Librarian Bot: Add base_model information to model

This pull request aims to enrich the metadata of your model by adding [`facebook/wav2vec2-large-xlsr-53`](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) 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

@@ -1,30 +1,31 @@
 ---
 language: ja
+license: apache-2.0
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
 datasets:
-  - common_voice
+- common_voice
 metrics:
-  - wer
-  - cer
-tags:
-  - audio
-  - automatic-speech-recognition
-  - speech
-  - xlsr-fine-tuning-week
-license: apache-2.0
+- wer
+- cer
+base_model: facebook/wav2vec2-large-xlsr-53
 model-index:
-  - name: Japanese XLSR Wav2Vec2 Large 53
-    results:
-      - task:
-          name: Speech Recognition
-          type: automatic-speech-recognition
-        dataset:
-          name: Common Voice ja
-          type: common_voice
-          args: ja
-        metrics:
-          - name: Test WER
-            type: wer
-            value: { wer_result_on_test } #TODO (IMPORTANT): replace {wer_result_on_test} with the WER error rate you achieved on the common_voice test set. It should be in the format XX.XX (don't add the % sign here). **Please** remember to fill out this value after you evaluated your model, so that your model appears on the leaderboard. If you fill out this model card before evaluating your model, please remember to edit the model card afterward to fill in your value
+- name: Japanese XLSR Wav2Vec2 Large 53
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Speech Recognition
+    dataset:
+      name: Common Voice ja
+      type: common_voice
+      args: ja
+    metrics:
+    - type: wer
+      value: {}
+      name: Test WER
 ---
 
 # Wav2Vec2-Large-XLSR-53-{language} #TODO: replace language with your {language}, _e.g._ French