 I am working on a project where I need to cluster similar images of street art. How can I get the relevant feature vectors from these images for clustering?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate feature vectors for images\', \'api_name\': \'imagenet_mobilenet_v3_large_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5\')", \'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_v3_large_100_224/feature_vector/5\', input_shape=(224, 224, 3), output_shape=(1280,))\\n])\\n\\n# Load an image and preprocess it\\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\\n# Generate feature vector\\nfeature_vector = model.predict(image_array)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'75.3%\'}, \'description\': \'This API provides a pre-trained image feature vector model, MobileNetV3-Large, from TensorFlow Hub. The model is trained on the ImageNet dataset and can be used to generate feature vectors for images of size 224x224 with 3 channels.\'}', metadata={})]


 We want to develop a simple application where users can take a photo of any object and find the description of it. Help us with creating a model that extracts features from the object's image.\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_140_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[batch_size, 224, 224, 3]\', \'output_shape\': \'[batch_size, 1792]\'}, \'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_140_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   trainable=False),\\n    tf.keras.layers.Dense(1, activation=\'sigmoid\')\\n])\\n\\nmodel.compile(optimizer=\'adam\', loss=\'binary_crossentropy\', metrics=[\'accuracy\'])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'80.3%\'}, \'description\': \'A pre-trained image feature vector model that extracts features from images using MobileNetV2 architecture with a width multiplier of 1.4 and an input size of 224x224 pixels. The model is trained on the ImageNet dataset and achieves 80.3% accuracy.\'}', metadata={})]


 I have a set of food images and I want to create a model that differentiates between different types of cuisine.\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={})]


 Show me the right API call to generate image feature vectors using MobileNet V2 architecture so that I can perform a classification task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Load and use a pre-trained image feature vector model\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.load(\'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 = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')\\n\\nimage = tf.zeros([1, 224, 224, 3])\\nfeatures = model(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'This module uses the MobileNet v2 architecture to generate image feature vectors. The model has been pre-trained on the ImageNet dataset and can be used for various image classification tasks.\'}', metadata={})]


 My dog is always getting into things that can be dangerous. Can you help me identify the object my dog is about to eat?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract feature vector from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\', output_shape=[1280])", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\', output_shape=[1280]),\\n    tf.keras.layers.Dense(1)\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\\nimage = tf.expand_dims(image, 0)\\n\\nfeature_vector = model.predict(image)\\nprint(feature_vector)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API allows you to extract a 1280-dimensional feature vector from an image using the MobileNetV2 architecture, which is pre-trained on the ImageNet dataset. The feature vector can be used for various tasks, such as image classification, object detection, and transfer learning.\'}', metadata={})]


 A photo is sent to me, and I would like to know what objects are in the photo.\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={})]


 Analyze customer reviews and identify positive and negative sentiments, so please convert the text reviews into 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={})]


 Recommend a method to extract image feature vectors with MobileNetV2. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using a pre-trained image feature vector model\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'False\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(1)])\\n\\nmodel.build([None, 224, 224, 3])\\nmodel.summary()", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. This model can be used for transfer learning to extract meaningful features from images and fine-tune on a specific classification task.\'}', metadata={})]


 Write an example code to use a pre-trained model to convert a sentence \"My cat is sitting on the porch\" into high-dimensional vectors.\n###Input: My cat is sitting on the porch\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image segmentation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image segmentation model\', \'api_name\': \'deeplabv3_257_mv_gpu\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')", \'api_arguments\': {\'input\': \'Image tensor\', \'output\': \'Segmentation mask tensor\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')\\nimage_tensor = tf.image.resize(image, (257, 257))\\nsegmentation_mask = model(image_tensor)", \'performance\': {\'dataset\': \'PASCAL VOC 2012\', \'accuracy\': \'0.899\'}, \'description\': \'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\'}', metadata={})]


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


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


 I have a list of sentences and I want to find which ones are most similar. How can I do that efficiently?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Universal Sentence Encoder\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'A list of sentences or phrases\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.80\'}, \'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={})]


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


 I have hundreds of images and I want to group them in categories, but first I need to extract features from those 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={})]


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


 Find out what's in the image taken by a wildlife photographer, so we can determine the main subject of the picture.\n###Input: An image taken by a wildlife photographer.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', input_shape=[224, 224, 3], output_shape=[1280])\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\'}', metadata={})]


 Can you tell me the type of food in this 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/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={})]


