 Figure out a way to extract valuable features from images of potential clients' products to compare their similarity.\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={})]


 My personal trainer wants me to analyze my workout form. Can you help me detect my body position in a photo?\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 help me highlight animals in my vacation photographs?\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={})]


 Detect objects and their locations on this image I just took of my room.\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={})]


 Create a flower classification model using feature vectors pre-extracted from images.\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 running a wildlife conservation project, and I need help to identify different species of animals and their locations in the images taken by camera traps.\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={})]


 We need to detect spam emails based on their subjects. Retrieve a high-dimensional vector from each email subject to be used for classification.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection\', \'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\'\\nloaded_model = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\nimage_np = np.array(image)\\n\\ninput_tensor = tf.convert_to_tensor(image_np)\\ninput_tensor = input_tensor[tf.newaxis, ...]\\n\\noutput_dict = loaded_model(input_tensor)\\n\\nboxes = output_dict[\'detection_boxes\'][0].numpy()\\nscores = output_dict[\'detection_scores\'][0].numpy()\\nclasses = output_dict[\'detection_classes\'][0].numpy().astype(np.int32)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'0.320\'}, \'description\': \'This API provides a pre-trained object detection model using TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and is trained on the COCO dataset. It can be used to detect multiple objects in an image and returns their class, bounding box coordinates, and confidence scores.\'}', metadata={})]


 There are a bunch of user reviews about our new product, and we need assistance categorizing them into different sentiments.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'input: List of text strings\', \'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!\', \'I am learning about text embedding\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 We have images that we want to analyze, and I hope I can find a way to extract features from these images for further analysis.\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={})]


 What is a good way to find the similarity between two sentences?\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'm a clothing store owner, and I want to create a recommendation system that suggests similar clothes by comparing their 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={})]


 I want to build a plant identifier app, and I need to determine the type of plant in a picture taken from the app.\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 please identify whether these two sentenes share a similar meaning or not?\n###Input: \"The quick brown fox jumps over the lazy dog.\", \"A speedy, sly fox leaps over a lounging canine.\"\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 want to create a custom image classifier, but first I need to use a pre-trained model to extract features from my dataset images.\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={})]


 Recommend a mobile app for automatically classifying images taken by the user.\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 want to find semantic similarity.\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 want to develop a smart surveillance system to monitor intruders. Please help us identify humans present in the 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\': {\'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={})]


 My friends uploaded a collection of photos from our trip, and I want to organize them by identifying what is in each photo.\n###Input: <noinput>\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={})]


 Identify which model is used to encode text for natural language processing tasks like text classification and semantic similarity.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional space\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'input_text\', \'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\': \'Semantic Textual Similarity\', \'accuracy\': \'0.8\'}, \'description\': \'This API provides a pre-trained model to convert text into high-dimensional space, which can be used for various natural language processing tasks such as semantic similarity, clustering, and classification.\'}', metadata={})]


 Calculate the similarity between the following two sentences: \"The quick brown fox jumps over the lazy dog\" and \"The fast auburn fox leapt over the idle canine.\"\n###Input: \"The quick brown fox jumps over the lazy dog\", \"The fast auburn fox leapt over the idle canine.\"\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={})]


 Construct a recommendation engine for a forum that finds similar threads based on the text content.\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={})]


 They have a dataset of stained glass images and want to organize them into categories or classes. They need a pre-trained model.\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={})]


 Provide me with a method to understand the semantic similarity between movie reviews.\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\', \'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\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803521\'}, \'description\': \'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\'}', metadata={})]


 We have a large photo archive, and we need a model to recognize and categorize pictures based on their content.\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={})]


 After a dinner party, I want to save the pictures taken during the party and find similar images for decoration purposes by extracting an image 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={})]


 I am trying to create clusters of similar customer feedback for a product.\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={})]


 I have pictures of animals, and I need to find out what type of animals they are by leveraging 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\': {\'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={})]


 We are working on an app that can identify plants. We have the images of the leaves, so all we need is a pre-trained model to classify the 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\': "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={})]


 Analyze the sentiments of these customer reviews before processing their feedback.\n###Input: [\"I loved the service and the food!\", \"The staff was rude, and I had a terrible experience.\", \"Not too bad, could be better.\"]\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/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/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\nresult = model(image_tensor)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Varies depending on the model\'}, \'description\': \'TensorFlow Hub provides a variety of pre-trained image classification models that can be easily loaded and used for various image classification tasks. The models are trained on the ImageNet dataset and their performance varies depending on the specific model chosen.\'}', metadata={})]


