diff --git a/docs/embeddings.md b/docs/embeddings.md
index 1714615071..5699f11897 100644
--- a/docs/embeddings.md
+++ b/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.
 
@@ -29,6 +28,13 @@ 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')`.
 
diff --git a/notebooks/Adapter_With_Embeddings.ipynb b/notebooks/Adapter_With_Embeddings.ipynb
new file mode 100644
index 0000000000..badf9ed266
--- /dev/null
+++ b/notebooks/Adapter_With_Embeddings.ipynb
@@ -0,0 +1,784 @@
+{
+  "cells": [
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Adapter Training with Embeddings\n",
+        "\n",
+        "The `adapters` library also allows you to train the embeddings with your adapter. This can also be used with a completly different tokenizer. This can be beneficial e.g. if the language you are working with is not well suited for the tokenizer of the model.\n",
+        "\n",
+        "This notebook will show how to train embeddings for a new tokenizer with an example case. (Note that this is only if an illustrative example that trains for a shorter number of steps, so the difference between the original and the new embeddings performance is very small.)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_CTO-c0uBrA7",
+        "outputId": "390048c1-4423-4229-cc46-52bd2d39110a"
+      },
+      "outputs": [],
+      "source": [
+        "! pip install -U adapters\n",
+        "! pip install -q datasets\n",
+        "! pip install -q accelerate"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GUSfPj1jFtBF"
+      },
+      "source": [
+        "Adding embeddings follows the same structure as adding adapters. Simply call `add_embeddings` and provide a new name for the embedding and the tokenizer that the embeddings should work with.\n",
+        "\n",
+        "To copy embeddings that are shared with an other tokenizer provide the name of the embeddings as `reference_embeddings` (or `default` if you want to use the original embeddings of the loaded model) and `reference_tokenizer` corresponding to the reference embeddings."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eo6klvA3Bsf4",
+        "outputId": "6f0ceb41-7fa8-4037-ca67-4bd22eacdddb"
+      },
+      "outputs": [],
+      "source": [
+        "from adapters import AutoAdapterModel\n",
+        "from transformers import AutoTokenizer\n",
+        "\n",
+        "model_name = \"roberta-base\"\n",
+        "\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-chinese\")\n",
+        "\n",
+        "chinese_tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "\n",
+        "model = AutoAdapterModel.from_pretrained(model_name)\n",
+        "model.add_adapter(\"a\")\n",
+        "model.add_embeddings(\"a\", chinese_tokenizer, reference_embedding=\"default\", reference_tokenizer=tokenizer)\n",
+        "model.add_classification_head(\"a\", num_labels=2)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OXwk1v7NHCgV"
+      },
+      "source": [
+        "To set the active embeddings, call `set_active_embeddings` and pass the name of the embeddings you want to set as active."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "Dt0VVFFFCS0T"
+      },
+      "outputs": [],
+      "source": [
+        "model.set_active_embeddings(\"a\")"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2sedLcurHLlk"
+      },
+      "source": [
+        "To train the embeddings, set the `train_embeddings` attribute to true in the `train_adapter` method. This will set the passed adapter setup as active and freeze all weights except for the adapter weights and the embedding weights (make sure the correct embedding is activated with `set_active_embeddings`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "TynU-4B1FQ10"
+      },
+      "outputs": [],
+      "source": [
+        "model.train_adapter(\"a\", train_embeddings=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G49N4LTnOKuf"
+      },
+      "source": [
+        "Next, we load and preprocess the dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "g0Ay4cK5Cdu1",
+        "outputId": "a0de5a1d-b90e-4950-d5da-974e1cf8bb8b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    train: Dataset({\n",
+              "        features: ['sentence1', 'sentence2', 'label'],\n",
+              "        num_rows: 62477\n",
+              "    })\n",
+              "    validation: Dataset({\n",
+              "        features: ['sentence1', 'sentence2', 'label'],\n",
+              "        num_rows: 20000\n",
+              "    })\n",
+              "    test: Dataset({\n",
+              "        features: ['sentence1', 'sentence2', 'label'],\n",
+              "        num_rows: 20000\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset = load_dataset(\"shibing624/nli_zh\", \"ATEC\")\n",
+        "dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "98c24a189b24490aac44830482c18ff5",
+            "49d5550b9d1c464a93ea012c17fcb903",
+            "08fb5b4d88aa46cf9ac9ae48d752ae35",
+            "80fd53eb99ed469d9ff7c35efaf71849",
+            "47d9afe6304c4c6da9357a0f08439a91",
+            "11d79c476f1949f0b944eeb42a0d7e4c",
+            "fa2d8938a1f04fbd984b5621ef74fa27",
+            "949b410645704312be1043287b9686ad",
