def efficientdet_lite4_spec(**kwargs): args = util.dict_with_default(default_dict=dict( model_name='efficientdet-lite4', uri='https://tfhub.dev/tensorflow/efficientdet/lite4/feature-vector/1' ), **kwargs) return EfficientDetModelSpec(**args)
def resnet_50_spec(**kwargs): args = util.dict_with_default(default_dict=dict( uri='https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4', compat_tf_versions=2, name='resnet_50'), **kwargs) return ImageModelSpec(**args)
def efficientnet_lite0_spec(**kwargs): """Model specification for EfficientNet-Lite0.""" args = util.dict_with_default(default_dict=dict( uri='https://tfhub.dev/tensorflow/efficientnet/lite0/feature-vector/2', compat_tf_versions=[1, 2], name='efficientnet_lite0'), **kwargs) return ImageModelSpec(**args)
def mobilenet_v2_spec(**kwargs): args = util.dict_with_default(default_dict=dict( uri= 'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4', compat_tf_versions=2, name='mobilenet_v2'), **kwargs) return ImageModelSpec(**args)
def efficientnet_lite3_spec(**kwargs): args = util.dict_with_default( default_dict=dict( uri='https://tfhub.dev/tensorflow/efficientnet/lite3/feature-vector/2', compat_tf_versions=[1, 2], input_image_shape=[280, 280], name='efficientnet_lite3'), **kwargs) return ImageModelSpec(**args)
def efficientnet_lite2_spec(**kwargs): """Model specification for EfficientNet-Lite2.""" args = util.dict_with_default(default_dict=dict( uri='https://tfhub.dev/tensorflow/efficientnet/lite2/feature-vector/2', compat_tf_versions=[1, 2], input_image_shape=[260, 260], name='efficientnet_lite2'), **kwargs) args.update(**kwargs) return ImageModelSpec(**args)
def mobilebert_classifier_spec(**kwargs): """Model specification for MobileBERT in the text classification task.""" args = util.dict_with_default(default_dict=dict( uri= 'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/1', is_tf2=False, distribution_strategy='off', name='MobileBert', default_batch_size=48), **kwargs) return BertClassifierModelSpec(**args)
def mobilebert_qa_spec(**kwargs): """Model specification for MobileBERT in the question answer task.""" args = util.dict_with_default(default_dict=dict( uri= 'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/1', is_tf2=False, distribution_strategy='off', learning_rate=4e-05, name='MobileBert', default_batch_size=32), **kwargs) return BertQAModelSpec(**args)
def mobilebert_classifier_spec(**kwargs): """Model specification for MobileBERT in the text classification task.""" args = util.dict_with_default(default_dict=dict( uri= 'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/1', is_tf2=False, distribution_strategy='off', convert_from_saved_model_tf2=True, name='MobileBert', tflite_input_name=_MOBILEBERT_TFLITE_INPUT_NAME, default_batch_size=48), **kwargs) return BertClassifierModelSpec(**args)
def mobilebert_qa_squad_spec(**kwargs): """Model specification for MobileBERT that already retrained on SQuAD1.1.""" args = util.dict_with_default(default_dict=dict( uri= 'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/squadv1/1', is_tf2=False, distribution_strategy='off', learning_rate=4e-05, name='MobileBert', init_from_squad_model=True, default_batch_size=32), **kwargs) return BertQAModelSpec(**args)
def mobilebert_qa_spec(**kwargs): """Model specification for MobileBERT in the question answer task.""" args = util.dict_with_default(default_dict=dict( uri= 'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/1', is_tf2=False, distribution_strategy='off', convert_from_saved_model_tf2=True, learning_rate=4e-05, name='MobileBert', tflite_input_name=_MOBILEBERT_TFLITE_INPUT_NAME, tflite_output_name=_MOBILEBERT_TFLITE_OUTPUT_NAME, default_batch_size=32), **kwargs) return BertQAModelSpec(**args)