GPT2 모형의 출력으로 감성 분석

from google.colab import drive
drive.mount('/gdrive')

import tensorflow as tf

filenames = ['/gdrive/My Drive/kogpt2/x_train_token.tfrecord']
raw_dataset = tf.data.TFRecordDataset(filenames)
feature_description = {
    'input': tf.io.FixedLenSequenceFeature([], tf.int64, allow_missing=True),
    'label': tf.io.FixedLenFeature([], tf.int64)
}
parsed_dataset = raw_dataset.map(lambda x: tf.io.parse_single_example(x, feature_description))

label_dataset = parsed_dataset.map(lambda x: x['label'])

filenames = ['/gdrive/My Drive/kogpt2/x_train_embed.tfrecord']
emb_raw_dataset = tf.data.TFRecordDataset(filenames)

emb_dataset = emb_raw_dataset.map(lambda x: tf.ensure_shape(tf.io.parse_tensor(x, tf.float32), (768,)))

classifier = tf.keras.Sequential([
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(3, activation='softmax')
])

classifier.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

dataset = tf.data.Dataset.zip((emb_dataset, label_dataset)).batch(32)

classifier.fit(dataset, epochs=5)