Update Embedding Documentation

docs/embeddings.md

@@ -1,7 +1,7 @@
 # Embeddings
 
 With `adapters`, we support dynamically adding, loading, and deleting of `Embeddings`. This section
-will give you an overview of these features. 
+will give you an overview of these features. A toy example is illustrated in this [notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/Adapter_With_Embeddings.ipynb).
 
 ## Adding and Deleting Embeddings
 The methods for handling embeddings are similar to the ones handling adapters. To add new embeddings we call
@@ -12,13 +12,12 @@ is currently active, the `active_embeddings` property contains the currently act
 
 ```python
 model.add_embeddings('name', tokenizer, reference_embedding='default', reference_tokenizer=reference_tokenizer)
-embedding_name = model.active_embeddings
 ```
 
 The original embedding of the transformers model is always available under the name `"default"`. To set it as the active
 embedding simply call the `set_active_embedding('name')` method.
 ```python
-model.set_active_embeddings("default")
+model.set_active_embeddings('name')
 ```
 Similarly, all other embeddings can be set as active by passing their name to the `set_active_embedding` method.
 
@@ -28,7 +27,12 @@ we want to delete. However, you cannot delete the default embedding.
 model.delete_embeddings('name')
 ```
 Please note, that if the active embedding is deleted the default embedding is set as the active embedding.
-
+## Training Embeddings
+Embeddings can only be trained with an adapter. To freeze all weights except for the embedding and the adapter:
+```python
+model.train_adapter('adapter_name', train_embeddings=True)
+```
+Except for the `train_embeddings` flag, the training is the same as for just training an adapter (see [Adapter Training](training.md)).
 ## Saving and Loading Embeddings
 You can save the embeddings by calling `save_embeddings('path/to/dir', 'name')` and load them with `load_embeddings('path/to/dir', 'name')`.