add saving and loading model in examples

examples/run_classifier.py

@@ -546,6 +546,15 @@ def main():
                     optimizer.zero_grad()
                     global_step += 1
 
+    # Save a trained model
+    model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+    output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
+    torch.save(model_to_save.state_dict(), output_model_file)
+
+    # Load a trained model that you have fine-tuned
+    model_state_dict = torch.load(output_model_file)
+    model = BertForSequenceClassification.from_pretrained(args.bert_model, state_dict=model_state_dict)
+
     if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         eval_examples = processor.get_dev_examples(args.data_dir)
         eval_features = convert_examples_to_features(
@@ -593,10 +602,6 @@ def main():
                   'global_step': global_step,
                   'loss': tr_loss/nb_tr_steps}
 
-        model_to_save = model.module if hasattr(model, 'module') else model
-        raise NotImplementedError # TODO add save of the configuration file and vocabulary file also ?
-        output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
-        torch.save(model_to_save, output_model_file)
         output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
         with open(output_eval_file, "w") as writer:
             logger.info("***** Eval results *****")

examples/run_squad.py

@@ -911,6 +911,15 @@ def main():
                     optimizer.zero_grad()
                     global_step += 1
 
+    # Save a trained model
+    model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+    output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
+    torch.save(model_to_save.state_dict(), output_model_file)
+
+    # Load a trained model that you have fine-tuned
+    model_state_dict = torch.load(output_model_file)
+    model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict)
+
     if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         eval_examples = read_squad_examples(
             input_file=args.predict_file, is_training=False)