Librarian Bot: Add base_model information to model

This pull request aims to enrich the metadata of your model by adding [`Danielwei0214/bert-base-chinese-finetuned-ner`](https://huggingface.co/Danielwei0214/bert-base-chinese-finetuned-ner) 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).

If you want to automatically add `base_model` metadata to more of your modes you can use the [Librarian Bot](https://huggingface.co/librarian-bot) [Metadata Request Service](https://huggingface.co/spaces/librarian-bots/metadata_request_service)!

README.md

@@ -10,31 +10,32 @@ metrics:
 - recall
 - f1
 - accuracy
+base_model: Danielwei0214/bert-base-chinese-finetuned-ner
 model-index:
 - name: bert-base-chinese-finetuned-ner
   results:
   - task:
-      name: Token Classification
       type: token-classification
+      name: Token Classification
     dataset:
-        name: gyr66/privacy_detection
-        type: gyr66/privacy_detection
-        config: privacy_detection
-        split: train
-        args: privacy_detection
+      name: gyr66/privacy_detection
+      type: gyr66/privacy_detection
+      config: privacy_detection
+      split: train
+      args: privacy_detection
     metrics:
-    - name: Precision
-      type: precision
+    - type: precision
       value: 0.65322
-    - name: Recall
-      type: recall
+      name: Precision
+    - type: recall
       value: 0.74169
-    - name: F1
-      type: f1
+      name: Recall
+    - type: f1
       value: 0.69465
-    - name: Accuracy
-      type: accuracy
+      name: F1
+    - type: accuracy
       value: 0.90517
+      name: Accuracy
 ---
 
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