diff --git a/docs/classes/adapter_config.rst b/docs/classes/adapter_config.rst
index 44fff5a0f2..911a3fade2 100644
--- a/docs/classes/adapter_config.rst
+++ b/docs/classes/adapter_config.rst
@@ -6,7 +6,7 @@ Classes representing the architectures of adapter modules and fusion layers.
 Single (bottleneck) adapters
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: adapters.AdapterConfigBase
+.. autoclass:: adapters.AdapterConfig
     :members:
 
 .. autoclass:: adapters.BnConfig
diff --git a/docs/contributing/adding_adapter_methods.md b/docs/contributing/adding_adapter_methods.md
index 29a7801579..9e5f53b560 100644
--- a/docs/contributing/adding_adapter_methods.md
+++ b/docs/contributing/adding_adapter_methods.md
@@ -18,14 +18,14 @@ Therefore, the described steps might not be applicable to each implementation.
 These module classes then have to be inserted into the correct locations within the Transformer model implementation.
 Thus, each adapter method implementation at least should provide two classes:
 
-- a configuration class deriving from `AdapterConfigBase` that provides attributes for all configuration options of the method
+- a configuration class deriving from `AdapterConfig` that provides attributes for all configuration options of the method
 - a module class deriving from the abstract `AdapterLayerBase` that provides the method parameters and a set of standard adapter management functions
     - modules supporting [adapter composition](https://docs.adapterhub.ml/adapter_composition.html) should instead derive from `ComposableAdapterLayerBase`
 
 ### Configuration
 
 All configuration classes reside in `src/adapters/configuration/adapter_config.py`.
-- To add a new configuration class for a new method, create a new subclass of [`AdapterConfigBase`](adapters.AdapterConfigBase).
+- To add a new configuration class for a new method, create a new subclass of [`AdapterConfig`](adapters.AdapterConfig).
     Make sure to set the `architecture` attribute in your class.
 - Finally, also make sure the config class is added to the `__init__.py` files in `src/adapters`.
 
diff --git a/docs/overview.md b/docs/overview.md
index c9af902693..a282df20d2 100644
--- a/docs/overview.md
+++ b/docs/overview.md
@@ -64,7 +64,7 @@ Identifiers and configuration classes are explained in more detail in the [next
 ## Configuration
 
 All supported adapter methods can be added, trained, saved and shared using the same set of model class functions (see [class documentation](adapters.ModelAdaptersMixin)).
-Each method is specified and configured using a specific configuration class, all of which derive from the common [`AdapterConfigBase`](adapters.AdapterConfigBase) class.
+Each method is specified and configured using a specific configuration class, all of which derive from the common [`AdapterConfig`](adapters.AdapterConfig) class.
 E.g., adding one of the supported adapter methods to an existing model instance follows this scheme:
 ```python
 model.add_adapter("name", config=<ADAPTER_CONFIG>)
diff --git a/docs/training.md b/docs/training.md
index bc070f9b96..649ace6e28 100644
--- a/docs/training.md
+++ b/docs/training.md
@@ -59,7 +59,7 @@ Compared to fine-tuning the entire model, we have to make only one significant a
 # task adapter - only add if not existing
 if task_name not in model.adapters_config:
     # resolve the adapter config
-    adapter_config = AdapterConfigBase.load(adapter_args.adapter_config)
+    adapter_config = AdapterConfig.load(adapter_args.adapter_config)
     # add a new adapter
     model.add_adapter(task_name, config=adapter_config)
 # Enable adapter training
diff --git a/docs/transitioning.md b/docs/transitioning.md
index 0df55eec44..d2f9e5481a 100644
--- a/docs/transitioning.md
+++ b/docs/transitioning.md
@@ -50,6 +50,12 @@ The `adapters` library supports the configuration of adapters using [config stri
 For a complete list of config strings and classes see [here](https://docs.adapterhub.ml/overview.html). We strongly recommend using the new config strings, but we will continue to support the old config strings for the time being to make the transition easier.
 Note that with the config strings the coresponding adapter config classes have changed, e.g. `PfeifferConfig` -> `SeqBnConfig`.
 
+Another consequence of this that the `AdapterConfig` class is now not only for the bottleneck adapters anymore, but the base class of all the configurations (previously `AdapterConfigBase`). Hence the function this class serves has changed. However, you can still load adapter configs with:
+```
+adapter_config = AdapterConfig.load("lora")
+```
+
+
 ## Features that are not supported by `adapters`
 
 Compared to `adapter-transformers`, there are a few features that are no longer supported by the `adapters` library: 
diff --git a/examples/pytorch/dependency-parsing/run_udp.py b/examples/pytorch/dependency-parsing/run_udp.py
index 1980d6ada6..8fefe1f49c 100644
--- a/examples/pytorch/dependency-parsing/run_udp.py
+++ b/examples/pytorch/dependency-parsing/run_udp.py
@@ -13,7 +13,7 @@
 
 import adapters
 import adapters.composition as ac
-from adapters import AdapterArguments, AdapterConfigBase, AutoAdapterModel, setup_adapter_training
+from adapters import AdapterArguments, AdapterConfig, AutoAdapterModel, setup_adapter_training
 from preprocessing import preprocess_dataset
 from transformers import AutoConfig, AutoTokenizer, HfArgumentParser, set_seed
 from utils_udp import UD_HEAD_LABELS, DependencyParsingAdapterTrainer, DependencyParsingTrainer, UDTrainingArguments
@@ -252,7 +252,7 @@ def main():
             logger.info("Loading best model for predictions.")
