How to understand Learning Curves

I’ve developed two models and wanted to test them. However, I don’t know how to intepret the results of the learning curves properly.

For Model 1:

                 precision    recall  f1-score   support
      class 1       0.82      0.94      0.88      1717
      class 2       0.97      0.90      0.93      3504

    accuracy                           0.91      5221

For Model 2:

                precision    recall  f1-score   support

      class 1       0.27      0.98      0.43       115
      class 2       1.00      0.77      0.87      1317

    accuracy                           0.79      1432

For model 2, I’ve checked that I’ve done the code right but the results don’t seem to match up with the learning curve. Could someone please explain what’s happening in each case?

Thanks

Some extra info:

• Linear SVC used
• There is a data imbalance but I set the class weight to balanced
• Dataset relates to a worklog (natural language processing)