 Our team has an image classification project. First, we need to extract features from our images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract image features", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[batch_size, 224, 224, 3]", "output_shape": "[batch_size, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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                   trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model, MobileNetV2, for TensorFlow Hub that extracts features from images. The input images should be 224x224 pixels with 3 channels (RGB). The output is a 1280-dimensional feature vector. The model is trained on the ImageNet dataset."}}


 I want to cluster customer chat messages so that their related concerns are addressed in a more meaningful way.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello, world!', 'How are you?'])\nprint(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "pearson_correlation=0.803"}, "description": "A model that encodes text into 512-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks"}}


 I want to build a text classifier for articles that could also find articles with similar content. Can you suggest a way to encode the text of the articles?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text", "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 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 There is a need to segment images of a park area with trees and people, and distinguish the trees from people.\n
 Use this API documentation for reference:  {"domain": "Image segmentation", "framework": "TensorFlow Hub", "functionality": "Semantic image segmentation", "api_name": "deeplabv3", "api_call": "hub.KerasLayer('https://tfhub.dev/tensorflow/deeplabv3/1', output_key='SemanticPredictions')", "api_arguments": {"input": "A 3-channel image tensor", "output": "A tensor with semantic labels"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nimage = tf.keras.Input(shape=(None, None, 3), dtype=tf.uint8, name='image_input')\nsegmentation = hub.KerasLayer('https://tfhub.dev/tensorflow/deeplabv3/1', output_key='SemanticPredictions')(image)\nmodel = tf.keras.Model(inputs=image, outputs=segmentation)", "performance": {"dataset": "PASCAL VOC 2012", "accuracy": "82.1% mean intersection-over-union (mIOU)"}, "description": "DeepLabv3 is an image segmentation model for TensorFlow Hub that assigns semantic labels to every pixel of an input image. It is based on the DeepLabv3 architecture and trained on the PASCAL VOC 2012 dataset."}}


 Our security firm needs an image analysis solution to detect cars and pedestrians from CCTV images.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Object detection in images", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1'\ndetector = hub.load(model_id)\nimage = tf.image.decode_jpeg(tf.io.read_file('image.jpg'))\nresult = detector(image[np.newaxis, ...])", "performance": {"dataset": "OpenImages V4", "accuracy": "Not specified"}, "description": "Detect objects in an image using TensorFlow Hub and the Faster R-CNN model with Inception ResNet V2"}}


 Can you recommend an API that helps to convert a research article abstract into a high-dimensional vector which we can then use to find similar articles?\n
 Use this API documentation for reference:  {"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": "embeddings = embed(['Hello world'])", "performance": {"dataset": "STS Benchmark", "accuracy": "Mean Squared Error (MSE) of 0.30 on the development set"}, "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."}}


 I need to monitor a warehouse's inventory and identify objects kept in the storage area.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in images", "api_name": "faster_rcnn_inception_resnet_v2_atrous_oid_v4", "api_call": "hub.load('https://tfhub.dev/google/faster_rcnn/inception_resnet_v2/atrous/oid_v4/1')", "api_arguments": {"image_tensor": "A tensor of shape [batch_size, height, width, 3] containing the input images."}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/faster_rcnn/inception_resnet_v2/atrous/oid_v4/1')\nimage_tensor = tf.zeros([1, 640, 640, 3])\noutput = model(image_tensor)", "performance": {"dataset": "Open Images Dataset v4", "accuracy": "mAP@0.5IOU = 0.480"}, "description": "A pre-trained Faster R-CNN model with Inception-ResNet-v2 feature extractor for object detection on the Open Images Dataset v4."}}


 Create a feature extraction model to recognize 10 different types of images from a given dataset with sizes 224x224x3.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "model = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                    input_shape=(224, 224, 3),\n                    trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A Keras layer that wraps a pre-trained MobileNetV2 model for feature extraction. It takes an input image of size 224x224x3 and outputs a 1280-dimensional feature vector."}}