 
             if adapter_args.train_adapter:
-                adapter_config = AdapterConfigBase.load(adapter_args.adapter_config, **adapter_config_kwargs)
+                adapter_config = AdapterConfig.load(adapter_args.adapter_config, **adapter_config_kwargs)
                 model.load_adapter(
                     os.path.join(training_args.output_dir, "best_model", task_name)
                     if training_args.do_train
@@ -262,9 +262,7 @@ def main():
                     **adapter_load_kwargs,
                 )
                 if adapter_args.load_lang_adapter:
-                    lang_adapter_config = AdapterConfigBase.load(
-                        adapter_args.lang_adapter_config, **adapter_config_kwargs
-                    )
+                    lang_adapter_config = AdapterConfig.load(adapter_args.lang_adapter_config, **adapter_config_kwargs)
                     lang_adapter_name = model.load_adapter(
                         os.path.join(training_args.output_dir, "best_model", lang_adapter_name)
                         if training_args.do_train
diff --git a/notebooks/04_Cross_Lingual_Transfer.ipynb b/notebooks/04_Cross_Lingual_Transfer.ipynb
index c951107590..4cebb3e54a 100644
--- a/notebooks/04_Cross_Lingual_Transfer.ipynb
+++ b/notebooks/04_Cross_Lingual_Transfer.ipynb
@@ -34,20 +34,20 @@
       "cell_type": "code",
       "execution_count": 1,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T09:24:50.921564600Z",
+          "start_time": "2023-08-17T09:24:44.078877400Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "qL3Sq1HQynCq",
-        "outputId": "aa36636a-e484-4ca8-bc6e-b54b37b098b7",
-        "ExecuteTime": {
-          "end_time": "2023-08-17T09:24:50.921564600Z",
-          "start_time": "2023-08-17T09:24:44.078877400Z"
-        }
+        "outputId": "aa36636a-e484-4ca8-bc6e-b54b37b098b7"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m204.3/204.3 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m61.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
@@ -84,6 +84,10 @@
       "cell_type": "code",
       "execution_count": 2,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T09:25:09.494858700Z",
+          "start_time": "2023-08-17T09:25:06.870881400Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 258,
@@ -168,120 +172,116 @@
           ]
         },
         "id": "INW7UEhC-I6b",
-        "outputId": "7f22b72b-0c63-4152-f6c2-01da36af2824",
-        "ExecuteTime": {
-          "end_time": "2023-08-17T09:25:09.494858700Z",
-          "start_time": "2023-08-17T09:25:06.870881400Z"
-        }
+        "outputId": "7f22b72b-0c63-4152-f6c2-01da36af2824"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Downloading builder script:   0%|          | 0.00/30.7k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "660b81766f7044b6a1ce2f8c2c45f461",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "660b81766f7044b6a1ce2f8c2c45f461"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/30.7k [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Downloading metadata:   0%|          | 0.00/38.7k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "017e9f5721174b169f12cff28faee34d",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "017e9f5721174b169f12cff28faee34d"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading metadata:   0%|          | 0.00/38.7k [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Downloading readme:   0%|          | 0.00/14.8k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "79ddbccc453643689f129740e5148e24",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "79ddbccc453643689f129740e5148e24"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/14.8k [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
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-              "Downloading data:   0%|          | 0.00/44.0k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "15c65deba3fa46ea914c95f86dbc01eb",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "15c65deba3fa46ea914c95f86dbc01eb"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/44.0k [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
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           "data": {
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "52b2e86553c14b148d37d3dddc3bc464",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "52b2e86553c14b148d37d3dddc3bc464"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/400 [00:00<?, ? examples/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
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           "data": {
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-              "Generating validation split:   0%|          | 0/100 [00:00<?, ? examples/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7501aa4a0a5f43f49d6dc10857e33317",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "7501aa4a0a5f43f49d6dc10857e33317"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating validation split:   0%|          | 0/100 [00:00<?, ? examples/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Generating test split:   0%|          | 0/500 [00:00<?, ? examples/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c4e3a3c37ff3462ca9251f86efe35fd5",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "c4e3a3c37ff3462ca9251f86efe35fd5"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split:   0%|          | 0/500 [00:00<?, ? examples/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'train': 400, 'validation': 100, 'test': 500}"
             ]
           },
+          "execution_count": 2,
           "metadata": {},
-          "execution_count": 2
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -305,19 +305,18 @@
       "cell_type": "code",
       "execution_count": 3,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T09:25:09.509817800Z",
+          "start_time": "2023-08-17T09:25:09.495856Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "ifu4q5IJ-hYI",
-        "outputId": "183f3b9d-a16f-45c8-f806-8ed5cee9eacc",
-        "ExecuteTime": {
-          "end_time": "2023-08-17T09:25:09.509817800Z",
-          "start_time": "2023-08-17T09:25:09.495856Z"
-        }
+        "outputId": "183f3b9d-a16f-45c8-f806-8ed5cee9eacc"
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'premise': Value(dtype='string', id=None),\n",
@@ -328,8 +327,9 @@
               " 'label': ClassLabel(names=['choice1', 'choice2'], id=None)}"
             ]
           },
+          "execution_count": 3,
           "metadata": {},
-          "execution_count": 3
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -349,6 +349,10 @@
       "cell_type": "code",
       "execution_count": 4,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T09:25:29.453443600Z",
+          "start_time": "2023-08-17T09:25:21.211501700Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 209,
@@ -422,96 +426,92 @@
           ]
         },
         "id": "hEnRCQfE_Oi3",
-        "outputId": "99c4bf2b-8552-4a8c-9530-67930c4a187d",
-        "ExecuteTime": {
-          "end_time": "2023-08-17T09:25:29.453443600Z",
-          "start_time": "2023-08-17T09:25:21.211501700Z"
-        }
+        "outputId": "99c4bf2b-8552-4a8c-9530-67930c4a187d"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9a088b764a214b95a00fac01005eb726",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "9a088b764a214b95a00fac01005eb726"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)lve/main/config.json:   0%|          | 0.00/615 [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
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           "data": {
