def test_TFRobertaForMaskedLM(self): from transformers import RobertaConfig, TFRobertaForMaskedLM keras.backend.clear_session() # pretrained_weights = 'roberta-base' tokenizer_file = 'roberta_roberta-base.pickle' tokenizer = self._get_tokenzier(tokenizer_file) text, inputs, inputs_onnx = self._prepare_inputs(tokenizer) config = RobertaConfig() model = TFRobertaForMaskedLM(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, rtol=1.e-2, atol=1.e-4))
def test_TFRobertaForMaskedLM(self): from transformers import RobertaTokenizer, TFRobertaForMaskedLM pretrained_weights = 'roberta-base' tokenizer = RobertaTokenizer.from_pretrained(pretrained_weights) text, inputs, inputs_onnx = self._prepare_inputs(tokenizer) model = TFRobertaForMaskedLM.from_pretrained(pretrained_weights) 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, rtol=1.e-2, atol=1.e-4))
def get_pretrained_roberta(pretrained_model): # NOTE: This will be pretrained unlike our analogous method for bert. return TFRobertaForMaskedLM.from_pretrained( pretrained_model, from_pt=roberta.from_pt(pretrained_model))