run_tf_glue MRPC evaluation only for MRPC

examples/run_tf_glue.py

@@ -71,20 +71,21 @@ history = model.fit(train_dataset, epochs=EPOCHS, steps_per_epoch=train_steps,
 os.makedirs('./save/', exist_ok=True)
 model.save_pretrained('./save/')
 
-# Load the TensorFlow model in PyTorch for inspection
-pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
-
-# Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
-sentence_0 = 'This research was consistent with his findings.'
-sentence_1 = 'His findings were compatible with this research.'
-sentence_2 = 'His findings were not compatible with this research.'
-inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
-inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
-
-del inputs_1["special_tokens_mask"]
-del inputs_2["special_tokens_mask"]
-
-pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
-pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
-print('sentence_1 is', 'a paraphrase' if pred_1 else 'not a paraphrase', 'of sentence_0')
-print('sentence_2 is', 'a paraphrase' if pred_2 else 'not a paraphrase', 'of sentence_0')
+if TASK == "mrpc":
+    # Load the TensorFlow model in PyTorch for inspection
+    pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
+
+    # Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
+    sentence_0 = 'This research was consistent with his findings.'
+    sentence_1 = 'His findings were compatible with this research.'
+    sentence_2 = 'His findings were not compatible with this research.'
+    inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
+    inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
+
+    del inputs_1["special_tokens_mask"]
+    del inputs_2["special_tokens_mask"]
+
+    pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
+    pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
+    print('sentence_1 is', 'a paraphrase' if pred_1 else 'not a paraphrase', 'of sentence_0')
+    print('sentence_2 is', 'a paraphrase' if pred_2 else 'not a paraphrase', 'of sentence_0')