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bf7d06fbfb7d45c98dde0900cebc16a3",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "bf7d06fbfb7d45c98dde0900cebc16a3"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)tencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "785aa76f50cb44cea93bc8d26c8fe117",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "785aa76f50cb44cea93bc8d26c8fe117"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/9.10M [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5221149fd81940099402d651f8dce8d5",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "5221149fd81940099402d651f8dce8d5"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
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+          "metadata": {},
+          "output_type": "display_data"
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a1aa441180e7414bbd30b297a9105643",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "a1aa441180e7414bbd30b297a9105643"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
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-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "fe314b1f2e5d4671bb820bfe16c197e6",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "fe314b1f2e5d4671bb820bfe16c197e6"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -565,6 +565,10 @@
       "cell_type": "code",
       "execution_count": 5,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T09:31:35.849814100Z",
+          "start_time": "2023-08-17T09:25:45.287070400Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 49,
@@ -583,26 +587,22 @@
           ]
         },
         "id": "fnq8n_KP_3aX",
-        "outputId": "9efa442e-b67d-4b98-a5b1-14dc4e3542b7",
-        "ExecuteTime": {
-          "end_time": "2023-08-17T09:31:35.849814100Z",
-          "start_time": "2023-08-17T09:25:45.287070400Z"
-        }
+        "outputId": "9efa442e-b67d-4b98-a5b1-14dc4e3542b7"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Downloading model.safetensors:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "421306178c18489abcdb27c1265ba6aa",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "421306178c18489abcdb27c1265ba6aa"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading model.safetensors:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -635,7 +635,6 @@
       "cell_type": "code",
       "execution_count": 6,
       "metadata": {
-        "id": "jRqbBgS0BoHJ",
         "ExecuteTime": {
           "end_time": "2023-08-17T09:33:12.495169Z",
           "start_time": "2023-08-17T09:32:53.909908500Z"
@@ -679,57 +678,58 @@
             "cda3c917778848c98dbd7752d798989b"
           ]
         },
+        "id": "jRqbBgS0BoHJ",
         "outputId": "7358bb8e-55f8-4c84-9d20-d901290c7838"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d8492798e90e4e1c9d52dad2abfff1a2",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "d8492798e90e4e1c9d52dad2abfff1a2"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ee1dd3ac0e364a9d81211fb48e3a63c2",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ee1dd3ac0e364a9d81211fb48e3a63c2"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a1c8238ae1e441e9a525141b41997c1c",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "a1c8238ae1e441e9a525141b41997c1c"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)eiffer/zh_relu_2.zip:   0%|          | 0.00/29.6M [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
-        "from adapters import AdapterConfigBase\n",
+        "from adapters import AdapterConfig\n",
         "\n",
         "# Load the language adapters\n",
-        "lang_adapter_config = AdapterConfigBase.load(\"seq_bn\", reduction_factor=2)\n",
+        "lang_adapter_config = AdapterConfig.load(\"seq_bn\", reduction_factor=2)\n",
         "model.load_adapter(\"en/wiki@ukp\", config=lang_adapter_config)\n",
         "model.load_adapter(\"zh/wiki@ukp\", config=lang_adapter_config)\n",
         "\n",
@@ -753,11 +753,11 @@
       "cell_type": "code",
       "execution_count": 7,
       "metadata": {
-        "id": "7wBpjGWZ4v7O",
         "ExecuteTime": {
           "end_time": "2023-08-17T09:33:22.096474100Z",
           "start_time": "2023-08-17T09:33:22.074532300Z"
-        }
+        },
+        "id": "7wBpjGWZ4v7O"
       },
       "outputs": [],
       "source": [
@@ -780,11 +780,11 @@
       "cell_type": "code",
       "execution_count": 8,
       "metadata": {
-        "id": "zgGqHJQbijgg",
         "ExecuteTime": {
           "end_time": "2023-08-17T09:33:24.908948500Z",
           "start_time": "2023-08-17T09:33:24.892991800Z"
-        }
+        },
+        "id": "zgGqHJQbijgg"
       },
       "outputs": [],
       "source": [
@@ -816,11 +816,11 @@
       "cell_type": "code",
       "execution_count": 9,
       "metadata": {
-        "id": "j0gFxQRdDkQ6",
         "ExecuteTime": {
           "end_time": "2023-08-17T09:33:43.542080300Z",
           "start_time": "2023-08-17T09:33:42.546744700Z"
-        }
+        },
+        "id": "j0gFxQRdDkQ6"
       },
       "outputs": [],
       "source": [
@@ -871,19 +871,15 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
             "  warnings.warn(\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
             "text/html": [
               "\n",
               "    <div>\n",
@@ -905,19 +901,23 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table><p>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "TrainOutput(global_step=128, training_loss=0.6968793570995331, metrics={'train_runtime': 55.5306, 'train_samples_per_second': 72.032, 'train_steps_per_second': 2.305, 'total_flos': 293495135040000.0, 'train_loss': 0.6968793570995331, 'epoch': 8.0})"
             ]
           },
+          "execution_count": 10,
           "metadata": {},
-          "execution_count": 10
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -940,6 +940,10 @@
       "cell_type": "code",
       "execution_count": 11,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T09:34:08.620963600Z",
+          "start_time": "2023-08-17T09:34:04.407241200Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 426,
@@ -1057,16 +1061,12 @@
           ]
         },
         "id": "cMgM1supdxpw",
-        "outputId": "e7dbf1f2-60f8-480d-8cd6-b601cecd697c",
-        "ExecuteTime": {
-          "end_time": "2023-08-17T09:34:08.620963600Z",
-          "start_time": "2023-08-17T09:34:04.407241200Z"
-        }
+        "outputId": "e7dbf1f2-60f8-480d-8cd6-b601cecd697c"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.10/dist-packages/datasets/load.py:2080: FutureWarning: 'ignore_verifications' was deprecated in favor of 'verification_mode' in version 2.9.1 and will be removed in 3.0.0.\n",
             "You can remove this warning by passing 'verification_mode=no_checks' instead.\n",
@@ -1074,148 +1074,148 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
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-            ],
             "application/vnd.jupyter.widget-view+json": {
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-              "version_minor": 0,
-              "model_id": "216fc1dd69b840788de0425040d646a3"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/3.90k [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
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-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1937074f179040e2b96d7972dc1a6998",
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-              "model_id": "1937074f179040e2b96d7972dc1a6998"
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+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
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           "data": {
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-            ],