 I need a quick way to classify images on my phone. Can you use a computationally efficient model to do this?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification using pre-trained models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": {"model_id": "string"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\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# Make a prediction\nprediction = model(image_array)\n# Decode the prediction\ndecoded_prediction = tf.keras.applications.mobilenet_v2.decode_predictions(prediction.numpy())", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "Pre-trained image classification model using MobileNetV2 architecture on the ImageNet dataset."}}


 Provide me with the facility to determine the similarity between two sentences using encoded text.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "URL of the model", "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": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 We want to recognize different types of dogs in images for an upcoming app. Help us with a model to get the image features.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "mobilenet_v2_100_224", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow_hub as hub\nimport tensorflow as tf\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                   trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained MobileNetV2 model that takes an input image of shape (224, 224, 3) and outputs a 1280-dimensional feature vector. Useful for image classification, object detection, and other computer vision tasks."}}


 I am planning my garden, please help me segment plants in an image for better landscape planning.\n
 Use this API documentation for reference:  {"domain": "Image segmentation", "framework": "TensorFlow Hub", "functionality": "Load and use pre-trained image segmentation models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/default/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["pip install tensorflow-hub"], "example_code": "import tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/default/1')\nsegmentation = model(image_tensor)", "performance": {"dataset": "PASCAL VOC 2012", "accuracy": "0.91"}, "description": "TensorFlow Hub provides pre-trained image segmentation models that can be loaded and used for various tasks such as semantic segmentation. The example code demonstrates how to load a DeepLabV3 model and perform segmentation on an input image tensor."}}


 Find out the type of birds in my backyard based on smartphone photos I took.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification model", "api_name": "imagenet_mobilenet_v2_100_224_classification", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))\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": "71.8%"}, "description": "A pre-trained image classification model using MobileNet V2 architecture and trained on ImageNet dataset."}}


 I'm working on a project that requires text clustering, and I need to convert a list of product reviews into vector representations in order to analyze the semantic similarities between them.\n
 Use this API documentation for reference:  {"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 list of strings to be embedded"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!', 'Embedding with universal-sentence-encoder'])", "performance": {"dataset": "Various", "accuracy": "N/A"}, "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. It is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 I found a bird on my trip outside, and I took a photo. Can you identify it?\n
 Use this API documentation for reference:  {"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\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\nimage = tf.random.normal([1, 224, 224, 3])\npredictions = model(image)\n", "performance": {"dataset": "ImageNet", "accuracy": "Varies depending on the model"}, "description": "A collection of pre-trained image classification models available on TensorFlow Hub, which can be used to classify images into various categories."}}


 Figure out the category of my image by using a pre-trained Inception model.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading and using pre-trained models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(299, 299))\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.expand_dims(input_image, 0)\npredictions = model(input_image)\n", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "description": "This API is used to load and use pre-trained image classification models from TensorFlow Hub for tasks like object recognition and scene understanding."}}


 Images have come from users, and we need to recognize their categories for better curation.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading and using pre-trained 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\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\n", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Loads a pre-trained image classification model from TensorFlow Hub and uses it for inference."}}


 We have several support tickets in our system, find a way to cluster them to find the most occurring topics.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text data for machine learning models", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "List of sentences or phrases"}, "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', 'Embedding sentences with TensorFlow Hub'])", "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. It is trained on a variety of data sources and a variety of tasks with the aim of dynamically accommodating a wide variety of natural language understanding tasks."}}


 We need to create an image classifier using transfer learning. I want to get feature vectors from pre-trained models.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet-feature-vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/feature_vector/1')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/feature_vector/1", "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\ninput = tf.keras.layers.Input(shape=(224, 224, 3))\nfeatures = hub.KerasLayer('https://tfhub.dev/google/imagenet/feature_vector/1', trainable=False)(input)\nmodel = tf.keras.Model(inputs=input, outputs=features)", "performance": {"dataset": "ImageNet", "accuracy": "N/A"}, "description": "This API provides a pre-trained image feature vector model for extracting features from images using TensorFlow Hub. The model is based on the ImageNet dataset and can be used for various image-related tasks such as classification, detection, and segmentation."}}


