Librarian Bot: Add base_model information to model

This pull request aims to enrich the metadata of your model by adding [`microsoft/swin-tiny-patch4-window7-224`](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) 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

@@ -5,6 +5,8 @@ tags:
 datasets:
 - image_folder
 - nielsr/eurosat-demo
+metrics:
+- accuracy
 widget:
 - src: https://drive.google.com/uc?id=1trKgvkMRQ3BB0VcqnDwmieLxXhWmS8rq
   example_title: Annual Crop
@@ -26,22 +28,21 @@ widget:
   example_title: River
 - src: https://drive.google.com/uc?id=1zVAfR7N5hXy6eq1cVOd8bXPjC1sqxVir
   example_title: Sea Lake
-metrics:
-- accuracy
+base_model: microsoft/swin-tiny-patch4-window7-224
 model-index:
 - name: swin-tiny-patch4-window7-224-finetuned-eurosat
   results:
   - task:
+      type: image-classification
       name: Image Classification
-      type: image-classification      
     dataset:
       name: image_folder
       type: image_folder
       args: default
     metrics:
-    - name: Accuracy
-      type: accuracy
+    - type: accuracy
       value: 0.9848148148148148
+      name: Accuracy
 ---
 
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