             "application/vnd.jupyter.widget-view+json": {
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/19.0k [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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-            ],
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+              "version_minor": 0
+            },
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+            ]
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-          "metadata": {}
+          "metadata": {},
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-              "model_id": "40c2e934eb384c8c8d3f19a9779b28d9"
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+              "version_minor": 0
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+            },
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+            ]
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-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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-            ],
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-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split:   0%|          | 0/500 [00:00<?, ? examples/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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-            ],
             "application/vnd.jupyter.widget-view+json": {
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-              "version_minor": 0,
-              "model_id": "3bf4f93b8e7e4b4aa8d4af2fd8683a56"
-            }
-          },
-          "metadata": {}
-        },
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Map:   0%|          | 0/500 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "{'labels': 0, 'input_ids': [[0, 6, 4941, 55359, 1173, 7825, 30638, 90132, 44507, 5702, 1562, 30, 22304, 2, 2, 6, 3800, 2165, 15068, 69175, 30, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 6, 4941, 55359, 1173, 7825, 30638, 90132, 44507, 5702, 1562, 30, 22304, 2, 2, 6, 3800, 2165, 1128, 30, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}\n"
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@@ -1240,11 +1240,11 @@
       "cell_type": "code",
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           "end_time": "2023-08-17T09:34:44.463042Z",
           "start_time": "2023-08-17T09:34:44.446088100Z"
-        }
+        },
+        "id": "V04UntKfeK_z"
       },
       "outputs": [],
       "source": [
@@ -1264,24 +1264,20 @@
       "cell_type": "code",
       "execution_count": 13,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T09:35:01.032697600Z",
+          "start_time": "2023-08-17T09:34:47.053111900Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 124
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         "id": "CrW1cJyaeMox",
-        "outputId": "2a548b3f-93e8-46b2-eb09-60ff63ba63b9",
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-          "end_time": "2023-08-17T09:35:01.032697600Z",
-          "start_time": "2023-08-17T09:34:47.053111900Z"
-        }
+        "outputId": "2a548b3f-93e8-46b2-eb09-60ff63ba63b9"
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             "text/html": [
               "\n",
               "    <div>\n",
@@ -1290,12 +1286,15 @@
               "      [63/63 00:04]\n",
               "    </div>\n",
               "    "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
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-          "metadata": {}
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@@ -1305,8 +1304,9 @@
               " 'eval_steps_per_second': 12.682}"
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+          "execution_count": 13,
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-          "execution_count": 13
+          "output_type": "execute_result"
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@@ -1338,10 +1338,11 @@
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+    "accelerator": "GPU",
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+      "gpuType": "T4",
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-      "toc_visible": true,
-      "gpuType": "T4"
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@@ -1370,98 +1371,47 @@
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-            "style": "IPY_MODEL_2bc1eb041c624197b006ca3c29e6819d",
-            "value": " 500/500 [00:00&lt;00:00, 1808.74 examples/s]"
+            "style": "IPY_MODEL_7ded086d37894a9e94142d6775d78b91",
+            "value": " 14.8k/14.8k [00:00&lt;00:00, 921kB/s]"
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-        "e9e86e5e4a1341d592f72110112f276d": {
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+            "layout": "IPY_MODEL_c713c1586a344d3da06b0d87764b4afb",
+            "placeholder": "​",
+            "style": "IPY_MODEL_6ff76b8372584d09a7df7bb52fccd176",
+            "value": " 1.12G/1.12G [00:06&lt;00:00, 96.5MB/s]"
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           "model_module": "@jupyter-widgets/base",
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@@ -10454,25 +10383,31 @@
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-            "_view_module": "@jupyter-widgets/base",
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-            "_view_name": "StyleView",
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+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_5a22f62bc57c4c78840c1ef51c621996",
+            "placeholder": "​",
+            "style": "IPY_MODEL_adbd936819104e65a5a6e94817f928f0",
+            "value": " 400/400 [00:00&lt;00:00, 2500.62 examples/s]"
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         },
-        "b36a3e769ea946d9b3a2eee4145d858a": {
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+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
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+            "bar_style": "success",
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+            "layout": "IPY_MODEL_5f4b4f5191754b4eb8aebd5af6c7942d",
+            "max": 500,
+            "min": 0,
+            "orientation": "horizontal",
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@@ -10588,26 +10604,10 @@
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       }
-    },
-    "accelerator": "GPU"
+    }
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/notebooks/08_NER_Wikiann.ipynb b/notebooks/08_NER_Wikiann.ipynb
index 422bf2ba83..dcb0f83d2e 100644
--- a/notebooks/08_NER_Wikiann.ipynb
+++ b/notebooks/08_NER_Wikiann.ipynb
@@ -26,20 +26,20 @@
       "cell_type": "code",
       "execution_count": 1,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T12:18:02.087873100Z",
+          "start_time": "2023-08-17T12:17:54.490601100Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "3AJLS4197g-i",
-        "outputId": "58e147cd-538d-498c-ae67-7b6f7ce3e21a",
-        "ExecuteTime": {
-          "end_time": "2023-08-17T12:18:02.087873100Z",
-          "start_time": "2023-08-17T12:17:54.490601100Z"
-        }
+        "outputId": "58e147cd-538d-498c-ae67-7b6f7ce3e21a"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/251.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/251.2 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m245.8/251.2 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.2/251.2 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h"
@@ -66,28 +66,28 @@
       "cell_type": "code",
       "execution_count": 2,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T10:54:31.730170400Z",
+          "start_time": "2023-08-17T10:54:28.844625500Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "vgQ5DC_48_r_",
