Add pipeline tag, library name and update license

This PR improves the model card by:
- Adding the `pipeline_tag: time-series-forecasting` to better categorize the model on the Hub.
- Adding `library_name: pytorch-lightning` to enable the automated "how to use" widget, as the model uses `pytorch_lightning` for loading.
- Updating the license in the metadata to `mit` to reflect the license specified in the GitHub repository.

README.md

@@ -1,8 +1,11 @@
 ---
-license: cc-by-nc-3.0
 datasets:
 - BGLab/FlowBench
+license: mit
+pipeline_tag: time-series-forecasting
+library_name: pytorch-lightning
 ---
+
 # Time-Dependent DeepONet for FlowBench (FPO)
 
 This repository hosts pre-trained **time-dependent DeepONet** checkpoints used in the paper:
@@ -33,8 +36,6 @@ All checkpoints are stored under the `checkpoints/` directory:
 
 Each checkpoint contains the weights for the time-dependent DeepONet used in the paper. For the exact architecture, data preprocessing, and training details, please refer to the GitHub repository.
 
-
-
 You can download any checkpoint using `huggingface_hub`:
 
 ```python