better save and test-eval

src/tasknet/models.py

@@ -271,8 +271,8 @@ class default:
             task: dataset["test"]
             for task, dataset in self.processed_tasks.items()
         }
-        # We revents trainer from automatically evaluating on each dataset:
-        # transformerS.Trainer recognizes eval_dataset instances of "dict"
+        # We preventstrainer from automatically evaluating on each dataset:
+        # transformers.Trainer recognizes eval_dataset instances of "dict"
         # But we use a custom "evaluate" function so that we can use different metrics for each task
         self.eval_dataset = MappingProxyType(self.eval_dataset)
         self.fix_callback()
@@ -308,7 +308,7 @@ def write_line(other, values):
             other.inner_table[0] = columns
             other.inner_table.append([values.get(c, np.nan) for c in columns])
 
-    def evaluate(self, **kwargs):
+    def evaluate(self,metric_key_prefix="eval", **kwargs):
         try:
             i=[i for (i,c) in enumerate(self.callback_handler.callbacks) if 'NotebookProgress' in str(c)][0]
             self.callback_handler.callbacks[i].training_tracker.write_line = fc.partial(
@@ -321,12 +321,13 @@ def evaluate(self, **kwargs):
             self.compute_metrics = task.compute_metrics
             output = transformers.Trainer.evaluate(
                 self,
-                eval_dataset=dict([fc.nth(i, self.eval_dataset.items())]),
+                eval_dataset=dict([fc.nth(i, (self.eval_dataset if metric_key_prefix=="eval" else self.test_dataset).items())]),
+                metric_key_prefix=metric_key_prefix
             )
             if "Accuracy" not in output:
                 output["Accuracy"] = np.nan
             outputs += [output]
-        return fc.join(outputs)
+        return fc.join(outputs) if metric_key_prefix!="test" else outputs
 
     def task_batch_size(self,task_name):
         if hasattr(task_name, 'num_choices'):
@@ -391,6 +392,9 @@ def get_test_dataloader(self, test_dataset=None):
             }
         )
 
+    def save_model(self,output_dir,**kwargs):
+        self.model.factorize().save_pretrained(output_dir)
+
     def preprocess_tasks(self, tasks, tokenizer):
 
         features_dict = {}