-        "outputId": "bb2d049b-6746-43e9-bd32-3a066536dac1",
-        "ExecuteTime": {
-          "end_time": "2023-08-17T10:54:31.730170400Z",
-          "start_time": "2023-08-17T10:54:28.844625500Z"
-        }
+        "outputId": "bb2d049b-6746-43e9-bd32-3a066536dac1"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
             "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']\n"
           ]
@@ -127,19 +127,19 @@
       "cell_type": "code",
       "execution_count": 3,
       "metadata": {
-        "id": "gZj6Jt-P7Ysd",
         "ExecuteTime": {
           "end_time": "2023-08-17T10:55:18.106652Z",
           "start_time": "2023-08-17T10:55:07.121375100Z"
-        }
+        },
+        "id": "gZj6Jt-P7Ysd"
       },
       "outputs": [],
       "source": [
-        "from adapters import AdapterConfigBase\n",
+        "from adapters import AdapterConfig\n",
         "target_language = \"gn\" # Choose any language that a bert-base-multilingual-cased language adapter is available for\n",
         "source_language = \"en\" # We support  \"en\", \"ja\", \"zh\", and \"ar\"\n",
         "\n",
-        "adapter_config = AdapterConfigBase.load(\n",
+        "adapter_config = AdapterConfig.load(\n",
         "    None,\n",
         "    leave_out=[11]\n",
         ")\n",
@@ -172,11 +172,11 @@
       "cell_type": "code",
       "execution_count": 4,
       "metadata": {
-        "id": "o-SUUa367TBr",
         "ExecuteTime": {
           "end_time": "2023-08-17T10:56:49.120227700Z",
           "start_time": "2023-08-17T10:55:35.178236700Z"
-        }
+        },
+        "id": "o-SUUa367TBr"
       },
       "outputs": [],
       "source": [
@@ -205,11 +205,11 @@
       "cell_type": "code",
       "execution_count": 5,
       "metadata": {
-        "id": "bbfMD2EM8OAs",
         "ExecuteTime": {
           "end_time": "2023-08-17T10:57:18.725260400Z",
           "start_time": "2023-08-17T10:57:18.709640900Z"
-        }
+        },
+        "id": "bbfMD2EM8OAs"
       },
       "outputs": [],
       "source": [
@@ -262,6 +262,10 @@
       "cell_type": "code",
       "execution_count": 6,
       "metadata": {
+        "ExecuteTime": {
+          "end_time": "2023-08-17T10:57:33.497198700Z",
+          "start_time": "2023-08-17T10:57:33.406508100Z"
+        },
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 49,
@@ -280,26 +284,22 @@
           ]
         },
         "id": "xrnTO-hV8pRc",
-        "outputId": "f6bd5820-c176-475e-fa10-8f449009587d",
-        "ExecuteTime": {
-          "end_time": "2023-08-17T10:57:33.497198700Z",
-          "start_time": "2023-08-17T10:57:33.406508100Z"
-        }
+        "outputId": "f6bd5820-c176-475e-fa10-8f449009587d"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d7bc633f2ede464aacc1e72fcfa12f7a",
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-        "3dad54731ab948ddb4b80a0d43a8c0a6": {
+        "c1845d1d27864c1887ff7b4882d06a6e": {
           "model_module": "@jupyter-widgets/controls",
-          "model_name": "DescriptionStyleModel",
           "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
           "state": {
             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
@@ -1063,10 +1066,10 @@
             "description_width": ""
           }
         },
-        "bf106e11f0a749b182fc3f0e2df5429d": {
+        "cd1abe7ae13e418c8e3e04c9b26dfe9d": {
           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -1115,26 +1118,32 @@
             "width": null
           }
         },
-        "4e02dd0f353047be90dbaf0fa2ecf0c3": {
+        "d7bc633f2ede464aacc1e72fcfa12f7a": {
           "model_module": "@jupyter-widgets/controls",
-          "model_name": "ProgressStyleModel",
           "model_module_version": "1.5.0",
+          "model_name": "HBoxModel",
           "state": {
+            "_dom_classes": [],
             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "_model_name": "ProgressStyleModel",
+            "_model_name": "HBoxModel",
             "_view_count": null,
-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
-            "bar_color": null,
-            "description_width": ""
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_9bbc7b3250bb413d87fd92481fbc124e",
+              "IPY_MODEL_9cd70a43ebb94a70acbba42eec2da49c",
+              "IPY_MODEL_b3d0bdd16c204efb821770b14e2cad90"
+            ],
+            "layout": "IPY_MODEL_22da358e24834033a9f58897fcb5c8bc"
           }
         },
-        "8872e1de590e49e7a648435eea7b4fe0": {
+        "df5aafeed72b4be9996671e9541ac56a": {
           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -1183,10 +1192,10 @@
             "width": null
           }
         },
-        "c1845d1d27864c1887ff7b4882d06a6e": {
+        "f3c854026aaa4a0489a2077af93ebb15": {
           "model_module": "@jupyter-widgets/controls",
-          "model_name": "DescriptionStyleModel",
           "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
           "state": {
             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
@@ -1199,17 +1208,8 @@
           }
         }
       }
-    },
-    "pycharm": {
-      "stem_cell": {
-        "cell_type": "raw",
-        "source": [],
-        "metadata": {
-          "collapsed": false
-        }
-      }
     }
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/src/adapters/__init__.py b/src/adapters/__init__.py
index bd78dee5de..a02be1d420 100644
--- a/src/adapters/__init__.py
+++ b/src/adapters/__init__.py
@@ -39,7 +39,7 @@
         "ADAPTERFUSION_CONFIG_MAP",
         "DEFAULT_ADAPTER_CONFIG",
         "DEFAULT_ADAPTERFUSION_CONFIG",
-        "AdapterConfigBase",
+        "AdapterConfig",
         "AdapterFusionConfig",
         "BnConfig",
         "CompacterConfig",
@@ -146,7 +146,7 @@
         ADAPTERFUSION_CONFIG_MAP,
         DEFAULT_ADAPTER_CONFIG,
         DEFAULT_ADAPTERFUSION_CONFIG,
-        AdapterConfigBase,
+        AdapterConfig,
         AdapterFusionConfig,
         BnConfig,
         CompacterConfig,
diff --git a/src/adapters/configuration/adapter_config.py b/src/adapters/configuration/adapter_config.py
index 0d72f08377..22b6c8101f 100644
--- a/src/adapters/configuration/adapter_config.py
+++ b/src/adapters/configuration/adapter_config.py
@@ -9,7 +9,7 @@
 logger = logging.getLogger(__name__)
 
 
-class AdapterConfigBase(Mapping):
+class AdapterConfig(Mapping):
     """
     Base class for all adaptation methods. This class does not define specific configuration keys, but only provides
     some common helper methods.
@@ -21,7 +21,7 @@ class AdapterConfigBase(Mapping):
     architecture: Optional[str] = None
 
     def __init__(self):
-        raise TypeError("AdapterConfigBase is an abstract class and cannot be instantiated.")
+        raise TypeError("AdapterConfig is an abstract class and cannot be instantiated.")
 
     # We want to emulate a simple form of immutability while keeping the ability to add custom attributes.
     # Therefore, we don't allow changing attribute values if set once.
@@ -57,7 +57,7 @@ def replace(self, **changes):
     @classmethod
     def from_dict(cls, config):
         """Creates a config class from a Python dict."""
-        if isinstance(config, AdapterConfigBase):
+        if isinstance(config, AdapterConfig):
             return config
 
         # the constructor does not accept additional kwargs, so add them separately
@@ -92,7 +92,7 @@ def _get_config_class(config_dict):
     @classmethod
     def load(cls, config: Union[dict, str], download_kwargs=None, **kwargs):
         """
-        Loads a given adapter configuration specifier into a full AdapterConfigBase instance.
+        Loads a given adapter configuration specifier into a full AdapterConfig instance.
