docs: Fix typo (#145)

emebeddings -> embeddings

Author: eltociear

README.md

@@ -124,7 +124,7 @@ Our [potion models](https://huggingface.co/collections/minishlab/potion-6721e0ab
 - **Distillation**: We distill a Model2Vec model from a Sentence Transformer model, using the method described above.
 - **Sentence Transformer inference**: We use the Sentence Transformer model to create mean embeddings for a large number of texts from a corpus.
 - **Training**: We train a model to minimize the cosine distance between the mean embeddings generated by the Sentence Transformer model and the mean embeddings generated by the Model2Vec model.
-- **Post-training re-regularization**: We re-regularize the trained emebeddings by first performing PCA, and then weighting the embeddings using `smooth inverse frequency (SIF)` weighting using the following formula: `w = 1e-3 / (1e-3 + proba)`. Here, `proba` is the probability of the token in the corpus we used for training.
+- **Post-training re-regularization**: We re-regularize the trained embeddings by first performing PCA, and then weighting the embeddings using `smooth inverse frequency (SIF)` weighting using the following formula: `w = 1e-3 / (1e-3 + proba)`. Here, `proba` is the probability of the token in the corpus we used for training.
 
 
 For a much more extensive deepdive, please refer to our [Model2Vec blog post](https://huggingface.co/blog/Pringled/model2vec) and our [Tokenlearn blog post](https://minishlab.github.io/tokenlearn_blogpost/).