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I have trained a classifier for sentiment analysis using BERT architecture. I am able to train the classifier and I am getting a validation accuracy of 87%. But whenever I feed in test data, or some simple sentence like "What an amazing movie", "I would love that book", etc, the model predicts the class for one and says list index out of range for other. I tried to find if there is a bug in my code as well.

(x_train, y_train), (x_test, y_test), preproc = text.texts_from_csv(TWITTER,
                                                                       preprocess_mode='bert',                                                                    
                                                                       text_column = 'text', label_columns = ['target'])

model = text.text_classifier('bert', (x_train, y_train), preproc=preproc)
learner = ktrain.get_learner(model,train_data=(x_train, y_train), val_data=(x_test, y_test), batch_size=6)

learner.fit_onecycle(2e-5, 1)

predictor = ktrain.get_predictor(learner.model,preproc)

predictor.predict(['I am very happy to meet you!'])

Error that I am getting

IndexError                                Traceback (most recent call last)
<ipython-input-28-82b8da552189> in <module>()
----> 1 predictor.predict(['I am very happy to meet you!'])

1 frames
/usr/local/lib/python3.6/dist-packages/ktrain/text/predictor.py in <listcomp>(.0)
     56                 preds = np.squeeze(preds)
     57                 if len(preds.shape) == 0: preds = np.expand_dims(preds, -1)
---> 58         result =  preds if return_proba or multilabel or not self.c else [self.c[np.argmax(pred)] for pred in preds]
     59         if multilabel and not return_proba:
     60             result =  [list(zip(self.c, r)) for r in result]

IndexError: list index out of range

My problem is that if I have any bugs in my code, then I should get this error every time I execute the predict method. Say I have 100 new test points, I am getting this error only for 30 to 40 test points and the remaining are classified properly. I tested this theory by feeding in tweets one tweet at a time. But I do not understand why is this happening.

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  • $\begingroup$ Try adding the following before calling 'predict': predictor.get_classes() $\endgroup$ – Brian O'Donnell May 14 at 16:53
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try without list operator []:

predictor.predict('I am very happy to meet you!')

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  • $\begingroup$ Please explain the issue and why this will work $\endgroup$ – 10xAI Jul 6 at 6:39
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OP evidently figured out the issue himself and posted solution here.

Basically, the class label integers in his dataset for a binary classification problem appeared as [0,4], instead of [0,1]. If class labels are integers, they must be consecutive values(e.g, [0,1] for two labels, [0,1,2,3] for four labels).

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