Kaggle competition: Jigsaw Multilingual Toxic Comment Classification
<import_modules><EOS> 
import pandas as pd
<SOS> metric: AUC Kaggle data source: jigsaw-multilingual-toxic-comment-classification<choose_model_class>
import pandas as pd
class TFBertForNaturalQuestionAnswering(TFBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
 self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name='bert')
 self.initializer = get_initializer(config.initializer_range)
 self.qa_outputs = L.Dense(config.num_labels,
kernel_initializer=self.initializer, name='qa_outputs')
 self.long_outputs = L.Dense(1, kernel_initializer=self.initializer,
name='long_outputs')
 def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
 sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
 start_logits, end_logits = tf.split(logits, 2, axis=-1)
 start_logits = tf.squeeze(start_logits, -1)
 end_logits = tf.squeeze(end_logits, -1)
 long_logits = tf.squeeze(self.long_outputs(sequence_output), -1)
 return start_logits, end_logits, long_logits 
<choose_model_class>
df = pd.read_csv(".. /input/submission4/submission2.csv" )
class TFRobertaForNaturalQuestionAnswering(TFRobertaPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
 self.num_labels = config.num_labels
self.roberta = TFRobertaMainLayer(config, name='roberta')
 self.initializer = get_initializer(config.initializer_range)
 self.qa_outputs = L.Dense(config.num_labels,
kernel_initializer=self.initializer, name='qa_outputs')
 self.long_outputs = L.Dense(1, kernel_initializer=self.initializer,
name='long_outputs')
 def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs)
 sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
 start_logits, end_logits = tf.split(logits, 2, axis=-1)
 start_logits = tf.squeeze(start_logits, -1)
 end_logits = tf.squeeze(end_logits, -1)
 long_logits = tf.squeeze(self.long_outputs(sequence_output), -1)
 return start_logits, end_logits, long_logits<import_modules>
df.to_csv('submission.csv', index=False )