Librarian Bot: Add base_model information to model

This pull request aims to enrich the metadata of your model by adding [`dbmdz/bert-base-italian-cased`](https://huggingface.co/dbmdz/bert-base-italian-cased) 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

@@ -1,4 +1,6 @@
 ---
+language:
+- it
 license: mit
 tags:
 - generated_from_trainer
@@ -9,12 +11,18 @@ metrics:
 - recall
 - f1
 - accuracy
+widget:
+- text: 'Ciao, sono Giacomo. Vivo a Milano e lavoro da Armani. '
+  example_title: Example 1
+- text: 'Domenica andrò allo stadio con Giovanna a guardare la Fiorentina. '
+  example_title: Example 2
+base_model: dbmdz/bert-base-italian-cased
 model-index:
 - name: bert-italian-finetuned-ner
   results:
   - task:
-      name: Token Classification
       type: token-classification
+      name: Token Classification
     dataset:
       name: wiki_neural
       type: wiki_neural
@@ -22,25 +30,18 @@ model-index:
       split: validation
       args: it
     metrics:
-    - name: Precision
-      type: precision
+    - type: precision
       value: 0.9438064759036144
-    - name: Recall
-      type: recall
+      name: Precision
+    - type: recall
       value: 0.954225352112676
-    - name: F1
-      type: f1
+      name: Recall
+    - type: f1
       value: 0.9489873178118493
-    - name: Accuracy
-      type: accuracy
+      name: F1
+    - type: accuracy
       value: 0.9917883014379933
-widget:
-- text: 'Ciao, sono Giacomo. Vivo a Milano e lavoro da Armani. '
-  example_title: Example 1
-- text: 'Domenica andrò allo stadio con Giovanna a guardare la Fiorentina. '
-  example_title: Example 2
-language:
-- it
+      name: Accuracy
 ---
 
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