Improve model card: Add pipeline tag, language, and descriptive tags, and paper link

This PR improves the model card for `EQUES/jpharma-bert-large` by:

- Adding `pipeline_tag: fill-mask` to the metadata, which ensures the model is properly categorized and enables the correct interactive widget on the Hugging Face Hub (e.g., at https://huggingface.co/models?pipeline_tag=fill-mask). This aligns with the provided sample usage (`pipeline("fill-mask", ...)`).
- Specifying `language: [ja, en]` as the model was continually pre-trained on both Japanese and English pharmaceutical/biomedical tokens, as stated in the paper abstract.
- Including relevant `tags` such as `bert`, `japanese`, `pharmaceutical`, and `biomedical` for better discoverability and categorization.
- Adding a prominent link to the paper [A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP](https://huggingface.co/papers/2505.16661) at the top of the model card.
- Correcting the typo "Examoke Usage" to "Example Usage".

README.md

@@ -1,14 +1,24 @@
 ---
 library_name: transformers
-tags: []
+pipeline_tag: fill-mask
+language:
+  - ja
+  - en
+tags:
+  - bert
+  - japanese
+  - pharmaceutical
+  - biomedical
 ---
 
+Paper: [A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP](https://huggingface.co/papers/2505.16661)
+
 # Model Card
 
 <!-- Provide a quick summary of what the model is/does. -->
 Our **JpharmaBERT (large)** is a continually pre-trained version of the BERT model ([tohoku-nlp/bert-large-japanese-v2](https://huggingface.co/tohoku-nlp/bert-large-japanese-v2)), further trained on pharmaceutical data — the same dataset used for [eques/jpharmatron](https://huggingface.co/EQUES/JPharmatron-7B).
 
-# Examoke Usage
+# Example Usage
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ```python