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Librarian Bot: Add base_model information to model

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This pull request aims to enrich the metadata of your model by adding [`google/vit-base-patch16-224-in21k`](https://huggingface.co/google/vit-base-patch16-224-in21k) 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)!

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  1. README.md +4 -3
README.md CHANGED
@@ -7,20 +7,21 @@ datasets:
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  - imagefolder
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  metrics:
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  - accuracy
 
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  model-index:
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  - name: vit-base-mri
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  results:
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  - task:
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- name: Image Classification
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  type: image-classification
 
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  dataset:
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  name: mriDataSet
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  type: imagefolder
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  args: default
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  metrics:
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- - name: Accuracy
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- type: accuracy
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  value: 0.9827025893699549
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  - imagefolder
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  metrics:
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  - accuracy
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+ base_model: google/vit-base-patch16-224-in21k
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  model-index:
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  - name: vit-base-mri
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  results:
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  - task:
 
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  type: image-classification
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+ name: Image Classification
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  dataset:
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  name: mriDataSet
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  type: imagefolder
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  args: default
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  metrics:
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+ - type: accuracy
 
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  value: 0.9827025893699549
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+ name: Accuracy
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You