 Can you help me to create fixed-sized vector representation for every input phrases in a dataframe?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text", "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": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "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!'])", "performance": {"dataset": "STS benchmark", "accuracy": "0.803"}, "description": "The Universal Sentence Encoder encodes text into a fixed-size embedding vector. It is trained on a variety of data sources and can be used for multiple NLP tasks such as text classification, semantic similarity, and clustering."}}


 I need an API to help me classify pet images. I'd like to use the extracted features from these images to make better predictions.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extracting feature vector 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/5')", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/5',\n                   input_shape=(224, 224, 3),\n                   trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])\nmodel.compile(optimizer='adam',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\nmodel.fit(x_train, y_train, epochs=5)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture on ImageNet dataset. It can be used for various image classification tasks by adding a classification head on top of the feature vector."}}


 Help me with identifying the object present in my new picture, using a ready-to-use model.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification models", "api_name": "imagenet-mobilenet_v2_130_224-classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')\n# Load and preprocess an image\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.expand_dims(input_image, 0)\ninput_image = tf.keras.applications.mobilenet_v2.preprocess_input(input_image)\n# Predict the class of the image\npredictions = model(input_image)\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\nprint('Predicted class:', predicted_class)", "performance": {"dataset": "ImageNet", "accuracy": "85.4%"}, "description": "A pre-trained image classification model based on MobileNetV2 architecture with 130 depth multiplier and 224x224 input size, trained on ImageNet dataset."}}


 I want to analyze images of my coin collection and build a classifier to identify coins. Provide me with a method to extract image features.\n
 Use this API documentation for reference:  {"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\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])\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)\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."}}


 We are doing data pre-processing for a project and our aim is to understand if the given two product titles are semantically similar or not. We first need to convert these product titles into high-dimensional vectors.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "input: List of text strings", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!', 'I am learning about text embedding'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 I'm working on a plant recognition app, and I want to classify a plant's image taken by users.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image Classification", "api_name": "imagenet_mobilenet_v2_100_224_classification", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": "URL: https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'),\n])\n", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset with 100% depth and 224x224 input size."}}


 How can I calculate the similarity between two sentences based on their embeddings?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data", "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')\ntext = ['This is a sample text', 'Another sample text']\nembeddings = embed(text)", "performance": {"dataset": "STS", "accuracy": "Pearson correlation coefficient of 0.8"}, "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."}}


 I have an image of my backyard. Identify if the BBQ grill is active.\n
 Use this API documentation for reference:  {"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.keras.preprocessing.image.load_img('path/to/image.jpg')\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.expand_dims(image_array, 0)\nresult = detector(input_image)\n", "performance": {"dataset": "COCO", "accuracy": "Unknown"}, "description": "A pre-trained TensorFlow Hub model for detecting objects in images using the SSD MobileNet V2 architecture."}}


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


 We are interesting in analyzing text messages to recommend interesting articles to our users. For this, we need to create an embedding for each message.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into a high-dimensional vector", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text: A list of strings to be embedded"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!'])", "performance": {"dataset": "STSbenchmark", "accuracy": "Pearson correlation of 0.803"}, "description": "The Universal Sentence Encoder encodes text into a high-dimensional vector that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Could you help me to find a quick way to rate similarities between my blog posts?\n
 Use this API documentation for reference:  {"domain": "Text classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"model_url": "https://tfhub.dev/google/universal-sentence-encoder/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "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": "STSbenchmark", "accuracy": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks."}}


