def create_and_check_bert_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFBertModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} sequence_output, pooled_output = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TFBertModel(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) # Also check the case where encoder outputs are not passed result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def test_model_from_pretrained(self): model = TFBertModel.from_pretrained("jplu/tiny-tf-bert-random") self.assertIsNotNone(model)