 @article{minishlab2024model2vec,
   author = {Tulkens, Stephan and {van Dongen}, Thomas},
   title = {Model2Vec: Fast State-of-the-Art Static Embeddings},