 I have a lot of vacation pictures and want to find those with similar content. Can you help me?\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature vector extraction", "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": {"image_size": "224x224", "input_type": "float32", "output_type": "float32", "batch_size": "variable"}, "python_environment_requirements": {"tensorflow": "2.0", "tensorflow_hub": "0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')\n# Load and preprocess an image\nimage = tf.keras.preprocessing.image.load_img('path/to/your/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# Extract features\nfeatures = model(input_image)", "performance": {"dataset": "ImageNet", "accuracy": "71.0"}, "description": "This API allows you to extract feature vectors from images using the MobileNetV2 model trained on ImageNet. The feature vectors can be used for various machine learning tasks such as image classification, object detection, and more."}}


 I am doing holiday gift shopping online, and I want to know what category a picture of a gift item belongs to.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained model", "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, TensorFlow Hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\nprint(model(tf.zeros([1, 224, 224, 3])))", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained MobileNetV2 model for image classification from TensorFlow Hub"}}


 Can you find an efficient image classification model to use on my smartphone for recognizing food items in my fridge?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained models for image classification", "api_name": "imagenet_mobilenet_v2_130_224_classification", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')", "api_arguments": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4", "trainable": "Optional, default is False"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer(\n        handle='https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4',\n        input_shape=(224, 224, 3),\n        trainable=False\n    )\n])\nimage = tf.keras.preprocessing.image.load_img('path_to_your_image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model.predict(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "80.3%"}, "description": "A pre-trained image classification model using the MobileNetV2 architecture, trained on the ImageNet dataset. It is designed for efficient on-device performance with reduced computational and memory requirements."}}


 Our art gallery is conducting an exhibition and we want to classify the images of paintings into different genres.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "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\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\n", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the model id."}}


 We are setting up an AI system to help classify images from outdoor events into different categories based on their content.\n
 Use this API documentation for reference:  {"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": "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": "", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on the ImageNet dataset."}}


 I am creating a shopping app and I want to suggest similar looking items from our database using item's image.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract image feature vectors", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input": "preprocessed image", "output": "feature vector"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')\n])\nimage = tf.keras.Input(shape=(224, 224, 3))\nfeature_vector = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture on ImageNet dataset. It takes a preprocessed image as input and returns a 1280-dimensional feature vector."}}


 I have an image and I want to recognize the object in this image, so I need to access a pre-trained image classification model.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading a pre-trained model for image classification", "api_name": "imagenet_mobilenet_v2_130_224_classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')", "api_arguments": "URL of the pre-trained model", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "description": "Load a pre-trained MobileNetV2 model for image classification using TensorFlow Hub."}}


 I have taken some pictures of my dog, and I want to organize them in a way that those which are similar to each other are together.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet-mobilenet_v2-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\nmodel = 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])\nfeatures = model.predict(images)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture on ImageNet dataset. It can be used for feature extraction from images."}}


 We want to find the most similar questions from user inputs to give them the best answer.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text", "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!', 'My name is John Doe.'])", "performance": {"dataset": "STS Benchmark", "accuracy": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 I am working on a product recommendation system for a flower shop. Extract feature vectors from flower images to be used in the system.\n
 Use this API documentation for reference:  {"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"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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                    trainable=False),\n    tf.keras.layers.Dense(5, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model based on MobileNet V2 architecture for extracting features from images. The model is trained on ImageNet dataset and has an accuracy of 71.9%."}}


 Recommend a playlist to me based on the audio characteristics of this song clip I'm sending you.\n
 Use this API documentation for reference:  {"domain": "Audio embedding", "framework": "TensorFlow Hub", "functionality": "Generate embeddings for audio files", "api_name": "audio_embedding_model", "api_call": "hub.load('https://tfhub.dev/google/yamnet/1')", "api_arguments": {"audio_inputs": "Tensor of shape [batch_size, num_samples] containing audio signals"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/yamnet/1')\n# Load an audio file\naudio_data = ... # Load audio data as a tensor\n# Generate embeddings\nembeddings = model(audio_data)", "performance": {"dataset": "YAMNet", "accuracy": "81.6% top-1 accuracy on AudioSet"}, "description": "This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more."}}


