def test_TFBertForSequenceClassification(self):
from transformers import BertConfig, TFBertForSequenceClassification
keras.backend.clear_session()
# pretrained_weights = 'bert-base-uncased'
tokenizer_file = 'bert_bert-base-uncased.pickle'
tokenizer = self._get_tokenzier(tokenizer_file)
text, inputs, inputs_onnx = self._prepare_inputs(tokenizer)
config = BertConfig()
model = TFBertForSequenceClassification(config)
predictions = model.predict(inputs)
onnx_model = keras2onnx.convert_keras(model, model.name)
self.assertTrue(run_onnx_runtime(onnx_model.graph.name, onnx_model, inputs_onnx, predictions, self.model_files))
config = BertConfig(num_labels=3, return_dict=True, model_type='bert-base-uncased')
model = TFBertForSequenceClassification(config=config)
if save_model:
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
model.compile(optimizer=optimizer, loss=model.compute_loss, metrics=['accuracy'])
model.fit(
train_dataset[0],
np.array(y_list),
epochs=5,
batch_size=BATCH_SIZE,
callbacks=[cp_callback]
)
else:
latest = tf.train.latest_checkpoint(checkpoint_dir)
model.load_weights(latest)
preds = model.predict(val_dataset[0])["logits"]
preds_proba = tf.keras.backend.softmax(preds, axis=1)
classes = np.argmax(preds, axis=-1)
score = classification_report(y_val, classes, digits=3)
print(score)
total = time.time() - start
print(f"Done in: {total}")
