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|
|
|
| import tensorflow as tf
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| from tensorflow.keras import layers, Model
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| from transformers import TFAutoModel
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|
|
| class MixedDataCrossEncoderTF(Model):
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| def __init__(self, model_name="dbmdz/bert-base-turkish-cased", numerical_feature_dim=5132, max_token_len=32, **kwargs):
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| super().__init__(**kwargs)
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| self.model_name = model_name
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| self.numerical_feature_dim = numerical_feature_dim
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| self.max_token_len = max_token_len
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|
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| self.bert = TFAutoModel.from_pretrained(model_name)
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|
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| self.numerical_mlp = tf.keras.Sequential([
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| layers.Input(shape=(numerical_feature_dim,)),
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| layers.Dense(512, activation='relu'),
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| layers.Dropout(0.3),
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| layers.Dense(128, activation='relu')
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| ], name="numerical_mlp")
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|
|
| self.concatenation = layers.Concatenate()
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| self.classifier = tf.keras.Sequential([
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| layers.Dense(256, activation='relu'),
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| layers.BatchNormalization(),
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| layers.Dropout(0.3),
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| layers.Dense(128, activation='relu'),
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| layers.BatchNormalization(),
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| layers.Dense(64, activation='relu'),
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| layers.BatchNormalization(),
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| layers.Dense(1, activation='sigmoid')
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| ], name="classifier")
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|
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| def call(self, inputs):
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| bert_output = self.bert(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])
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| text_features = bert_output.pooler_output
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|
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| numerical_processed_features = self.numerical_mlp(inputs['numerical_features'])
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|
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| combined_features = self.concatenation([text_features, numerical_processed_features])
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|
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| prediction_score = self.classifier(combined_features)
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| return prediction_score
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|
|
| def get_config(self):
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| config = super().get_config()
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| config.update({
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| "model_name": self.model_name,
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| "numerical_feature_dim": self.numerical_feature_dim,
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| "max_token_len": self.max_token_len,
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| })
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| return config
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|