 We are building a car recognition system. We need to extract features from car images to feed them into the classifier.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "inception_v3", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5')", "api_arguments": {"input": "image", "output": "feature_vector"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow_hub as hub\nimport tensorflow as tf\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5',\n                   input_shape=(299,299,3))\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, 0)\nfeatures = model.predict(image)", "performance": {"dataset": "ImageNet", "accuracy": "77.9%"}, "description": "Feature extraction from images using Inception V3 model trained on ImageNet dataset"}}


 Please help me with a system that studies product descriptions so that we can group products with similar features.\n
 Use this API documentation for reference:  {"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."}}


 I need to find features in the images to perform image classification.\n
 Use this API documentation for reference:  {"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"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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), trainable=False)\n])\nimage = tf.keras.preprocessing.image.load_img('path_to_your_image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\nfeatures = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This API provides a Keras layer that extracts features from images using the MobileNetV2 model trained on ImageNet dataset. It can be used for various tasks such as image classification, object detection, and transfer learning."}}


 We need to find similar news articles from a large corpus. Please embed them into high-dimensional vectors.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data for machine learning", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "Text data"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.8"}, "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": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks."}}


 Discover all the elements present within a photograph of a street scene.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in images", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1')", "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/centernet/resnet50v1_fpn_512x512/1'\ndetector = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('image.jpg')\nimage = tf.keras.preprocessing.image.img_to_array(image)\nresult = detector(image)", "performance": {"dataset": "COCO", "accuracy": "mAP (mean Average Precision)"}, "description": "Detect objects in images using TensorFlow Hub and a pre-trained CenterNet model with ResNet50V1 FPN backbone"}}


 We own a car dealer business and want to develop an app that categorizes different car categories based on uploaded images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract high-level features from images", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]", "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\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), trainable=False),\n    tf.keras.layers.Dense(1, activation='sigmoid')\n])\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It extracts high-level features from images and can be used for tasks like image classification, object detection, and transfer learning."}}


 Given images of painted landscapes, we want to find similar images based on their visual features. Which API can we use for extracting the feature vector?\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/5')", "api_arguments": "input_shape, output_shape", "python_environment_requirements": "tensorflow, tensorflow-hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/5',\n                    input_shape=(224,224,3),\n                    output_shape=(1280,))\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained MobileNetV2 model for extracting image feature vectors using TensorFlow Hub."}}


 We need to know what is in these photos so we can report to our officials.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained model for classifying images", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\n# Load and preprocess an image\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.keras.applications.inception_v3.preprocess_input(image)\n# Make a prediction\npredictions = model.predict(tf.expand_dims(image, 0))\n# Decode the predictions\nclass_names = tf.keras.applications.inception_v3.decode_predictions(predictions, top=1)\nprint(class_names)", "performance": {"dataset": "ImageNet", "accuracy": "0.779"}, "description": "A pre-trained image classification model using Inception V3 architecture, trained on the ImageNet dataset."}}


 We have a collection of animal photos, and we want to obtain a feature vector from each photo in order to properly organize them.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract features from images", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture. It takes an input image of shape [224, 224, 3] and outputs a feature vector of shape [1280]."}}


 I took a picture of a street and want to separate the vehicles, people, and buildings in the scene.\n
 Use this API documentation for reference:  {"domain": "Image segmentation", "framework": "TensorFlow Hub", "functionality": "DeepLab v3", "api_name": "deeplabv3", "api_call": "hub.load('https://tfhub.dev/tensorflow/deeplabv3/1')", "api_arguments": {"input_tensor": "3-D float32 tensor", "output_tensor": "4-D float32 tensor"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/tensorflow/deeplabv3/1')\ninput_image = tf.constant(image, dtype=tf.float32)\noutput_image = model(input_image)", "performance": {"dataset": "PASCAL VOC 2012", "accuracy": "77.21%"}, "description": "DeepLab v3 is an image segmentation model for TensorFlow Hub that segments images into different classes. It is trained on the PASCAL VOC 2012 dataset and achieves an accuracy of 77.21% on the validation set."}}


