Update README.md

README.md

@@ -5,6 +5,8 @@ model_name: potion-retrieval-512dim-60kvocab-v1replica-v1
 tags:
 - embeddings
 - static-embeddings
+- mteb
+- sentence-transformers
 ---
 
 ```
@@ -21,9 +23,15 @@ PEARL                                                         49.31
 WordSim                                                       50.02
 ```
 
-# potion-retrieval-512dim-60kvocab-v1replica-v1 Model Card
+# potion-retrieval-32M Model Card
+
+<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](https://github.com/MinishLab/tokenlearn). It is a distilled version of the [baai/bge-base-en-v1.5](https://huggingface.co/baai/bge-base-en-v1.5) 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 uses a larger vocabulary size than the [potion-science-8M](https://huggingface.co/minishlab/potion-base-8M) model which can be beneficial for tasks that require a larger vocabulary.
 
-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.
 
 
 ## Installation
@@ -37,39 +45,29 @@ pip install model2vec
 Load this model using the `from_pretrained` method:
 ```python
 from model2vec import StaticModel
-
 # Load a pretrained Model2Vec model
-model = StaticModel.from_pretrained("potion-retrieval-512dim-60kvocab-v1replica-v1")
-
+model = StaticModel.from_pretrained("minishlab/potion-retrieval-32M")
 # Compute text embeddings
 embeddings = model.encode(["Example sentence"])
 ```
 
-Alternatively, you can distill your own model using the `distill` method:
-```python
-from model2vec.distill import distill
-
-# Choose a Sentence Transformer model
-model_name = "BAAI/bge-base-en-v1.5"
-
-# Distill the model
-m2v_model = distill(model_name=model_name, 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](https://huggingface.co/spaces/mteb/leaderboard). This model is pre-trained using [Tokenlearn](https://github.com/MinishLab/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](https://openreview.net/pdf?id=SyK00v5xx).
 
-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.
+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 Results](https://github.com/MinishLab/model2vec?tab=readme-ov-file#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)
 
 ## Library Authors
@@ -81,10 +79,10 @@ Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team c
 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},
+  authors = {Stephan Tulkens and Thomas van Dongen},
+  title = {Model2Vec: The Fastest State-of-the-Art Static Embeddings in the World},
   year = {2024},
-  url = {https://github.com/MinishLab/model2vec},
+  url = {https://github.com/MinishLab/model2vec}
 }
 ```