 I want to build a simple mobile app that identifies plants by analyzing photos of their leaves.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 I want to make a food identification app that can help me classify different dishes at a restaurant.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': \'tf.keras.Sequential([hub.KerasLayer(model_id)])\', \'api_arguments\': [\'model_id: The identifier of the pre-trained model to load\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/1\'\\nmodel = tf.keras.Sequential([hub.KerasLayer(model_id)])\\n\\n# Load an image and make a prediction\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(299, 299))\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\ninput_array = tf.expand_dims(input_array, 0)\\npredictions = model.predict(input_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'78.1%\'}, \'description\': \'A pre-trained image classification model using the Inception V3 architecture, available on TensorFlow Hub. The model can be used to classify images into 1000 different categories.\'}', metadata={})]


 I want to know what is in this image I just took.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.expand_dims(image, axis=0)\\n\\nresult = detector(image)\\n", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'0.320\'}, \'description\': \'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\'}', metadata={})]


 We need to classify images taken by our surveillance camera to detect unwanted activity.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection and classification in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\nresult = detector(image_tensor)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not specified\'}, \'description\': \'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\'}', metadata={})]


 We are working with an autonomous vehicle company, and they want to detect and classify objects in the environment while the vehicle is on the road.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection and classification in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\nresult = detector(image_tensor)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not specified\'}, \'description\': \'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\'}', metadata={})]


 How do I know the breed of a random dog in a picture?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image segmentation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Semantic segmentation\', \'api_name\': \'deeplabv3\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/deeplabv3/1\')", \'api_arguments\': [\'input_tensor\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nsegmentation = hub.load(\'https://tfhub.dev/tensorflow/deeplabv3/1\')\\ninput_tensor = tf.random.uniform(shape=[1, 1024, 1024, 3], dtype=tf.float32)\\noutput = segmentation(input_tensor)", \'performance\': {\'dataset\': \'PASCAL VOC 2012\', \'accuracy\': \'87.5\'}, \'description\': \'DeepLabv3 is a state-of-the-art semantic segmentation model that uses atrous convolution and fully connected conditional random fields for dense prediction. It can be used for various image segmentation tasks, such as object segmentation, scene parsing, and person segmentation.\'}', metadata={})]


 I have a picture of my room and I want to identify all the objects present in it.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n  hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3], output_shape=[1280]),\\n  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', metadata={})]


 I have a collection of movie descriptions and I want to find the resemblance between them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n  hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3], output_shape=[1280]),\\n  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', metadata={})]


 To find similar images in a database, we need to extract useful features to compare images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1280,)\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   output_shape=(1280,))\\n])\\n\\n# Use the model to extract features from an image\\nfeature_vector = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\'}', metadata={})]


 Which API could be used to compare the semantic similarity between two given pieces of text?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 We have some cars pictures and want to classify them, give us the API call to get a pre-trained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'94.1%\'}, \'description\': \'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\'}', metadata={})]


 We have received customer complaints about our products and want to group them into different categories to address them more effectively. Help us get embeddings for the complaint text data to enable clustering.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embed text data for machine learning models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\')", \'api_arguments\': {\'model_id\': \'A string representing the URL or path to the TensorFlow Hub model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\'\\nembed = hub.load(model_id)\\nembeddings = embed([\'Hello, world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'Wikipedia\', \'accuracy\': \'Not specified\'}, \'description\': \'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\'}', metadata={})]


 Help me convert Reddit post comments into numerical vector format that can be easily understood by machine algorithms.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embed text data for machine learning models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\')", \'api_arguments\': {\'model_id\': \'A string representing the URL or path to the TensorFlow Hub model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\'\\nembed = hub.load(model_id)\\nembeddings = embed([\'Hello, world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'Wikipedia\', \'accuracy\': \'Not specified\'}, \'description\': \'Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models.\'}', metadata={})]