 I need to quickly classify images of various objects on my phone. Which pre-trained model should I use for efficient processing in a mobile environment?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "mobilenet_v2_100_224", "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": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4', output_shape=[1001])\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)\npredictions = model.predict(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNet V2 architecture with 1001 output classes available on TensorFlow Hub."}}


 I want to enhance my wildlife images and need to extract an image feature vector to use in advanced image processing tasks.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4"}, "python_environment_requirements": ["tensorflow", "tensorflow-hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nfeature_extractor = hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')\nimage_input = tf.keras.Input(shape=(224, 224, 3), dtype=tf.float32, name='input_image')\nfeatures = feature_extractor(image_input)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset. It is designed for efficient on-device image classification and related tasks."}}


 My computer has folders with many images, and I want to classify these images into categories, such as animals, buildings, and so on.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the model_id"}}


 Show me how to classify an image of a dog using a pre-trained model with TensorFlow Hub and Inception V3 architecture.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Model for classifying images", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\npredictions = model(image)\n", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "description": "A pre-trained model for image classification using TensorFlow Hub and Inception V3 architecture."}}


 Recommend a suitable movie based on a given movie's plot. For better recommendations, obtain the plot's embedding.\n
 Use this API documentation for reference:  {"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": {"input": "text"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.8"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\ntext = ['This is a sample text.']\nembeddings = embed(text)\nprint(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "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."}}


 Obtain image features from an image dataset to use in a product recommendation system\n
 Use this API documentation for reference:  {"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"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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                   trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\nhistory = model.fit(train_batches, epochs=10, validation_data=validation_batches)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This module uses MobileNet v2 architecture for image feature vector extraction. It takes an input image of shape (224, 224, 3) and outputs a 1280-dimensional feature vector."}}


 I took a photo of a car and I want to extract its features in a vector form to use it for classification tasks later.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Generate feature vectors from images", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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                   trainable=False)\n])\n# Load image\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# Generate feature vector\nfeatures = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This API provides a pre-trained MobileNetV2 model for generating feature vectors from images. The feature vectors can be used for various machine learning tasks, such as image classification, clustering, or similarity search."}}


 Calculate the similarity between two sentences and determine if they have the same meaning.\n
 Use this API documentation for reference:  {"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": "text_input", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient > 0.8"}, "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."}}


 We have an image dataset and want a pre-trained model as a feature extractor.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract features from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = 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])\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\nfeatures = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained model that extracts 1280-dimensional feature vectors from 224x224 RGB images using MobileNetV2 architecture."}}


 We are creating a custom image search engine that can give similar image results. Extract features from images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract image features", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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                   trainable=False)\n])\n# Example input: a batch of images with shape [batch_size, 224, 224, 3].\nimages = tf.random.uniform((4, 224, 224, 3))\n# Extract features for the batch of images.\nfeatures = model(images)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model using MobileNet V2 architecture on ImageNet dataset, with 100% depth multiplier and 224x224 input image resolution. Useful for extracting features from images."}}


 Can you compare two sentences similarly using the embedding generation?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "input_text", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!'])\nprint(embeddings)", "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."}}


 Provide me with an encoded version of the following review, so I can later use it for sentiment analysis: \"What an amazing product! I've been using it for a few weeks now and couldn't be happier.\"\n###Input: \"What an amazing product! I've been using it for a few weeks now and couldn't be happier.\"\n
 Use this API documentation for reference:  {"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": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!'])", "performance": {"dataset": "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."}}