+            "7ebf93d6ab3843f9b98b52da2fc34def",
+            "f3c836272a8a40a98fb7258269dac760",
+            "71a73c610e0c48c3881c5329fb9f0e75"
+          ]
+        },
+        "id": "ARMSP7k_Dbsv",
+        "outputId": "a05ddef4-33dd-487c-f88f-2363103a2971"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "98c24a189b24490aac44830482c18ff5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Map:   0%|          | 0/20000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "def encode_batch(batch):\n",
+        "  \"\"\"Encodes a batch of input data using the model tokenizer.\"\"\"\n",
+        "  return tokenizer(batch[\"sentence1\"], batch[\"sentence2\"], max_length=80, truncation=True, padding=\"max_length\")\n",
+        "\n",
+        "# Encode the input data\n",
+        "dataset = dataset.map(encode_batch, batched=True)\n",
+        "# The transformers model expects the target class column to be named \"labels\"\n",
+        "\n",
+        "dataset = dataset.rename_column(original_column_name=\"label\", new_column_name=\"labels\")\n",
+        "# Transform to pytorch tensors and only output the required columns\n",
+        "dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yLH-v6ZcOQe-"
+      },
+      "source": [
+        "The trainings setup does not change compared to training just the adapter."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7z96wkvQEdF0",
+        "outputId": "22a53388-2d41-4a55-ee7d-8502757ec114"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from transformers import TrainingArguments, EvalPrediction\n",
+        "from adapters import AdapterTrainer\n",
+        "\n",
+        "training_args = TrainingArguments(\n",
+        "    learning_rate=1e-4,\n",
+        "    per_device_train_batch_size=32,\n",
+        "    per_device_eval_batch_size=32,\n",
+        "    logging_steps=200,\n",
+        "    output_dir=\"./training_output\",\n",
+        "    overwrite_output_dir=True,\n",
+        "    remove_unused_columns=False,\n",
+        "    # This would probably need to be bigger\n",
+        "    # but for illustration and for it to run in colab this is small\n",
+        "    max_steps = 5000,\n",
+        ")\n",
+        "\n",
+        "def compute_accuracy(p: EvalPrediction):\n",
+        "  preds = np.argmax(p.predictions, axis=1)\n",
+        "  return {\"acc\": (preds == p.label_ids).mean()}\n",
+        "\n",
+        "trainer = AdapterTrainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=dataset[\"train\"],\n",
+        "    eval_dataset=dataset[\"validation\"],\n",
+        "    compute_metrics=compute_accuracy,\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 933
+        },
+        "id": "IIF0d6HpE-jm",
+        "outputId": "19d6a9fd-1143-4a53-d2d8-00dbf81bddf1"
+      },
+      "outputs": [],
+      "source": [
+        "trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 147
+        },
+        "id": "u84x5Rk7Fg8j",
+        "outputId": "542cd919-5deb-4f91-80cc-8a2d293a0bcf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [625/625 01:29]\n",
+              "    </div>\n",
+              "    "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'eval_loss': 0.4556490182876587,\n",
+              " 'eval_acc': 0.81615,\n",
+              " 'eval_runtime': 89.8272,\n",
+              " 'eval_samples_per_second': 222.65,\n",
+              " 'eval_steps_per_second': 6.958,\n",
+              " 'epoch': 2.56}"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trainer.evaluate()"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IPu6q0_NOWpX"
+      },
+      "source": [
+        "You can dynamically change the embeddings. For instance, to evaluate with the original embedding you can simply do the following:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "4pcHbes-JDba"
+      },
+      "outputs": [],
+      "source": [
+        "model.set_active_embeddings(\"default\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 147
+        },
+        "id": "luyH4MIXLck-",
+        "outputId": "1743e066-f868-499f-b687-9c9df47c5ab7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='1250' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [625/625 02:58]\n",
+              "    </div>\n",
+              "    "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'eval_loss': 0.5493476390838623,\n",
+              " 'eval_acc': 0.81535,\n",
+              " 'eval_runtime': 88.7409,\n",
+              " 'eval_samples_per_second': 225.375,\n",
+              " 'eval_steps_per_second': 7.043,\n",
+              " 'epoch': 2.56}"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trainer.evaluate()"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This notebook provides a a toy example on how to add, train and change the embedding. For more info, check our [documentation](https://docs.adapterhub.ml/embeddings.html) and the [EmbeddingMixin](https://docs.adapterhub.ml/classes/model_mixins.html#embeddingadaptersmixin). "
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "test_env",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.8.18 (default, Sep 11 2023, 08:17:16) \n[Clang 14.0.6 ]"
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