 We want to obtain the location of a boat in an image. Detect objects in the given image and provide the location of the boat if found.\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/ssd_mobilenet_v2/2\')", \'api_arguments\': [{\'name\': \'model_id\', \'type\': \'string\', \'description\': \'The ID of the model to load 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/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\n\\n# Load an image, preprocess it, and run the detector\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.image.resize(input_image, (300, 300))\\ninput_image = tf.expand_dims(input_image, 0)\\n\\n# Run the detector\\noutput = detector(input_image)\\n\\n# Process the output\\nfor i in range(output[\'num_detections\']):\\n    if output[\'detection_scores\'][i] > 0.5:\\n        print(\'Detected object:\', output[\'detection_class_entities\'][i], \'with confidence:\', output[\'detection_scores\'][i])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU\'}, \'description\': \'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\'}', metadata={})]


 Create a system to find the similarity between two sentences.\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'm building a recommendation system for similar pictures. Provide me with a vector representation of a picture to measure image similarity.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'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\': \'[None, 224, 224, 3]\', \'output_shape\': \'[None, 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\', input_shape=[224, 224, 3])\\n])\\n\\nfeatures = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'A compact representation of the MobileNet V2 model trained on ImageNet for image feature extraction. It accepts an input of shape [None, 224, 224, 3] and outputs a feature vector of shape [None, 1280].\'}', metadata={})]


 Recommend to me some suitable products based on the textual similarity of their descriptions from a database.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading\', \'api_name\': \'tensorflow_hub.KerasLayer\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/nnlm-en-dim128/2\')", \'api_arguments\': {\'handle\': \'A string, the handle of the Hub module to load\', \'kwargs\': \'Optional keyword arguments\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': \'import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(handle="https://tfhub.dev/google/nnlm-en-dim128/2")\\n])\', \'performance\': {\'dataset\': \'NNLM\', \'accuracy\': \'N/A\'}, \'description\': \'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\'}', metadata={})]


 Extract audio features to classify sounds in the environment.\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={})]


 Write an AI-powered program to summarize movie plots while retaining maximum details from the given plot.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': {\'model_id\': \'string\'}, \'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/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(299, 299))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.expand_dims(image, axis=0)\\n\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained image classification model available on TensorFlow Hub that can be used to classify images into various categories. The example provided demonstrates how to load the Inception V3 model and make predictions on a given image.\'}', metadata={})]


 Help me compare similarities between user reviews about two different products.\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={})]


 Provided with images of paintings, I want to determine which art era they belong 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={})]


 Understand the relationship between two movie synopses and determine whether they seem similar or not.\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={})]


 Suggest personalized ads to users by creating a semantic similarity model that recommends advertisements based on textual similarity to user preferences stored in the description field. \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\', \'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\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803521\'}, \'description\': \'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\'}', metadata={})]


 There is a list of customer reviews and your task is to calculate the semantic similarity between each pair of reviews.\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={})]


 Expose a method to compute the semantic similarity of pairs of sentences by comparing the high-dimensional vectors.\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\', \'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\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803521\'}, \'description\': \'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\'}', metadata={})]


 I am analyzing tweets, and I want to know the sentiment of each tweet. Please help me convert tweets into meaningful representations.\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 need to build a machine learning model that can automatically detect and classify different food items from images.\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={})]


 Identify the animal in the photo I have taken.\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={})]


 Generate an appropriate label for instance of 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'm traveling to a new city and I want to use my phone to identify different objects in my surroundings.\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/tf2-preview/mobilenet_v2/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/tf2-preview/mobilenet_v2/classification/4\'\\nmodel = hub.load(model_id)\\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(image_array)\\npredicted_class = tf.argmax(predictions[0])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\'}', metadata={})]


 Analyze a list of movie reviews and group them based on semantic similarity by first converting the reviews into embeddings.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Text preprocessing\', \'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_hub as hub; embed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\'); embeddings = embed([\'Hello world!\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'0.78\'}, \'description\': \'This module uses the Universal Sentence Encoder model to preprocess text by converting it into embeddings.\'}', metadata={})]


 I am working on a car accident detection system. Show me how to detect object and classify them in images\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={})]


 Identify the objects present in the picture I took from my last hiking trip.\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/ssd_mobilenet_v2/2\')", \'api_arguments\': {\'model_id\': \'A string representing the TensorFlow Hub model URL\'}, \'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/tensorflow/ssd_mobilenet_v2/2\'\\nmodel = hub.load(model_id)\\n\\n# Load an image\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\n\\n# Detect objects in the image\\nresult = model(image)\\n\\n# Print detected objects\\nfor obj in result[\'detection_boxes\']:\\n    print(obj)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU\'}, \'description\': \'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNetV2 backbone.\'}', metadata={})]


