Librarian Bot: Add base_model information to model

This pull request aims to enrich the metadata of your model by adding [`deepset/gbert-base`](https://huggingface.co/deepset/gbert-base) as a `base_model` field, situated in the `YAML` block of your model's `README.md`.

How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection.

**Why add this?** Enhancing your model's metadata in this way:
- **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- **Highlights Impact** - It showcases the contributions and influences different models have within the community.

For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer).

This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, queries, or need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien). Your input is invaluable to us!

README.md

@@ -2,9 +2,6 @@
 language:
 - de
 license: mit
-widget:
- - text: |
-     Philipp ist 26 Jahre alt und lebt in Nürnberg, Deutschland. Derzeit arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face, um künstliche Intelligenz durch Open Source und Open Science zu demokratisieren.
 datasets:
 - germaner
 metrics:
@@ -12,29 +9,36 @@ metrics:
 - recall
 - f1
 - accuracy
+widget:
+- text: 'Philipp ist 26 Jahre alt und lebt in Nürnberg, Deutschland. Derzeit arbeitet
+    er als Machine Learning Engineer und Tech Lead bei Hugging Face, um künstliche
+    Intelligenz durch Open Source und Open Science zu demokratisieren.
+
+    '
+base_model: deepset/gbert-base
 model-index:
 - name: gbert-base-germaner
   results:
   - task:
-      name: Token Classification
       type: token-classification
+      name: Token Classification
     dataset:
       name: germaner
       type: germaner
       args: default
     metrics:
-    - name: precision
-      type: precision
+    - type: precision
       value: 0.8520523797532108
-    - name: recall
-      type: recall
+      name: precision
+    - type: recall
       value: 0.8754204398447607
-    - name: f1
-      type: f1
+      name: recall
+    - type: f1
       value: 0.8635783563042368
-    - name: accuracy
-      type: accuracy
+      name: f1
+    - type: accuracy
       value: 0.976147969774973
+      name: accuracy
 ---
 
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