Automatic "Model Cards" for Flair models, and default ability to resume training

flair/models/sequence_tagger_model.py

@@ -254,14 +254,9 @@ def _init_model_with_state_dict(state):
 
         rnn_type = "LSTM" if "rnn_type" not in state.keys() else state["rnn_type"]
         use_dropout = 0.0 if "use_dropout" not in state.keys() else state["use_dropout"]
-        use_word_dropout = (
-            0.0 if "use_word_dropout" not in state.keys() else state["use_word_dropout"]
-        )
-        use_locked_dropout = (
-            0.0
-            if "use_locked_dropout" not in state.keys()
-            else state["use_locked_dropout"]
-        )
+        use_word_dropout = 0.0 if "use_word_dropout" not in state.keys() else state["use_word_dropout"]
+        use_locked_dropout = 0.0 if "use_locked_dropout" not in state.keys() else state["use_locked_dropout"]
+
         train_initial_hidden_state = (
             False
             if "train_initial_hidden_state" not in state.keys()

flair/nn/model.py

@@ -1,4 +1,3 @@
-import copy
 import itertools
 import logging
 import warnings
@@ -82,8 +81,37 @@ def save(self, model_file: Union[str, Path]):
         """
         model_state = self._get_state_dict()
 
+        # in Flair <0.9.1, optimizer and scheduler used to train model are not saved
+        optimizer = scheduler = None
+
+        # write out a "model card" if one is set
+        if hasattr(self, 'model_card'):
+
+            # special handling for optimizer: remember optimizer class and state dictionary
+            if 'training_parameters' in self.model_card:
+                training_parameters = self.model_card['training_parameters']
+
+                if 'optimizer' in training_parameters:
+                    optimizer = training_parameters['optimizer']
+                    training_parameters['optimizer_state_dict'] = optimizer.state_dict()
+                    training_parameters['optimizer'] = optimizer.__class__
+
+                if 'scheduler' in training_parameters:
+                    scheduler = training_parameters['scheduler']
+                    training_parameters['scheduler_state_dict'] = scheduler.state_dict()
+                    training_parameters['scheduler'] = scheduler.__class__
+
+            model_state['model_card'] = self.model_card
+
+        # save model
         torch.save(model_state, str(model_file), pickle_protocol=4)
 
+        # restore optimizer and scheduler to model card if set
+        if optimizer:
+            self.model_card['training_parameters']['optimizer'] = optimizer
+        if scheduler:
+            self.model_card['training_parameters']['scheduler'] = scheduler
+
     @classmethod
     def load(cls, model: Union[str, Path]):
         """
@@ -102,11 +130,38 @@ def load(cls, model: Union[str, Path]):
 
         model = cls._init_model_with_state_dict(state)
 
+        if 'model_card' in state:
+            model.model_card = state['model_card']
+
         model.eval()
         model.to(flair.device)
 
         return model
 
+    def print_model_card(self):
+        if hasattr(self, 'model_card'):
+            param_out = "\n------------------------------------\n"
+            param_out += "--------- Flair Model Card ---------\n"
+            param_out += "------------------------------------\n"
+            param_out += "- this Flair model was trained with:\n"
+            param_out += f"-- Flair version {self.model_card['flair_version']}\n"
+            param_out += f"-- PyTorch version {self.model_card['pytorch_version']}\n"
+            if 'transformers_version' in self.model_card:
+                param_out += f"-- Transformers version {self.model_card['transformers_version']}\n"
+            param_out += "------------------------------------\n"
+
+            param_out += "------- Training Parameters: -------\n"
+            param_out += "------------------------------------\n"
+            training_params = '\n'.join(f'-- {param} = {self.model_card["training_parameters"][param]}'
+                                        for param in self.model_card['training_parameters'])
+            param_out += training_params + "\n"
+            param_out += "------------------------------------\n"
+
+            log.info(param_out)
+        else:
+            log.info(
+                "This model has no model card (likely because it is not yet trained or was trained with Flair version < 0.9.1)")
+
 
 class Classifier(Model):
     """Abstract base class for all Flair models that do classification, both single- and multi-label.
@@ -175,7 +230,7 @@ def evaluate(
 
                     for gold_label in datapoint.get_labels(gold_label_type):
                         representation = str(sentence_id) + ': ' + gold_label.identifier
-                        
+
                         value = gold_label.value
                         if gold_label_dictionary and gold_label_dictionary.get_idx_for_item(value) == 0:
                             value = '<unk>'