fix loading from tf/pt

examples/run_tf_glue.py

@@ -27,7 +27,8 @@ tf_model.save_pretrained('./runs/')
 pt_model = BertForSequenceClassification.from_pretrained('./runs/')
 
 # Quickly inspect a few predictions
-
+inputs = tokenizer.encode_plus("I said the company is doing great", "The company has good results", add_special_tokens=True)
+pred = pt_model(torch.tensor([tokens]))
 
 # Divers
 import torch

pytorch_transformers/modeling_tf_utils.py

@@ -224,8 +224,8 @@ class TFPreTrainedModel(tf.keras.Model):
                     # Load from a PyTorch checkpoint
                     archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
                 else:
-                    raise EnvironmentError("Error no file named {} found in directory {}".format(
-                        tuple(WEIGHTS_NAME, TF2_WEIGHTS_NAME),
+                    raise EnvironmentError("Error no file named {} found in directory {} or `from_pt` set to False".format(
+                        [WEIGHTS_NAME, TF2_WEIGHTS_NAME],
                         pretrained_model_name_or_path))
             elif os.path.isfile(pretrained_model_name_or_path):
                 archive_file = pretrained_model_name_or_path

pytorch_transformers/modeling_utils.py

@@ -304,7 +304,7 @@ class PreTrainedModel(nn.Module):
                     # Load from a PyTorch checkpoint
                     archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
                 else:
-                    raise EnvironmentError("Error no file named {} found in directory {}".format(
+                    raise EnvironmentError("Error no file named {} found in directory {} or `from_tf` set to False".format(
                         [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"],
                         pretrained_model_name_or_path))
             elif os.path.isfile(pretrained_model_name_or_path):