 We are creating a visual recommender system based on images. We need to extract features from them.\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\': \'[batch_size, 224, 224, 3]\', \'output_shape\': \'[batch_size, 1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "model = 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                    trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model that can be used to extract features from images. The model is based on the MobileNetV2 architecture and is trained on the ImageNet dataset.\'}', metadata={})]


 In order to analyze the sentiment of tweets on Twitter, I need the Universal Sentence Encoder model to convert tweets into embeddings for analysis.\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\', \'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\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803521\'}, \'description\': \'This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering.\'}', metadata={})]


 Detect and count the number of different objects in a picture I took at a beach.\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/ssd_mobilenet_v2/2\')", \'api_arguments\': {\'model_id\': \'A string representing the TensorFlow Hub model URL\'}, \'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/tensorflow/ssd_mobilenet_v2/2\'\\nmodel = hub.load(model_id)\\n\\n# Load an image\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\n\\n# Detect objects in the image\\nresult = model(image)\\n\\n# Print detected objects\\nfor obj in result[\'detection_boxes\']:\\n    print(obj)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU\'}, \'description\': \'A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNetV2 backbone.\'}', metadata={})]


 I am visiting different places and I want to know which landmark I am visiting.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'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!\', \'I am learning about embeddings.\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'Various tasks\', \'accuracy\': \'State-of-the-art\'}, \'description\': \'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 Identify different types of birds when hikers send the images to our system.\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={})]


 How can I compare the similarity between multiple sentences?\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={})]


 Tell me what kind of animal/bird is in this photo 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={})]


 Determine the top 3 closest category matches for a given image using the Inception V3 pre-trained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': {\'model_id\': \'string\'}, \'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/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(299, 299))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.expand_dims(image, axis=0)\\n\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained image classification model available on TensorFlow Hub that can be used to classify images into various categories. The example provided demonstrates how to load the Inception V3 model and make predictions on a given image.\'}', metadata={})]


 I need a simple way to encode text into vectors for my clustering algorithm.\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={})]


 Find a tool to extract features from images and create a model to identify different types of plants.\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={})]


 How do I create image-feature-vector for the last travel destination photo album to organize similar pictures together?\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={})]


 Identify the make and model of a car using an image posted on an online car marketplace.\n###Input: \"https://url-to-image-of-car-from-marketplace\"\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={})]


 Assist me in identifying animal species present in a collection of images for an ecological study.\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 building a tool to measure the similarity between different product descriptions. To do this, I need to first get embeddings for each description.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate embeddings for audio files\', \'api_name\': \'audio_embedding_model\', \'api_call\': "hub.load(\'https://tfhub.dev/google/yamnet/1\')", \'api_arguments\': {\'audio_inputs\': \'Tensor of shape [batch_size, num_samples] containing audio signals\'}, \'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 = hub.load(\'https://tfhub.dev/google/yamnet/1\')\\n\\n# Load an audio file\\naudio_data = ... # Load audio data as a tensor\\n\\n# Generate embeddings\\nembeddings = model(audio_data)", \'performance\': {\'dataset\': \'YAMNet\', \'accuracy\': \'81.6% top-1 accuracy on AudioSet\'}, \'description\': \'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\'}', metadata={})]


 Show me how to classify an image of a car into one of the 1000 categories.\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/tf2-preview/mobilenet_v2/classification/4\')", \'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/google/tf2-preview/mobilenet_v2/classification/4\'\\nmodel = hub.load(model_id)\\ninput_image = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\ninput_array = tf.keras.preprocessing.image.img_to_array(input_image)\\ninput_batch = tf.expand_dims(input_array, 0)\\npredictions = model(input_batch)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using TensorFlow Hub that can classify images into one of 1000 classes. The example code demonstrates how to load the model, preprocess an input image, and obtain predictions.\'}', metadata={})]


 We need to create an image classifier for recognizing dog breeds. Before creating the classifier, we should extract a fixed-length feature vector from each image.\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={})]


 Help me create a machine learning model to identify whether a solar panel is damaged or not using images.\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={})]


 Suppose you want to build a search engine that can find the most visually similar products in your online shop. You need to extract feature vectors from product 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\': {\'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 a teacher and want to classify images related to educational material. Find out the category of educational images by providing the image's URL.\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={})]


 Can you find the semantic similarity between a list of sentences given their embeddings obtained from a pre-trained model?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Text embedding using a pre-trained model\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'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!\', \'Embed text with Universal Sentence Encoder\'])", \'performance\': {\'dataset\': \'various\', \'accuracy\': \'N/A\'}, \'description\': \'A pre-trained text embedding model that converts sentences into fixed-size vector representations, enabling efficient semantic similarity calculations and other natural language processing tasks.\'}', metadata={})]