 Organize a digital library of photos and identify the ones with similar content by comparing their image features.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\'),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])\\n\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\\n              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\\n              metrics=[\'accuracy\'])\\n\\nmodel.fit(x_train, y_train, epochs=5)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A Keras layer that wraps the MobileNet V2 model pre-trained on ImageNet for image feature extraction. It can be used to create a custom image classification model by adding a dense layer with the desired number of output classes and training on a specific dataset.\'}', metadata={})]


 Given a collection of images, find the most likely object depicted in each image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\')", \'api_arguments\': {\'model_id\': \'A string representing the ID of the model to be loaded\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1\'\\ndetector = hub.load(model_id)\\n\\n# Run the detector on an image\\nresult = detector(image_tensor)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Varies depending on the model version\'}, \'description\': \'A collection of TensorFlow Hub models for detecting objects in images using the EfficientDet architecture. These models can be used for various tasks such as object detection, instance segmentation, and more.\'}', metadata={})]


 I am making an image recognition app, and I want to extract features from each image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1280,)\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   output_shape=(1280,))\\n])\\n\\n# Use the model to extract features from an image\\nfeature_vector = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\'}', metadata={})]


 Analyze whether the descriptions of two movies are similar by generating dense vector representations of the text and comparing them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text data\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'Text input\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various text sources\', \'accuracy\': \'High similarity scores for semantically similar sentences\'}, \'description\': \'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\'}', metadata={})]


 We have a set of images that we want to classify into different categories. We need a pre-trained image classification model for this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification using a pre-trained model\', \'api_name\': \'mobilenet_v2\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\')", \'api_arguments\': {\'model_url\': \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', \'input_shape\': \'IMAGE_SHAPE+(3,)\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nIMAGE_SHAPE = (224, 224)\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', input_shape=IMAGE_SHAPE+(3,))\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\', target_size=IMAGE_SHAPE)\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\nresult = model.predict(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices.\'}', metadata={})]


 Our team has an image classification project. First, we need to extract features from our images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1280,)\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   output_shape=(1280,))\\n])\\n\\n# Use the model to extract features from an image\\nfeature_vector = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\'}', metadata={})]


 I want to cluster customer chat messages so that their related concerns are addressed in a more meaningful way.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image segmentation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image segmentation model\', \'api_name\': \'deeplabv3_257_mv_gpu\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': \'import tensorflow_hub as hub\\nmodel = hub.load("https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1")\\nsegmentation = model(input_image)\', \'performance\': {\'dataset\': \'PASCAL VOC 2012\', \'accuracy\': \'0.897\'}, \'description\': \'DeepLabv3 is a state-of-the-art deep learning model for image segmentation. It assigns a semantic label to each pixel in the input image, allowing for a more detailed understanding of the image content. This model is trained on the PASCAL VOC 2012 dataset and achieves a mean intersection-over-union (mIoU) score of 0.897.\'}', metadata={})]


 I want to build a text classifier for articles that could also find articles with similar content. Can you suggest a way to encode the text of the articles?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 There is a need to segment images of a park area with trees and people, and distinguish the trees from people.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'TensorFlow 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\ndef load_image(file_path):\\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\\n    return img_array\\n\\n# Predict the class of the image\\nimg_array = load_image(\'path/to/your/image.jpg\')\\npredictions = model(img_array)\\n\\n# Get the class with the highest probability\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\'}', metadata={})]


 Our security firm needs an image analysis solution to detect cars and pedestrians from CCTV images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 Can you recommend an API that helps to convert a research article abstract into a high-dimensional vector which we can then use to find similar articles?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading a pre-trained image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/4\'\\nmodel = hub.load(model_id)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'This API allows you to load a pre-trained image classification model from TensorFlow Hub using a specific model_id. The model can then be used to classify images into different categories.\'}', metadata={})]


 I need to monitor a warehouse's inventory and identify objects kept in the storage area.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\')\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\n\\nresult = detector(input_image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP\'}, \'description\': \'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\'}', metadata={})]


 Create a feature extraction model to recognize 10 different types of images from a given dataset with sizes 224x224x3.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'TensorFlow 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\ndef load_image(file_path):\\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\\n    return img_array\\n\\n# Predict the class of the image\\nimg_array = load_image(\'path/to/your/image.jpg\')\\npredictions = model(img_array)\\n\\n# Get the class with the highest probability\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\'}', metadata={})]


