Librarian Bot: Add base_model information to model

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

@@ -1,28 +1,29 @@
 ---
-license: apache-2.0
 language: en
+license: apache-2.0
 tags:
 - generated_from_keras_callback
-model-index:
-- name: pull_request_comments_model
-  results: []
 widget:
-- text: >-
-    Please add a detailed comment explaining what this config parameter means.
+- text: Please add a detailed comment explaining what this config parameter means.
   example_title: Code example
-- text: >-
-    Because if non-chief workers don’t restore the model from the checkpoint, training can‘t continue. 
-    For example, I am using tf.estimator.Estimator API and tf.distribute.CollectiveAllReduceStrategy for distributed training.
-    Firstly, I train for 1000 rounds(train_stpes=1000) and save the checkpoint, it works normally.
-    Then I set the train_steps to 2000, only the is_chief role can restore the model from the checkpoint without any error.
+- text: Because if non-chief workers don’t restore the model from the checkpoint,
+    training can‘t continue.  For example, I am using tf.estimator.Estimator API and
+    tf.distribute.CollectiveAllReduceStrategy for distributed training. Firstly, I
+    train for 1000 rounds(train_stpes=1000) and save the checkpoint, it works normally.
+    Then I set the train_steps to 2000, only the is_chief role can restore the model
+    from the checkpoint without any error.
   example_title: ML example
-- text: >-
-    Hi, Could you please review this PR when you have some time? Thank you very much!
+- text: Hi, Could you please review this PR when you have some time? Thank you very
+    much!
   example_title: Management example
-- text: >-
-    Sorry, i was on holiday all the month and i didn’t have so much time to review this PR. I also had a lot of problems with my computer, so i couldn’t work...
+- text: Sorry, i was on holiday all the month and i didn’t have so much time to review
+    this PR. I also had a lot of problems with my computer, so i couldn’t work...
   example_title: Other example
 pipeline_tag: text-classification
+base_model: distilbert-base-uncased
+model-index:
+- name: pull_request_comments_model
+  results: []
 ---
 
 # pull_request_comments_model