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examples/run_tf_glue.py

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+import tensorflow as tf
+import tensorflow_datasets
+from pytorch_transformers import BertTokenizer, TFBertForSequenceClassification, glue_convert_examples_to_features
+
+# Load tokenizer, model, dataset
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
+dataset, dataset_info = tensorflow_datasets.load("glue/mrpc", split="train", with_info=True)
+print(dataset_info)
+
+# Prepare dataset for GLUE
+dataset = glue_convert_examples_to_features(dataset, tokenizer, task='mrpc', max_length=64)
+dataset = dataset.batch(32)
+
+# Compile model for training
+learning_rate = tf.keras.optimizers.schedules.PolynomialDecay(5e-5, 300, 0)
+model.compile(optimizer=tf.keras.optimizers.Adam(
+              learning_rate=learning_rate),
+              loss=lambda x, y: tf.keras.backend.sparse_categorical_crossentropy(x, y, from_logits=True),
+              metrics=['accuracy'])
+
+# Train model
+model.fit(dataset, epochs=3)