 Identify the semantic theme of the given list of customer support tickets.\n###Input: ['The payment did not go through.', 'Issues with the payment method.', 'I need help with my payment.', 'Unable to complete payment.', 'Payment error encountered.']\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 want a system that will read the image of my room and identify all the objects present in the room along with their confidence score.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'ssd_mobilenet_v2\', \'api_call\': "hub.load(\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\')", \'api_arguments\': \'image, score_threshold\', \'python_environment_requirements\': \'Python 3, TensorFlow 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\ndetector = hub.load(\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\')\\nresult = detector(image, score_threshold=0.5)", \'performance\': {\'dataset\': \'Open Images v4\', \'accuracy\': \'mAP@0.5IOU = 32.8%\'}, \'description\': \'A pre-trained SSD MobileNet V2 model for object detection in images, trained on the Open Images v4 dataset. Detects objects and outputs their bounding boxes and class labels with associated confidence scores.\'}', metadata={})]


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


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


 I need an easy way to sort out thousands of travel photos by recognizing landmarks and other objects.\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={})]


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


 I need to analyze user feedback and compare it to a list of predefined topics.\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={})]


 We need to recognize the flowers in the pictures from our botanical garden.\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)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image_path\', 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)\\nclass_index = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture, which can recognize 1000 different classes from the ImageNet dataset.\'}', metadata={})]


 I want a solid text preprocessing method to make my model more efficient in dealing with different texts.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading a text preprocessing model\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'model_url\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, World!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'Not specified\'}, \'description\': \'Load and use the Universal Sentence Encoder model for text preprocessing in TensorFlow Hub\'}', metadata={})]


 A fashion store wants to categorize newly arrived clothes into different classes. Please make sure the right category is assigned to each clothing item.\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={})]


 To match clothes in my wardrobe, I need to extract features from images and organize them based on 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={})]


 We will launch a new product that relies on user reviews, analyze reviews, and generate insights based on their sentiments.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Video classification model\', \'api_name\': \'Video_classification_model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/Video_classification_model_id/1\')", \'api_arguments\': [], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Kinetics-400\', \'accuracy\': \'\'}, \'description\': \'A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content.\'}', metadata={})]


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


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


 I want to create a phone cover design recommendation system. For that, I need to get some features of cover 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={})]


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


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


 For arranging my documents, I want to compute the semantic similarity between a set of sentences.\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 would like to group similar user reviews together. To do this, I need to transform the text reviews into high dimensional vectors.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained Image Classifier\', \'api_name\': \'mobilenet_v2\', \'api_call\': "tf.keras.Sequential([hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\')])", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', \'output_shape\': \'[1001]\'}, \'python_environment_requirements\': {\'tensorflow\': \'2.x\', \'tensorflow_hub\': \'0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = tf.keras.Sequential([hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', output_shape=[1001])])\\nmodel.build([None, 224, 224, 3])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture on TensorFlow Hub. It can be used to classify images into 1000 different categories with high accuracy.\'}', metadata={})]


 I need to find if a word is a positive or negative sentiment. I need to process my text before feeding it to my sentiment analysis model.\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={})]


 I have a collection of photos I took while on vacation, and I want to know what objects are in each photo.\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={})]


 Create a list of similar art pictures by comparing the feature vectors of the existing art 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={})]


 Discover a person's sentiment after reading their review\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={})]


 We have several images of clothing items and want to differentiate them by their features. Generate a model that will create a feature vector for each item.\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={})]


 Create a mobile app that takes a photo of a plant and identifies its species. To do this, use an image classifier to process the photo.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Audio processing\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'Load and use an audio embedding model from TensorFlow Hub to process audio data.\'}', metadata={})]


 I want to use an API to find the degree of similarity between sentences. Firstly, I need to obtain their high-dimensional vector representations.\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={})]


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


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


 Create a document classifier, and identify the genre of a movie poster by classifying the image content.\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={})]


 Develop a model that will extract feature vectors from images, which we will use later 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\': \'image_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', trainable=False)", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', trainable=False),\\n    tf.keras.layers.Dense(1000, activation=\'softmax\')\\n])\\n\\nmodel.build([None, 299, 299, 3])\\nmodel.summary()", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'This API provides a compact representation of images using the Inception V3 model from TensorFlow Hub. It extracts feature vectors from images, which can be used for various machine learning tasks such as classification and clustering.\'}', metadata={})]


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


 Explain how to create a image classification model using a pre-trained MobileNetV2 model as a feature extractor.\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={})]


 There are comments written by users in different languages, and we want to calculate the similarity between pairs of comments.\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={})]


 Analyze large data sets containing various customer reviews. Transform the text from these reviews into vectors for further analysis like sentiment, similar phrases, and other natural language processing tasks.\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 am visiting the Metropolitan Museum of Art and want to quickly identify the category or genre of the artwork by taking a picture of it.\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 sentences\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "import tensorflow_hub as hub; embed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\'); embeddings = embed([\'Hello world\', \'I am learning how to use the Universal Sentence Encoder\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'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. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs.\'}', metadata={})]