 I have several images of various objects; can you check if there are any boats in them?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/4'\nmodel = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(299, 299))\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.expand_dims(input_image, 0)\npredictions = model(input_image)\npredicted_class = tf.argmax(predictions[0]).numpy()", "performance": {"dataset": "ImageNet", "accuracy": "78.1%"}, "description": "This example demonstrates how to load a pre-trained Inception V3 model from TensorFlow Hub and use it to classify an image. The model has been trained on the ImageNet dataset and achieves 78.1% accuracy."}}


 We are building a software that can recognize similar objects in a series of images. We need feature vectors for each image.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "(224, 224, 3)"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "model = tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4', input_shape=(224, 224, 3), trainable=False), tf.keras.layers.Dense(1)])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture for extracting features from images. The model is trained on ImageNet dataset and can be used for various computer vision tasks."}}


 Can you help me obtain the embeddings of a list of sentences for a research on similarity between news headlines?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello, world!', 'How are you?'])\nprint(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "pearson_correlation=0.803"}, "description": "A model that encodes text into 512-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks"}}


 I have an image dataset and I want to compute feature vectors to perform similarity computation using nearest neighbor searching.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract feature vectors from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input": "image_tensor", "output": "feature_vector"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer(\n        'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n        input_shape=(224, 224, 3)\n    )\n])\nimage_tensor = tf.zeros(shape=(1, 224, 224, 3))\nfeature_vector = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained MobileNetV2 model that takes an image tensor as input and returns a 1280-dimensional feature vector."}}


 A dating website wants to auto-match users based on the similarity of their profiles. How can we do that using text embeddings?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!'])\nprint(embeddings)", "performance": {"dataset": "STSbenchmark", "accuracy": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 We are creating a mobile app where users can take photos of objects, and our app should identify what the object is.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained model for image classification", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\npredictions = model(image)\n", "performance": {"dataset": "ImageNet", "accuracy": "77.9%"}, "description": "A pre-trained model for image classification using Inception V3 architecture and trained on ImageNet dataset."}}


 We are building a Tinder-clone app for pets adoption. When user swipes, we want to show them the most similar pets based on images. Therefore, in order to find similar pets, we need to get the feature vector of each pet photo.\n
 Use this API documentation for reference:  {"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": "[None, 224, 224, 3]", "output_shape": "[None, 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',\n                    input_shape=(224,224,3),\n                    trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\n              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained model that takes an image input and outputs a 1280-dimensional feature vector representing the image, which can be used for various tasks such as classification, clustering, and similarity search."}}


 I have pictures on my phone and want to organize them into categories. Help me identify what each picture represents.\n
 Use this API documentation for reference:  {"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": ["model_url"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "Load MobileNetV2 model pre-trained on ImageNet dataset for image classification."}}


 Identify the category for these images to decide which category I should tag them on my blog.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification 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": {"input": "224x224 RGB image", "output": "1001 element vector of logits"}, "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n# Preprocess image\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# Make prediction\npredictions = model(input_image)\n# Get class label\npredicted_class = tf.argmax(predictions, axis=-1)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model using MobileNet V2 architecture on the ImageNet dataset."}}


 Provide an API call that can detect any landmark in a given photo.\n
 Use this API documentation for reference:  {"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\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\nresult = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "Varies depending on the model"}, "description": "TensorFlow Hub provides a variety of pre-trained image classification models that can be easily loaded and used for various image classification tasks. The models are trained on the ImageNet dataset and their performance varies depending on the specific model chosen."}}


 We have a car selling website and want to identify the model of the car based on the images uploaded by the seller.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading a pre-trained image classification model", "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\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n# Prepare an image\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\n# Make predictions\npredictions = model(image_array)\n# Decode predictions\npredicted_class = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=1)[0][0][1]", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture and trained on ImageNet dataset. The model is available on TensorFlow Hub and can be used for classifying images into one of 1000 classes."}}