 
         Args:
             config (Union[dict, str]): The configuration to load. Can be either:
@@ -117,7 +117,7 @@ def load(cls, config: Union[dict, str], download_kwargs=None, **kwargs):
         else:
             config_dict = resolve_adapter_config(config, local_map=local_map)
         # convert back to dict to allow attr overrides
-        if isinstance(config_dict, AdapterConfigBase):
+        if isinstance(config_dict, AdapterConfig):
             cls_new = config_dict.__class__
             config_dict = config_dict.to_dict()
         else:
@@ -128,7 +128,7 @@ def load(cls, config: Union[dict, str], download_kwargs=None, **kwargs):
 
 
 @dataclass(eq=False)
-class BnConfig(AdapterConfigBase):
+class BnConfig(AdapterConfig):
     """
     Base class that models the architecture of a bottleneck adapter.
 
@@ -359,7 +359,7 @@ class ParBnConfig(BnConfig):
 
 
 @dataclass(eq=False)
-class PrefixTuningConfig(AdapterConfigBase):
+class PrefixTuningConfig(AdapterConfig):
     """
     The Prefix Tuning architecture proposed by Li & Liang (2021). See https://arxiv.org/pdf/2101.00190.pdf.
 
@@ -396,7 +396,7 @@ class PrefixTuningConfig(AdapterConfigBase):
 
 
 @dataclass(eq=False)
-class LoRAConfig(AdapterConfigBase):
+class LoRAConfig(AdapterConfig):
     """
     The Low-Rank Adaptation (LoRA) architecture proposed by Hu et al. (2021). See https://arxiv.org/pdf/2106.09685.pdf.
     LoRA adapts a model by reparametrizing the weights of a layer matrix. You can merge the additional weights with the
@@ -462,7 +462,7 @@ class IA3Config(LoRAConfig):
     use_gating: bool = False
 
 
-class ConfigUnion(AdapterConfigBase):
+class ConfigUnion(AdapterConfig):
     """
     Composes multiple adaptation method configurations into one. This class can be used to define complex adaptation
     method setups.
@@ -470,9 +470,9 @@ class ConfigUnion(AdapterConfigBase):
 
     architecture: Optional[str] = "union"
 
-    configs: List[AdapterConfigBase]
+    configs: List[AdapterConfig]
 
-    def __init__(self, *configs: List[AdapterConfigBase]):
+    def __init__(self, *configs: List[AdapterConfig]):
         self.validate(configs)
         self.configs = configs
 
@@ -483,7 +483,7 @@ def validate(configs):
         setup.
 
         Args:
-            configs (List[AdapterConfigBase]): list of configs to check.
+            configs (List[AdapterConfig]): list of configs to check.
 
         Raises:
             TypeError: One of the configurations has a wrong type. ValueError: At least two given configurations
@@ -491,8 +491,8 @@ def validate(configs):
         """
         # perform single config checks
         for config in configs:
-            if not isinstance(config, AdapterConfigBase):
-                raise TypeError(f"{config} is not an instance of AdapterConfigBase")
+            if not isinstance(config, AdapterConfig):
+                raise TypeError(f"{config} is not an instance of AdapterConfig")
             elif isinstance(config, ConfigUnion):
                 raise TypeError(f"{config} of type {type(config)} is not supported in a config union.")
         # perform pairwise check
@@ -539,7 +539,7 @@ def replace(self, **changes):
 
     @classmethod
     def from_dict(cls, config):
-        if isinstance(config, AdapterConfigBase):
+        if isinstance(config, AdapterConfig):
             return config
 
         configs = []
diff --git a/src/adapters/configuration/adapter_fusion_config.py b/src/adapters/configuration/adapter_fusion_config.py
index afda2964c8..552bcdbe61 100644
--- a/src/adapters/configuration/adapter_fusion_config.py
+++ b/src/adapters/configuration/adapter_fusion_config.py
@@ -2,11 +2,11 @@
 from typing import Union
 
 from ..utils import resolve_adapter_config
-from .adapter_config import AdapterConfigBase
+from .adapter_config import AdapterConfig
 
 
 @dataclass(eq=False)
-class AdapterFusionConfig(AdapterConfigBase):
+class AdapterFusionConfig(AdapterConfig):
     """Base class that models the architecture of an adapter fusion layer."""
 
     key: bool
diff --git a/src/adapters/configuration/model_adapters_config.py b/src/adapters/configuration/model_adapters_config.py
index aeb89d493f..3f4c3023da 100644
--- a/src/adapters/configuration/model_adapters_config.py
+++ b/src/adapters/configuration/model_adapters_config.py
@@ -6,7 +6,7 @@
 from .. import __version__
 from ..composition import AdapterCompositionBlock
 from ..utils import get_adapter_config_hash
-from .adapter_config import ADAPTER_CONFIG_MAP, DEFAULT_ADAPTER_CONFIG, AdapterConfigBase, ConfigUnion
+from .adapter_config import ADAPTER_CONFIG_MAP, DEFAULT_ADAPTER_CONFIG, AdapterConfig, ConfigUnion
 from .adapter_fusion_config import ADAPTERFUSION_CONFIG_MAP, DEFAULT_ADAPTERFUSION_CONFIG
 
 
@@ -77,8 +77,8 @@ def match(
         config = self.get(adapter_name)
         if config is None:
             return None
-        elif not isinstance(config, AdapterConfigBase):
-            config = AdapterConfigBase.load(config)
+        elif not isinstance(config, AdapterConfig):
+            config = AdapterConfig.load(config)
 
         if isinstance(config, config_type):
             leave_out = config.get("leave_out", [])
@@ -125,7 +125,7 @@ def add(self, adapter_name: str, config: Optional[Union[str, dict]] = None):
         # if it's a dict, compute it's hash and add a new entry to the config map
         elif isinstance(config, Mapping):
             config_name = get_adapter_config_hash(config)
-            self.config_map[config_name] = AdapterConfigBase.load(config)
+            self.config_map[config_name] = AdapterConfig.load(config)
         else:
             raise ValueError("Invalid adapter config: {}".format(config))
         self.adapters[adapter_name] = config_name
@@ -207,14 +207,14 @@ def to_dict(self):
         output_dict["adapters"] = copy.deepcopy(self.adapters)
         output_dict["config_map"] = {}
         for k, v in self.config_map.items():
-            if isinstance(v, AdapterConfigBase):
+            if isinstance(v, AdapterConfig):
                 output_dict["config_map"][k] = v.to_dict()
             else:
                 output_dict["config_map"][k] = copy.deepcopy(v)
         output_dict["fusions"] = copy.deepcopy(self.fusions)
         output_dict["fusion_config_map"] = {}
         for k, v in self.fusion_config_map.items():
-            if isinstance(v, AdapterConfigBase):
+            if isinstance(v, AdapterConfig):
                 output_dict["fusion_config_map"][k] = v.to_dict()
             else:
                 output_dict["fusion_config_map"][k] = copy.deepcopy(v)
@@ -233,7 +233,7 @@ def build_full_config(adapter_config, model_config, save_id2label=False, **kwarg
     config_dict.update(kwargs)
     if not hasattr(model_config, "prediction_heads") and save_id2label:
         config_dict["label2id"] = model_config.label2id
-    if isinstance(adapter_config, AdapterConfigBase):
+    if isinstance(adapter_config, AdapterConfig):
         config_dict["config"] = adapter_config.to_dict()
     else:
         config_dict["config"] = adapter_config
diff --git a/src/adapters/loading.py b/src/adapters/loading.py
index 25ad7e4fc1..f189a120a8 100644
--- a/src/adapters/loading.py
+++ b/src/adapters/loading.py
@@ -7,7 +7,7 @@
 
 import torch
 
-from .configuration import AdapterConfigBase, build_full_config
+from .configuration import AdapterConfig, build_full_config
 from .head_utils import STATIC_TO_FLEX_HEAD_MAP, get_head_config_and_rename_list
 from .utils import (
     ACTIVATION_RENAME,
@@ -420,7 +420,7 @@ def load(
             Tuple[str, str]: A tuple consisting of the local file system directory from which the weights where loaded
             and the name of the loaded weights.
         """
-        requested_config = AdapterConfigBase.load(config) if config else None
+        requested_config = AdapterConfig.load(config) if config else None
         # Resolve the weights to be loaded based on the given identifier and the current adapter config
         model_name = self.model.model_name or model_name
         resolved_folder = resolve_adapter_path(
diff --git a/src/adapters/model_mixin.py b/src/adapters/model_mixin.py
index ca31958ded..078086ece8 100644
--- a/src/adapters/model_mixin.py
+++ b/src/adapters/model_mixin.py
@@ -13,7 +13,7 @@
 from transformers.modeling_outputs import ModelOutput
 
 from .composition import AdapterCompositionBlock, Fuse, Stack, parse_composition
-from .configuration import ADAPTER_CONFIG_MAP, AdapterConfigBase, AdapterFusionConfig, BnConfig
+from .configuration import ADAPTER_CONFIG_MAP, AdapterConfig, AdapterFusionConfig, BnConfig
 from .context import AdapterSetup, ForwardContext
 from .hub_mixin import PushAdapterToHubMixin
 from .loading import AdapterFusionLoader, AdapterLoader, PredictionHeadLoader, WeightsLoader
@@ -518,7 +518,7 @@ def add_adapter(self, adapter_name: str, config=None, overwrite_ok: bool = False
 
         Args:
             adapter_name (str): The name of the adapter module to be added.
-            config (str or dict or AdapterConfigBase, optional): The adapter configuration, can be either:
+            config (str or dict or AdapterConfig, optional): The adapter configuration, can be either:
 
                 - the string identifier of a pre-defined configuration dictionary
                 - a configuration dictionary specifying the full config
@@ -529,7 +529,7 @@ def add_adapter(self, adapter_name: str, config=None, overwrite_ok: bool = False
                 Set the adapter to be the active one. By default (False),
             the adapter is added but not activated.
         """
-        config = AdapterConfigBase.load(config)  # ensure config is ok and up-to-date
+        config = AdapterConfig.load(config)  # ensure config is ok and up-to-date
         # In case adapter already exists and we allow overwriting, explicitly delete the existing one first
         if overwrite_ok and adapter_name in self.adapters_config:
             self.delete_adapter(adapter_name)
diff --git a/src/adapters/training.py b/src/adapters/training.py
index b959dff101..8316011398 100644
--- a/src/adapters/training.py
+++ b/src/adapters/training.py
@@ -2,7 +2,7 @@
 from typing import Optional
 
 from .composition import Stack
-from .configuration import AdapterConfigBase
+from .configuration import AdapterConfig
 
 
 @dataclass
@@ -57,7 +57,7 @@ def setup_adapter_training(
     # Setup adapters
     if adapter_args.train_adapter:
         # resolve the adapter config
-        adapter_config = AdapterConfigBase.load(adapter_args.adapter_config, **adapter_config_kwargs)
+        adapter_config = AdapterConfig.load(adapter_args.adapter_config, **adapter_config_kwargs)
         # load a pre-trained from Hub if specified
         # note: this logic has changed in versions > 3.1.0: adapter is also loaded if it already exists
         if adapter_args.load_adapter:
@@ -73,7 +73,7 @@ def setup_adapter_training(
         # optionally load a pre-trained language adapter
         if adapter_args.load_lang_adapter:
             # resolve the language adapter config
-            lang_adapter_config = AdapterConfigBase.load(adapter_args.lang_adapter_config, **adapter_config_kwargs)
+            lang_adapter_config = AdapterConfig.load(adapter_args.lang_adapter_config, **adapter_config_kwargs)
             # load the language adapter from Hub
             lang_adapter_name = model.load_adapter(
                 adapter_args.load_lang_adapter,
diff --git a/tests_adapters/methods/test_compacter.py b/tests_adapters/methods/test_compacter.py
index 82c9acddbc..253b0fbf4f 100644
--- a/tests_adapters/methods/test_compacter.py
+++ b/tests_adapters/methods/test_compacter.py
@@ -30,7 +30,7 @@ def test_forward_compacter(self):
 
     def test_forward_shared_phm_compacter(self):
         model = self.get_model()
-        adapter_config = CompacterPlusPlusConfig(phm_dim=2, shared_W_phm=True, reduction_factor=8)
+        adapter_config = CompacterPlusPlusConfig(phm_dim=4, shared_W_phm=True, reduction_factor=4)
         self.run_forward_test(model, adapter_config)
 
     def test_load_compacter(self):
diff --git a/tests_adapters/test_adapter_config.py b/tests_adapters/test_adapter_config.py
index fe47c0c25b..5667213683 100644
--- a/tests_adapters/test_adapter_config.py
+++ b/tests_adapters/test_adapter_config.py
@@ -4,7 +4,7 @@
 
 from adapters import (
     ADAPTER_CONFIG_MAP,
-    AdapterConfigBase,
+    AdapterConfig,
     ConfigUnion,
     DoubleSeqBnConfig,
     LoRAConfig,
@@ -23,14 +23,12 @@ def test_config_load(self):
         # TODO still uses the old config names as only these are available on the Hub
         for config_name in ["pfeiffer", "houlsby"]:
             with self.subTest(config_name=config_name):
-                config = AdapterConfigBase.load(
-                    config_name, download_kwargs=download_kwargs, non_linearity="leakyrelu"
-                )
-                self.assertTrue(isinstance(config, AdapterConfigBase))
+                config = AdapterConfig.load(config_name, download_kwargs=download_kwargs, non_linearity="leakyrelu")
+                self.assertTrue(isinstance(config, AdapterConfig))
                 self.assertEqual(config.non_linearity, "leakyrelu")
 
     def test_config_immutable(self):
-        def set_attr(config: AdapterConfigBase):
+        def set_attr(config: AdapterConfig):
             config.non_linearity = "dummy"
             config.r = -1  # for LoRA
 
@@ -56,8 +54,8 @@ class CustomAdapterConfig(SeqBnConfig):
         config = CustomAdapterConfig()
         config_dict = config.to_dict()
         self.assertEqual(config_dict["custom_attr"], "test_value")
-        # When calling load on an AdapterConfigBase instance, don't change the class of the config.