 An airport authority wants to detect the presence of drones close to the airport runway.\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/ssd_mobilenet_v2/2\')", \'api_arguments\': [{\'name\': \'model_id\', \'type\': \'string\', \'description\': \'The ID of the model to load 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/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\n\\n# Load an image, preprocess it, and run the detector\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.image.resize(input_image, (300, 300))\\ninput_image = tf.expand_dims(input_image, 0)\\n\\n# Run the detector\\noutput = detector(input_image)\\n\\n# Process the output\\nfor i in range(output[\'num_detections\']):\\n    if output[\'detection_scores\'][i] > 0.5:\\n        print(\'Detected object:\', output[\'detection_class_entities\'][i], \'with confidence:\', output[\'detection_scores\'][i])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU\'}, \'description\': \'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\'}', metadata={})]


 I have a lot of images and I want to categorize them based on the objects present in the images.\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/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_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\nresult = detector(image[tf.newaxis, ...])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP\'}, \'description\': \'This API allows you to detect objects in images using a pre-trained model from TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and has been trained on the COCO dataset.\'}', metadata={})]


 I'm planning to do sentiment analysis on a bunch of news articles. Help me convert the article content to 20-dimensional vectors.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Speech-to-Text\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Convert audio speech to text\', \'api_name\': \'wav2vec2_large_xlsr_53\', \'api_call\': "hub.load(\'https://tfhub.dev/vasudevgupta7/wav2vec2/1\')", \'api_arguments\': {\'audio_input\': \'wav file\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.5.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/vasudevgupta7/wav2vec2/1\')\\ntranscript = model.signatures[\'serving_default\'](audio_input)[\'output_0\']", \'performance\': {\'dataset\': \'CommonVoice\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained model to convert audio speech to text using wav2vec2_large_xlsr_53 architecture from TensorFlow Hub.\'}', metadata={})]


 In order to perform image classification using a machine learning model, I need to generate feature vectors for the given images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract feature vectors from images\', \'api_name\': \'inception_v3\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\')", \'api_arguments\': {\'input\': \'image_tensor\', \'output\': \'feature_vector\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nimage_tensor = tf.zeros([1, 299, 299, 3])\\nfeature_vector = hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\')(image_tensor)\\nprint(feature_vector.shape)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'This API allows you to extract feature vectors from images using the Inception V3 model, which is pre-trained on the ImageNet dataset. The feature vectors can be used for various machine learning tasks, such as image classification, object detection, and image similarity.\'}', metadata={})]


 Generate embeddings for movie descriptions to create a recommendation system based on semantic similarity.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate embeddings for audio files\', \'api_name\': \'audio_embedding_model\', \'api_call\': "hub.load(\'https://tfhub.dev/google/yamnet/1\')", \'api_arguments\': {\'audio_inputs\': \'Tensor of shape [batch_size, num_samples] containing audio signals\'}, \'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 = hub.load(\'https://tfhub.dev/google/yamnet/1\')\\n\\n# Load an audio file\\naudio_data = ... # Load audio data as a tensor\\n\\n# Generate embeddings\\nembeddings = model(audio_data)", \'performance\': {\'dataset\': \'YAMNet\', \'accuracy\': \'81.6% top-1 accuracy on AudioSet\'}, \'description\': \'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\'}', metadata={})]


 Match products descriptions to our customer search query in our e-commerce store. First, encode them into vectors.\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/ssd_mobilenet_v2/2\')", \'api_arguments\': [{\'name\': \'model_id\', \'type\': \'string\', \'description\': \'The ID of the model to load 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/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\n\\n# Load an image, preprocess it, and run the detector\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.image.resize(input_image, (300, 300))\\ninput_image = tf.expand_dims(input_image, 0)\\n\\n# Run the detector\\noutput = detector(input_image)\\n\\n# Process the output\\nfor i in range(output[\'num_detections\']):\\n    if output[\'detection_scores\'][i] > 0.5:\\n        print(\'Detected object:\', output[\'detection_class_entities\'][i], \'with confidence:\', output[\'detection_scores\'][i])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP@0.5IOU\'}, \'description\': \'A pre-trained object detection model that can detect objects in images using TensorFlow Hub\'}', metadata={})]


 For a fashion e-commerce website, I want to build a recommendation system on clothing items based on their similarity. How do I extract feature vectors from clothing images for comparison?\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={})]


 For my language model, I require preprocessed text. Use an appropriate method to assist me in preparing the input text for further processing.\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 are trying to cluster movie reviews from the Internet. First, we want to transform the reviews into fixed dimension vectors.\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={})]