 Could you provide an embedding for my sentence to be used in semantic similarity analysis?\n###Input: Someone is learning how to use a Python library.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text for machine learning tasks\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'A list of strings\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'hello world\', \'my name is John\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'0.803\'}, \'description\': \'A text embedding model that converts text into high-dimensional vectors for use in various machine learning tasks. The Universal Sentence Encoder encodes text into 512-dimensional vectors that can be used for tasks such as semantic similarity, classification, or clustering.\'}', metadata={})]


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


 We want to create an app that can recognize furniture in images. Find an API that can help us.\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={})]


 Transform a list of product titles into numerical vectors to perform sentiment analysis.\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_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'description\': \'A pre-trained model for embedding text into high-dimensional vectors that can be used for a wide range of tasks such as sentiment analysis, semantic similarity, and classification.\'}', metadata={})]


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


 We are building an app to help people categorize their personal photos into categories. We need to extract features from 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\': "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={})]


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


 I want to find the similarity between user-generated restaurant reviews, so I need to transform these reviews into an appropriate format.\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={})]


 Compile a report on ongoing political conflicts in the Middle East, using an embedding to identify the key topics.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image pose detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detects human poses in images\', \'api_name\': \'movenet\', \'api_call\': "hub.load(\'https://tfhub.dev/google/movenet/singlepose/lightning/3\')", \'api_arguments\': \'image_tensor\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/google/movenet/singlepose/lightning/3\')\\nimage_tensor = ... # Load image tensor\\noutputs = model(image_tensor)\\nkeypoints = outputs[\'output_0\'].numpy()", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'70.4 mAP\'}, \'description\': \'MoveNet is a family of efficient and accurate pose detection models designed for real-time applications. This model detects 17 key points of a single person in an image.\'}', metadata={})]


 Our school wants to analyse students' Reddit comments to find relevant topics. We need vectors to represent their comments for further processing.\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={})]


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


 I am building a chatbot for a cosmetics store and want to understand the semantic similarity between different customer queries.\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={})]


 From a collection of images, we want to extract a feature vector for each image using MobileNetV2.\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={})]


 Find and display Cosine Similarity between two different 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={})]


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


 Transform the following text into its corresponding high-dimensional vector: \"I love exploring new places and trying different cuisines.\"\n###Input: I love exploring new places and trying different cuisines.\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: A tensor of strings, output: A tensor of shape (num_examples, 512)\', \'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 love machine learning.\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'SNLI, MultiNLI, SICK\', \'accuracy\': \'86.3%, 76.6%, 88.0%\'}, \'description\': \'A pre-trained model for encoding text into a 512-dimensional vector. Useful for various NLP tasks like semantic similarity, sentiment analysis, and text classification.\'}', metadata={})]


 To build an image recognition system, we need a pre-trained model to extract features from images. Provide the API call for this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extracts feature vectors from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\'}, \'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/inception_v3/feature_vector/5\')\\n])\\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\\nfeatures = model.predict(image)\\nprint(features)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'78.1%\'}, \'description\': \'This API provides a pre-trained Inception V3 model for extracting feature vectors from images. It is built on TensorFlow Hub and requires TensorFlow and TensorFlow Hub packages. The example code demonstrates how to load an image, preprocess it, and extract features using the API.\'}', metadata={})]


 I have a list of sentences that I want to preprocess and make ready to be fed to a machine learning model. Can you do that?\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\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'model_id\': \'URL of the pre-trained 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/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\n\\n# Load an image and preprocess it\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\', target_size=(224, 224))\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\n\\n# Make predictions\\npredictions = model(input_image)\\n\\n# Decode the predictions\\nclass_names = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=1)\\nprint(class_names)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'This API loads a pre-trained image classification model from TensorFlow Hub, allowing users to classify images using the loaded model. The example demonstrates how to load a MobileNetV2 model, preprocess an input image, make predictions, and decode the predictions to obtain class names.\'}', metadata={})]


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


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


 I have a few news articles and I want to see if they're similar. I need a tool to determine the similarity between them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'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 find similar listings for an Airbnb property, can you find its vector representation for further comparison?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract feature vector from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\', output_shape=[1280])", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\', output_shape=[1280]),\\n    tf.keras.layers.Dense(1)\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\\nimage = tf.expand_dims(image, 0)\\n\\nfeature_vector = model.predict(image)\\nprint(feature_vector)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API allows you to extract a 1280-dimensional feature vector from an image using the MobileNetV2 architecture, which is pre-trained on the ImageNet dataset. The feature vector can be used for various tasks, such as image classification, object detection, and transfer learning.\'}', metadata={})]


 Extract useful information from an image for further processing by minimizing the input.\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\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\'),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, useful for extracting features from images. The model is trained on ImageNet dataset and has an accuracy of 71.9%.\'}', metadata={})]


 We need to incorporate an API to differentiate between species of flowers.\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={})]


 We have customer reviews and need to categorize them. Convert the text into a compatible form for machine learning analysis.\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={})]