 I need a quick way to classify images on my phone. Can you use a computationally efficient model to do this?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Load and use pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.random.uniform((1, 224, 224, 3))\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\'}, \'description\': \'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\'}', metadata={})]


 Provide me with the facility to determine the similarity between two sentences using encoded text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 We want to recognize different types of dogs in images for an upcoming app. Help us with a model to get the image features.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'TensorFlow 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\ndef load_image(file_path):\\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\\n    return img_array\\n\\n# Predict the class of the image\\nimg_array = load_image(\'path/to/your/image.jpg\')\\npredictions = model(img_array)\\n\\n# Get the class with the highest probability\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\'}', metadata={})]


 I am planning my garden, please help me segment plants in an image for better landscape planning.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\nimage = tf.image.decode_image(tf.io.read_file(\'path/to/image.jpg\'))\\nresult = detector(image[tf.newaxis, ...])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained TensorFlow Hub model for detecting objects in an image using the Single Shot MultiBox Detector (SSD) with MobileNet V2.\'}', metadata={})]


 Find out the type of birds in my backyard based on smartphone photos I took.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow>=2.0\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\n\\nresult = detector(image_tensor)\\nprint(result)", \'performance\': {\'dataset\': \'Open Images v4\', \'accuracy\': \'0.545 mAP\'}, \'description\': \'A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates.\'}', metadata={})]


 I'm working on a project that requires text clustering, and I need to convert a list of product reviews into vector representations in order to analyze the semantic similarities between them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into a high-dimensional vector\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text: A list of strings to be embedded\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub; embed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\'); embeddings = embed([\'Hello world!\'])", \'performance\': {\'dataset\': \'STSbenchmark\', \'accuracy\': \'Pearson correlation of 0.803\'}, \'description\': \'The Universal Sentence Encoder encodes text into a high-dimensional vector that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 I found a bird on my trip outside, and I took a photo. Can you identify it?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate image feature vectors\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'output_shape\': \'[1280]\', \'trainable\': \'False\'}, \'python_environment_requirements\': [\'tensorflow>=2.0.0\', \'tensorflow-hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                    output_shape=[1280],\\n                    trainable=False),\\n    tf.keras.layers.Dense(5, activation=\'softmax\')\\n])\\n\\nmodel.build([None, 224, 224, 3])\\nmodel.summary()", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture on ImageNet dataset. Can be used for transfer learning and generating image feature vectors.\'}', metadata={})]


 Figure out the category of my image by using a pre-trained Inception model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nloaded_model = hub.load(model_id)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not provided\'}, \'description\': \'Load an image classification model from TensorFlow Hub by providing the model_id. This example demonstrates how to load the Inception V3 model.\'}', metadata={})]


 Images have come from users, and we need to recognize their categories for better curation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'ssd_mobilenet_v2\', \'api_call\': "hub.load(\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\')", \'api_arguments\': \'image, score_threshold\', \'python_environment_requirements\': \'Python 3, TensorFlow 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\ndetector = hub.load(\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\')\\nresult = detector(image, score_threshold=0.5)", \'performance\': {\'dataset\': \'Open Images v4\', \'accuracy\': \'mAP@0.5IOU = 32.8%\'}, \'description\': \'A pre-trained SSD MobileNet V2 model for object detection in images, trained on the Open Images v4 dataset. Detects objects and outputs their bounding boxes and class labels with associated confidence scores.\'}', metadata={})]


 We have several support tickets in our system, find a way to cluster them to find the most occurring topics.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Load and use pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/resnet_v2_50/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.random.uniform((1, 224, 224, 3))\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Top-1 Accuracy: 76.3%, Top-5 Accuracy: 93.2%\'}, \'description\': \'This API allows you to load and use pre-trained models for image classification from TensorFlow Hub. It provides a simple way to integrate state-of-the-art image classification models into your applications.\'}', metadata={})]


 We need to create an image classifier using transfer learning. I want to get feature vectors from pre-trained models.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 Can you help me to create fixed-sized vector representation for every input phrases in a dataframe?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.5.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),  # Can be True, see below.\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\'}', metadata={})]


 I need an API to help me classify pet images. I'd like to use the extracted features from these images to make better predictions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'model_id\': \'URL or path to the TensorFlow Hub model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\nimage = tf.zeros([1, 224, 224, 3])\\noutput = model(image)\\nprint(output)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\'}', metadata={})]


 Help me with identifying the object present in my new picture, using a ready-to-use model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Identify and locate objects in images\', \'api_name\': \'ssd_mobilenet_v2\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': {\'input\': \'Tensor of shape [batch_size, height, width, 3]\', \'output\': "Dictionaries with keys \'detection_boxes\', \'detection_classes\', \'detection_scores\'"}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\ndetector = hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\'path/to/image.jpg\'))\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\nresult = detector(image_tensor)\\nprint(result)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP of 0.22\'}, \'description\': \'A pre-trained model for object detection using SSD MobileNet V2 architecture, capable of identifying and locating objects in images.\'}', metadata={})]


 I want to analyze images of my coin collection and build a classifier to identify coins. Provide me with a method to extract image features.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using a pre-trained image feature vector model\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'False\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(1)])\\n\\nmodel.build([None, 224, 224, 3])\\nmodel.summary()", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. This model can be used for transfer learning to extract meaningful features from images and fine-tune on a specific classification task.\'}', metadata={})]


 We are doing data pre-processing for a project and our aim is to understand if the given two product titles are semantically similar or not. We first need to convert these product titles into high-dimensional vectors.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text data\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'Text input\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various text sources\', \'accuracy\': \'High similarity scores for semantically similar sentences\'}, \'description\': \'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\'}', metadata={})]


 I'm working on a plant recognition app, and I want to classify a plant's image taken by users.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3], output_shape=[1280])\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\'}', metadata={})]


 How can I calculate the similarity between two sentences based on their embeddings?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 I have an image of my backyard. Identify if the BBQ grill is active.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n  hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3], output_shape=[1280]),\\n  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', metadata={})]


 I am building a phone app that recognizes food. We need a lightweight model to identify different types of food.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification using a pre-trained model\', \'api_name\': \'mobilenet_v2\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\')", \'api_arguments\': {\'model_url\': \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', \'input_shape\': \'IMAGE_SHAPE+(3,)\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nIMAGE_SHAPE = (224, 224)\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', input_shape=IMAGE_SHAPE+(3,))\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\', target_size=IMAGE_SHAPE)\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\nresult = model.predict(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices.\'}', metadata={})]


 We are interesting in analyzing text messages to recommend interesting articles to our users. For this, we need to create an embedding for each message.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Create image feature vector\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4\'\\nmodel = hub.load(model_id)\\nfeature_vector = model(image_tensor)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'Load an image feature vector model from TensorFlow Hub and use it to create feature vectors for images.\'}', metadata={})]


 Could you help me to find a quick way to rate similarities between my blog posts?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 I have a lot of vacation pictures and want to find those with similar content. Can you help me?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 I am doing holiday gift shopping online, and I want to know what category a picture of a gift item belongs to.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 Can you find an efficient image classification model to use on my smartphone for recognizing food items in my fridge?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading a pre-trained image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'model_id\': \'URL of the TensorFlow Hub model\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Depends on the specific model\'}, \'description\': \'This API allows you to load a pre-trained image classification model from TensorFlow Hub for use in your applications.\'}', metadata={})]


 Our art gallery is conducting an exhibition and we want to classify the images of paintings into different genres.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'TensorFlow 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\ndef load_image(file_path):\\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\\n    return img_array\\n\\n# Predict the class of the image\\nimg_array = load_image(\'path/to/your/image.jpg\')\\npredictions = model(img_array)\\n\\n# Get the class with the highest probability\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\'}', metadata={})]


 We are setting up an AI system to help classify images from outdoor events into different categories based on their content.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification using pre-trained models\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model.predict(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset. It can classify images into 1000 different categories with an accuracy of 71.0%.\'}', metadata={})]


 I am creating a shopping app and I want to suggest similar looking items from our database using item's image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 I have an image and I want to recognize the object in this image, so I need to access a pre-trained image classification model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'94.1%\'}, \'description\': \'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\'}', metadata={})]


 I have taken some pictures of my dog, and I want to organize them in a way that those which are similar to each other are together.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text data\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'Text input\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various text sources\', \'accuracy\': \'High similarity scores for semantically similar sentences\'}, \'description\': \'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\'}', metadata={})]


 We want to find the most similar questions from user inputs to give them the best answer.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 I am working on a product recommendation system for a flower shop. Extract feature vectors from flower images to be used in the system.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extracting feature vectors from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224,224,3))\\n])\\n\\n# Load an image and preprocess it\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\n# Extract the feature vector\\nfeature_vector = model.predict(image_array)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API allows you to extract feature vectors from images using the MobileNetV2 model trained on the ImageNet dataset. The feature vectors can be used for various machine learning tasks, such as image classification, image retrieval, or transfer learning.\'}', metadata={})]


 Recommend a playlist to me based on the audio characteristics of this song clip I'm sending you.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Audio feature extraction\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/yamnet/1\')", \'api_arguments\': [{\'model_id\': \'URL or path to the TensorFlow Hub model\'}], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/yamnet/1\'\\nmodel = hub.load(model_id)\\n\\n# Load a wav file\\nfilename = \'audio_file.wav\'\\nfile_contents = tf.io.read_file(filename)\\nwaveform = tf.audio.decode_wav(file_contents).audio\\n\\n# Extract features\\nscores, embeddings, spectrogram = model(waveform)", \'performance\': {\'dataset\': \'AudioSet\', \'accuracy\': \'Not specified\'}, \'description\': "YAMNet is a deep net that predicts 521 audio event classes based on the AudioSet dataset. It\'s used for audio feature extraction and can be used for tasks like audio classification, tagging, and recommendation."}', metadata={})]


 We are building a car recognition system. We need to extract features from car images to feed them into the classifier.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1280,)\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   output_shape=(1280,))\\n])\\n\\n# Use the model to extract features from an image\\nfeature_vector = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\'}', metadata={})]


 Please help me with a system that studies product descriptions so that we can group products with similar features.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text data\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'Text input\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various text sources\', \'accuracy\': \'High similarity scores for semantically similar sentences\'}, \'description\': \'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\'}', metadata={})]


 I need to find features in the images to perform image classification.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1280,)\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   output_shape=(1280,))\\n])\\n\\n# Use the model to extract features from an image\\nfeature_vector = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\'}', metadata={})]


 We need to find similar news articles from a large corpus. Please embed them into high-dimensional vectors.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text data\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'Text input\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various text sources\', \'accuracy\': \'High similarity scores for semantically similar sentences\'}, \'description\': \'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\'}', metadata={})]


 Discover all the elements present within a photograph of a street scene.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\')", \'api_arguments\': [{\'name\': \'model_id\', \'type\': \'string\', \'description\': \'The unique identifier of the model to be loaded from TensorFlow Hub\'}], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\n\\nresult = detector(image[np.newaxis, ...])", \'performance\': {\'dataset\': \'Open Images V4\', \'accuracy\': \'Not specified\'}, \'description\': \'Loads a TensorFlow Hub model for image object detection, allowing the detection of objects within images using the Faster R-CNN architecture with Inception ResNet V2.\'}', metadata={})]


 We own a car dealer business and want to develop an app that categorizes different car categories based on uploaded images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Load and run a pre-trained image classification model\', \'api_name\': \'imagenet_mobilenet_v2_100_224\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Run the model on a sample image\\nsample_image = tf.zeros([1, 224, 224, 3])\\nlogits = model(sample_image)\\npredicted_class = tf.argmax(logits, axis=-1)\\nprint(predicted_class)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This API allows you to load and run a pre-trained MobileNetV2 image classification model from TensorFlow Hub. The model is trained on the ImageNet dataset and can classify images into 1000 different categories with an accuracy of 71.9%.\'}', metadata={})]


 Given images of painted landscapes, we want to find similar images based on their visual features. Which API can we use for extracting the feature vector?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 We need to know what is in these photos so we can report to our officials.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image segmentation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image segmentation model\', \'api_name\': \'deeplabv3_257_mv_gpu\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')", \'api_arguments\': {\'input\': \'Image tensor\', \'output\': \'Segmentation mask tensor\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')\\nimage_tensor = tf.image.resize(image, (257, 257))\\nsegmentation_mask = model(image_tensor)", \'performance\': {\'dataset\': \'PASCAL VOC 2012\', \'accuracy\': \'0.899\'}, \'description\': \'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\'}', metadata={})]


 We have a collection of animal photos, and we want to obtain a feature vector from each photo in order to properly organize them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 I took a picture of a street and want to separate the vehicles, people, and buildings in the scene.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\')\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\n\\nresult = detector(input_image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP\'}, \'description\': \'This API allows for object detection in images using TensorFlow Hub and the EfficientDet model. It can be used to load a pre-trained model and detect objects in a given image.\'}', metadata={})]


 I need to quickly classify images of various objects on my phone. Which pre-trained model should I use for efficient processing in a mobile environment?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embed text for various natural language processing tasks\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'text_input\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\', \'Embedding with Universal Sentence Encoder.\'])", \'performance\': {\'dataset\': \'STS-Benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803\'}, \'description\': \'A text embedding model that converts text into high-dimensional vectors for use in natural language processing tasks. The Universal Sentence Encoder is trained on a variety of data sources and can be fine-tuned for specific tasks.\'}', metadata={})]


 I want to enhance my wildlife images and need to extract an image feature vector to use in advanced image processing tasks.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 299, 299, 3])\\nfeatures = model(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\'}', metadata={})]


 My computer has folders with many images, and I want to classify these images into categories, such as animals, buildings, and so on.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection and classification in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\nresult = detector(image_tensor)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not specified\'}, \'description\': \'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\'}', metadata={})]


 Show me how to classify an image of a dog using a pre-trained model with TensorFlow Hub and Inception V3 architecture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification using pre-trained models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(299, 299))\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\n\\npredictions = model(input_image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'TensorFlow Hub provides pre-trained models for image classification tasks. This example demonstrates how to load a pre-trained Inception V3 model from TensorFlow Hub and use it to classify an image.\'}', metadata={})]


 Recommend a suitable movie based on a given movie's plot. For better recommendations, obtain the plot's embedding.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\ndef predict(image):\\n    logits = model(image)\\n    return logits", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices.\'}', metadata={})]


 Obtain image features from an image dataset to use in a product recommendation system\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 299, 299, 3])\\nfeatures = model(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\'}', metadata={})]


 I took a photo of a car and I want to extract its features in a vector form to use it for classification tasks later.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 299, 299, 3])\\nfeatures = model(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'Load a pre-trained image feature vector model from TensorFlow Hub and use it to extract features from an image.\'}', metadata={})]


 Calculate the similarity between two sentences and determine if they have the same meaning.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 We have an image dataset and want a pre-trained model as a feature extractor.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v1_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/4\')", \'api_arguments\': \'input_shape, batch_size, dtype, trainable\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/4\', input_shape=(224, 224, 3), trainable=False),\\n    tf.keras.layers.Dense(1, activation=\'sigmoid\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'70.9%\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV1 architecture for image classification and feature extraction. The model is trained on the ImageNet dataset and provides an accuracy of 70.9%.\'}', metadata={})]


 We are creating a custom image search engine that can give similar image results. Extract features from images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract features from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n  hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3])\\n])\\n\\n# Load an image and preprocess it\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.expand_dims(image, 0)\\n\\n# Extract features from the image\\nfeatures = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'A pre-trained deep learning model that extracts features from images using the MobileNetV2 architecture.\'}', metadata={})]