Update README.md

README.md

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 # dense_encoder-msmarco-distilbert-word2vec256k-MLM_445k
 
-This model is based on [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k](https://huggingface.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k) with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 445k steps with frozen embedding matrix on the MSMARCO corpus.
+This model is based on [vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k](https://huggingface.co/vocab-transformers/msmarco-distilbert-word2vec256k-MLM_445k) with a 256k sized vocabulary initialized with word2vec that has been trained with MLM for 445k steps. **Note: Token embeddings where updated!**
 
-It has been trained on MS MARCO using [MarginMSELoss](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py) with a frozen embedding matrix. See the train_script.py in this repository.
+It has been trained on MS MARCO using [MarginMSELoss](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_margin-mse.py). See the train_script.py in this repository. **Note: Token embeddings where updated!**
 
 
 Performance: