diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..485dee6
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.idea
diff --git a/README.md b/README.md
index 72ca71e..d312126 100644
--- a/README.md
+++ b/README.md
@@ -8,10 +8,11 @@ To install the package add the package path to the `packages.yml` file in your d
 
 In order to use the model audit post-hook the following variables have to be set in your `dbt_project.yml` file.
 
-| Variable              | Description                |
-| --------------------- | -------------------------- |
-| `dbt_ml:audit_schema` | Schema of the audit table. |
-| `dbt_ml:audit_table`  | Name of the audit table.   |
+| Variable               | Description                       |
+| ---------------------- | --------------------------------- |
+| `dbt_ml:audit_database`| Name of the GCP Project to use.   |
+| `dbt_ml:audit_schema`  | Schema of the audit table.        |
+| `dbt_ml:audit_table`   | Name of the audit table.          |
 
 You will also need to specify the post-hook in your `dbt_project.yml` file<sup>[1]</sup> as `{{ dbt_ml.model_audit() }}`. Optionally, you can use the `dbt_ml.create_model_audit_table()` macro to create the audit table automatically if it does not exist - for example in an on-run-start hook.
 
@@ -20,6 +21,7 @@ Example config for `dbt_project.yml` below:
 vars:
   "dbt_ml:audit_schema": "audit"
   "dbt_ml:audit_table": "ml_models"
+  "dbt_ml:audit_database": "database"
 on-run-start:
   - '{% do adapter.create_schema(api.Relation.create(target.project, "audit")) %}'
   - "{{ dbt_ml.create_model_audit_table() }}"
diff --git a/macros/hooks/model_audit.sql b/macros/hooks/model_audit.sql
index 74b4b36..7065fb4 100644
--- a/macros/hooks/model_audit.sql
+++ b/macros/hooks/model_audit.sql
@@ -5,7 +5,7 @@
     'schema': 'string',
     'created_at': type_timestamp(),
     'training_info': 'array<struct<training_run int64, iteration int64, loss float64, eval_loss float64, learning_rate float64, duration_ms int64, cluster_info array<struct<centroid_id int64, cluster_radius float64, cluster_size int64>>>>',
-    'feature_info': 'array<struct<input string, min float64, max float64, mean float64, median float64, stddev float64, category_count int64, null_count int64>>',
+    'feature_info': 'array<struct<input string, min float64, max float64, mean float64, median float64, stddev float64, category_count int64, null_count int64, dimension int64>>',
     'weights': 'array<struct<processed_input string, weight float64, category_weights array<struct<category string, weight float64>>>>',
     'evaluate': 'array<struct<precision	float64, recall	float64, accuracy float64, f1_score float64, log_loss float64, roc_auc float64>>',
 }) %}
@@ -100,7 +100,7 @@ tensorflow: {}
 
     {% set info_types = ['training_info', 'feature_info', 'weights', 'evaluate'] %}
 
-    insert `{{ target.database }}.{{ var('dbt_ml:audit_schema') }}.{{ var('dbt_ml:audit_table') }}`
+    insert `{{ var('dbt_ml:audit_database') }}.{{ var('dbt_ml:audit_schema') }}.{{ var('dbt_ml:audit_table') }}`
     (model, schema, created_at, {{ info_types | join(', ') }})
 
     select
@@ -125,7 +125,7 @@ tensorflow: {}
 {% macro create_model_audit_table() %}
     {%- set audit_table =
         api.Relation.create(
-            database=target.database,
+            database=var('dbt_ml:audit_database'),
             schema=var('dbt_ml:audit_schema'),
             identifier=var('dbt_ml:audit_table'),
             type='table'
diff --git a/macros/materializations/model.sql b/macros/materializations/model.sql
index 529692e..354a50d 100644
--- a/macros/materializations/model.sql
+++ b/macros/materializations/model.sql
@@ -19,18 +19,11 @@
 {% endmacro %}
 
 {% macro model_options(ml_config, labels) %}
-    {%- if labels -%}
-        {%- set label_list = [] -%}
-        {%- for label, value in labels.items() -%}
-            {%- do label_list.append((label, value)) -%}
-        {%- endfor -%}
-        {%- do ml_config.update({'labels': label_list}) -%}
-    {%- endif -%}
 
     {% set options -%}
         options(
             {%- for opt_key, opt_val in ml_config.items() -%}
-                {%- if opt_val is sequence and not (opt_val | first) is number and (opt_val | first).startswith('hparam_') -%}
+                {%- if opt_val is sequence and (opt_val | first) is string and (opt_val | first).startswith('hparam_') -%}
                     {{ opt_key }}={{ opt_val[0] }}({{ opt_val[1:] | join(', ') }})
                 {%- else -%}
                     {{ opt_key }}={{ (opt_val | tojson) if opt_val is string else opt_val }}