-        config = AdapterConfigBase.load(config, custom_attr="test_value_2")
+        # When calling load on an AdapterConfig instance, don't change the class of the config.
+        config = AdapterConfig.load(config, custom_attr="test_value_2")
         self.assertTrue(isinstance(config, CustomAdapterConfig))
         self.assertEqual(config["custom_attr"], "test_value_2")
 
@@ -79,8 +77,8 @@ def test_config_union_valid(self):
                 config_new = ConfigUnion.from_dict(config_dict)
                 self.assertEqual(config, config_new)
 
-                self.assertIsInstance(config_new[0], AdapterConfigBase)
-                self.assertIsInstance(config_new[1], AdapterConfigBase)
+                self.assertIsInstance(config_new[0], AdapterConfig)
+                self.assertIsInstance(config_new[1], AdapterConfig)
 
     def test_config_union_invalid(self):
         unions = [
@@ -113,7 +111,7 @@ def test_config_string_valid(self):
         ]
         for config_str, config in to_test:
             with self.subTest(config_str=config_str):
-                config_new = AdapterConfigBase.load(config_str)
+                config_new = AdapterConfig.load(config_str)
                 self.assertEqual(config, config_new)
 
     def test_config_string_invalid(self):
@@ -125,4 +123,4 @@ def test_config_string_invalid(self):
         ]
         for config_str, error_type in to_test:
             with self.subTest(config_str=config_str):
-                self.assertRaises(error_type, AdapterConfigBase.load, config_str)
+                self.assertRaises(error_type, AdapterConfig.load, config_str)
diff --git a/tests_adapters/test_adapter_fusion_common.py b/tests_adapters/test_adapter_fusion_common.py
index 474752eb6e..4ee25fa06a 100644
--- a/tests_adapters/test_adapter_fusion_common.py
+++ b/tests_adapters/test_adapter_fusion_common.py
@@ -5,7 +5,7 @@
 
 import torch
 
-from adapters import ADAPTER_MODEL_MAPPING, ADAPTERFUSION_CONFIG_MAP, AdapterConfigBase, AutoAdapterModel, SeqBnConfig
+from adapters import ADAPTER_MODEL_MAPPING, ADAPTERFUSION_CONFIG_MAP, AdapterConfig, AutoAdapterModel, SeqBnConfig
 from adapters.composition import Fuse
 from adapters.utils import ADAPTERFUSION_WEIGHTS_NAME
 from adapters.wrappers import load_model
@@ -16,7 +16,7 @@
 class AdapterFusionModelTestMixin:
     def test_add_adapter_fusion(self):
         config_name = "seq_bn"
-        adapter_config = AdapterConfigBase.load(config_name)
+        adapter_config = AdapterConfig.load(config_name)
 
         for adater_fusion_config_name, adapter_fusion_config in ADAPTERFUSION_CONFIG_MAP.items():
             model = self.get_model()
diff --git a/tests_adapters/test_adapter_hub.py b/tests_adapters/test_adapter_hub.py
index cf07bd6e0a..28a47136aa 100644
--- a/tests_adapters/test_adapter_hub.py
+++ b/tests_adapters/test_adapter_hub.py
@@ -4,7 +4,7 @@
 import numpy as np
 
 import adapters
-from adapters import ADAPTER_CONFIG_MAP, AdapterConfigBase, BertAdapterModel, get_adapter_config_hash
+from adapters import ADAPTER_CONFIG_MAP, AdapterConfig, BertAdapterModel, get_adapter_config_hash
 from adapters.trainer import AdapterTrainer as Trainer
 from adapters.utils import find_in_index
 from tests.test_modeling_common import ids_tensor
@@ -65,7 +65,7 @@ def test_load_task_adapter_from_hub(self):
                 self.assertNotIn(adapter_name, model.base_model.invertible_adapters)
 
                 # check if config is valid
-                expected_hash = get_adapter_config_hash(AdapterConfigBase.load(config))
+                expected_hash = get_adapter_config_hash(AdapterConfig.load(config))
                 real_hash = get_adapter_config_hash(model.adapters_config.get(adapter_name))
                 self.assertEqual(expected_hash, real_hash)
 
@@ -115,7 +115,7 @@ def test_load_lang_adapter_from_hub(self):
             with self.subTest(config=config):
                 model = AutoModel.from_pretrained("bert-base-multilingual-cased")
                 adapters.init(model)
-                config = AdapterConfigBase.load(config, non_linearity="gelu", reduction_factor=2)
+                config = AdapterConfig.load(config, non_linearity="gelu", reduction_factor=2)
 
                 loading_info = {}
                 adapter_name = model.load_adapter(
@@ -154,7 +154,7 @@ def test_load_adapter_with_head_from_hub(self):
 
         self.assertIn(adapter_name, model.adapters_config.adapters)
         # check if config is valid
-        expected_hash = get_adapter_config_hash(AdapterConfigBase.load("houlsby"))
+        expected_hash = get_adapter_config_hash(AdapterConfig.load("houlsby"))
         real_hash = get_adapter_config_hash(model.adapters_config.get(adapter_name))
         self.assertEqual(expected_hash, real_hash)