BERT model bug encountered during training

Question

So, I made a custom dataset consisting of reviews form several E-learning sites. What I am trying to do is build a model that can recognize emotions based on text and for training I am using the dataset I've made via scraping. While working on BERT, I encountered this error

normalize() argument 2 must be str, not float

here's my code:-

import numpy as np 
import pandas as pd
import numpy as np


import tensorflow as tf
print(tf.__version__)
import ktrain
from ktrain import text

from sklearn.model_selection import train_test_split
import pickle


#class_names = ["Frustration", "Not satisfied", "Satisfied", "Happy", "Excitement"]


data = pd.read_csv("Final_scraped_dataset.csv")
print(data.head())

X = data['Text']
y = data['Emotions']


class_names = np.unique(data['Emotions'])
print(class_names)
        
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)


    
    
print(X_train.shape)
print(y_train.shape)

print(X_test.shape)
print(y_test.shape)
print(X_train.head(10))

encoding = {
    'Frustration': 0,
    'Not satisfied': 1,
    'Satisfied': 2,
    'Happy': 3,
    'Excitement' : 4
}

y_train = [encoding[x] for x in y_train]
y_test = [encoding[x] for x in y_test]



X_train = X_train.tolist()
X_test = X_test.tolist()




#print(X_train)

(x_train,  y_train), (x_test, y_test), preproc = text.texts_from_array(x_train=X_train, y_train=y_train,
                                                                       x_test=X_test, y_test=y_test,
                                                                       class_names=class_names,
                                                                       preprocess_mode='bert',
                                                                       maxlen=200, 
                                                                       max_features=15000) #I've encountered the error here


'''model = text.text_classifier('bert', train_data=(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=4)

learner.fit_onecycle(2e-5, 3)


learner.validate(val_data=(x_test, y_test))

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

import time 

message = 'I hate you a lot'

start_time = time.time() 
prediction = predictor.predict(message)

print('predicted: {} ({:.2f})'.format(prediction, (time.time() - start_time)))

# let's save the predictor for later use
predictor.save("new_model/bert_model")


print("SAVED  _______")'''

here's the complete error:-

  File "D:\Sentiment analysis\BERT_model_new_dataset.py", line 73, in <module>
    max_features=15000)

  File "D:\Anaconda3\envs\pythy37\lib\site-packages\ktrain\text\data.py", line 373, in texts_from_array
    trn = preproc.preprocess_train(x_train, y_train, verbose=verbose)

  File "D:\Anaconda3\envs\pythy37\lib\site-packages\ktrain\text\preprocessor.py", line 796, in preprocess_train
    x = bert_tokenize(texts, self.tok, self.maxlen, verbose=verbose)

  File "D:\Anaconda3\envs\pythy37\lib\site-packages\ktrain\text\preprocessor.py", line 166, in bert_tokenize
    ids, segments = tokenizer.encode(doc, max_len=max_length)

  File "D:\Anaconda3\envs\pythy37\lib\site-packages\keras_bert\tokenizer.py", line 73, in encode
    first_tokens = self._tokenize(first)

  File "D:\Anaconda3\envs\pythy37\lib\site-packages\keras_bert\tokenizer.py", line 103, in _tokenize
    text = unicodedata.normalize('NFD', text)

TypeError: normalize() argument 2 must be str, not float

Answer 1

It sounds like you may have a float value in your data['Text'] column somehow.

You can try something like this to shed more light on what's happening:

for i, s in enumerate(data['Text']):
    if not isinstance(s, str):  print('Text in row %s is not a string: %s' % (i, s))