 I'd like to look into a list of articles and rank them by their similarity to the provided statement. \n###Input: <noinput>\n
 Use this API documentation for reference:  {"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 strings"}, "python_environment_requirements": {"tensorflow": "2.0.0", "tensorflow_hub": "0.8.0"}, "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', 'This is a test'])", "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."}}


 Can you suggest how to extract features from animal images and classify them into their respective species?\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4", "trainable": "False"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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                   trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained MobileNetV2 model for extracting image feature vectors. The model takes an image of size 224x224 and returns a 1280-dimensional feature vector."}}


 Create an image-based recommendation system to identify visually similar products.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": ["input_shape", "output_shape", "trainable"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained feature vector model that can be used for image classification and other computer vision tasks."}}


 Our company wants to create a product recommender that sorts products based on visual similarity, help us identify the products in images.\n
 Use this API documentation for reference:  {"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/inception_v3/classification/5')", "api_arguments": {"model_id": "string"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.expand_dims(input_image, 0)\npredictions = model(input_image)\n", "performance": {"dataset": "ImageNet", "accuracy": "0.779"}, "description": "A pre-trained image classification model using Inception V3 architecture, trained on ImageNet dataset. The model can be used to classify images into 1000 different categories."}}


 I have a list of short movie descriptions and want to get an embedding of each description.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "URL of the model", "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": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Develop an application that can recognize the names of items on a store shelf based on their images.\n
 Use this API documentation for reference:  {"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."}}


 Determine which politicians are similar to each other based on their quotes by obtaining an embedding for each quote.\n
 Use this API documentation for reference:  {"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; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world']); print(embeddings)", "performance": {"dataset": "STSbenchmark", "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. It is trained on a variety of data sources and a variety of tasks with the aim of dynamically accommodating a wide variety of natural language understanding tasks."}}


 I have a list of movie reviews and I want to find the semantic similarity between them. How can I convert theses reviews into numerical data?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Generate embeddings for text", "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!'])", "performance": {"dataset": "Various", "accuracy": "N/A"}, "description": "A pre-trained model for generating text embeddings using the Universal Sentence Encoder. It can be used for various NLP tasks like semantic similarity, text classification, and clustering."}}


 There are some insects in the park, I want to recognize them using an app.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification model", "api_name": "imagenet-mobilenet_v2_100_224-classification", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model.predict(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset with 100% depth and 224x224 input size."}}


 I want to inspect my living room and detect all the objects in a photo I have taken.\n
 Use this API documentation for reference:  {"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."}}


 A retail store needs a tool that will be able to detect misplaced items on their shelves.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in an image", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1'\ndetector = hub.load(model_id)\nimage = tf.image.decode_jpeg(tf.io.read_file('image.jpg'))\nresult = detector([image])", "performance": {"dataset": "OpenImages_v4", "accuracy": "N/A"}, "description": "This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model."}}


 I need to build a pet breed classifier from images. I want to use a pre-trained model to extract feature vectors of a pet image.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "model_id", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/feature_vector/4')", "api_arguments": ["model_id", "input_shape", "trainable"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/4'\ninputs = tf.keras.Input(shape=(224, 224, 3))\nfeature_extractor = hub.KerasLayer(model_id, trainable=False)(inputs)\noutputs = tf.keras.layers.Dense(1000, activation='softmax')(feature_extractor)\nmodel = tf.keras.Model(inputs=inputs, outputs=outputs)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "This API allows users to extract feature vectors from images using a pre-trained model from TensorFlow Hub. The feature vectors can then be used for various image recognition and classification tasks."}}


 We have set of images and want classify them into meaningful categories.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nloaded_model = hub.load(model_id)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the specified model_id."}}


 Show me how to get the semantic similarity between two sentences.\n###Input: \"I am going to the store to buy groceries.\", \"I will visit the market to purchase food items.\" \n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data", "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!'])", "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."}}