Librarian Bot: Add base_model information to model

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

@@ -1,19 +1,20 @@
 ---
+language:
+- en
 license: mit
+library_name: transformers
 tags:
 - generated_from_trainer
-model-index:
-- name: biomedical_question_answering
-  results: []
 datasets:
 - Shushant/BiomedicalQuestionAnsweringDataset
-language:
-- en
 metrics:
 - exact_match
 - f1
-library_name: transformers
 pipeline_tag: question-answering
+base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
+model-index:
+- name: biomedical_question_answering
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You