 Imagine you are building a recommendation system that suggests articles to users on a website. To accomplish that, you need an embedding for each article title in order to compare their similarity.\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": "List of sentences or phrases"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": {"import tensorflow as tf": "", "import tensorflow_hub as hub": "", "embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')": "", "sentences = ['This is a test sentence.', 'Another example sentence.']": "", "embeddings = embed(sentences)": "", "print(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."}}


 Tell me what kind of flower is in this image by classifying it.\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/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))\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 specified"}, "description": "TensorFlow Hub provides pre-trained models for image classification tasks. This example demonstrates how to load a pre-trained Inception V3 model from TensorFlow Hub and use it to classify an image."}}


 Verify if the following two sentences are related by measuring the semantic similarity: Sentence 1: \"The sky is blue today.\" Sentence 2: \"Today the atmosphere appears azure.\"\n###Input: The sky is blue today. Today the atmosphere appears azure.\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."}}


 Can you help me find a location of certain objects after providing some images?\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."}}


 We need to get image feature vectors and build a simple image classifier for our image dataset.\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."}}


 My friend sent me a picture of his new dog breed, but I can't recognize it. Can you classify this image for me?\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_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')\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\nresults = model(image)\npredicted_class = tf.math.argmax(results[0], axis=-1).numpy()", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture, trained on ImageNet dataset, and available on TensorFlow Hub for easy integration."}}


 We have to recognize a variety of animals and classify them in a zoo, using a lightweight model that can work on mobile phones.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification model", "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", "output_shape": "[1001]"}, "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_130_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)", "performance": {"dataset": "ImageNet", "accuracy": "0.856"}, "description": "This model is a pre-trained image classification model based on MobileNetV2 architecture. It can classify images into 1000 different categories."}}


 I've got an article in English, and I'd like to analyze it by clustering similar paragraphs together. How would I go about turning each paragraph of text into a high-dimensional vector?\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."}}


 Tell me how to build a model using feature extraction from images, for use in a binary classification task.\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": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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(1, activation='sigmoid')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained model that can be used to extract feature vectors from images using MobileNetV2 architecture with 224x224 input size."}}


 I work for an art gallery looking for a solution to classify images of our collection by art style.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract feature vector from images", "api_name": "module_name", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1')", "api_arguments": ["module_name"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodule_name = 'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1'\nmodule = hub.load(module_name)\nimage = tf.random.uniform((1, 299, 299, 3))\nfeatures = module(image)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "This API allows you to extract feature vectors from images using a pre-trained model from TensorFlow Hub. The model is based on the Inception V3 architecture and has been trained on the ImageNet dataset."}}


 I just bought a new drone and I want to capture a street video where I can recognize the types of vehicles approaching.\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 want to measure the similarity between two images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Generate feature vectors from images", "api_name": "image_feature_vector", "api_call": "hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4')", "api_arguments": {"input": "image_tensor", "output": "feature_vector"}, "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4')\nimage_tensor = tf.zeros([1, 224, 224, 3])\nfeature_vector = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture on TensorFlow Hub. It generates feature vectors from input images for various applications like image classification, object detection, and image similarity."}}


 Provide me with a tool to create an image classifier by extracting features from images in a dataset.\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": {"url": "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', trainable=False),\n    tf.keras.layers.Dense(1, activation='sigmoid')\n])\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This API provides a pre-trained MobileNetV2 model for extracting image features. It takes an input image of size 224x224 and returns a 1280-dimensional feature vector."}}


 Figure out a way to extract valuable features from images of potential clients' products to compare their similarity.\n
 Use this API documentation for reference:  {"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": "[None, 224, 224, 3]", "output_shape": "[None, 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/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                    input_shape=(224, 224, 3),\n                    output_shape=(1280,))\n])\nimg = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))\nimg_array = tf.keras.preprocessing.image.img_to_array(img)\nimg_array = tf.expand_dims(img_array, 0)\nfeatures = model.predict(img_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This API provides a feature vector for an image using the MobileNetV2 model trained on ImageNet dataset. The input image should be of size 224x224."}}


 My personal trainer wants me to analyze my workout form. Can you help me detect my body position in a photo?\n
 Use this API documentation for reference:  {"domain": "Image pose detection", "framework": "TensorFlow Hub", "functionality": "Detect human poses in images", "api_name": "movenet", "api_call": "hub.load('https://tfhub.dev/google/movenet/singlepose/lightning/3')", "api_arguments": {"input": "image_tensor", "output": "pose_keypoints"}, "python_environment_requirements": {"tensorflow": ">=2.5.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow_hub as hub\nmovenet = hub.load('https://tfhub.dev/google/movenet/singlepose/lightning/3')\npose_keypoints = movenet(image_tensor)", "performance": {"dataset": "COCO", "accuracy": "70.6 mAP"}, "description": "MoveNet is a family of pose detection models designed for efficient and accurate detection of human poses in images."}}


 Can you help me highlight animals in my vacation photographs?\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Object detection", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2'\nloaded_model = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg')\nimage_np = np.array(image)\ninput_tensor = tf.convert_to_tensor(image_np)\ninput_tensor = input_tensor[tf.newaxis, ...]\noutput_dict = loaded_model(input_tensor)\nboxes = output_dict['detection_boxes'][0].numpy()\nscores = output_dict['detection_scores'][0].numpy()\nclasses = output_dict['detection_classes'][0].numpy().astype(np.int32)", "performance": {"dataset": "COCO", "accuracy": "0.320"}, "description": "This API provides a pre-trained object detection model using TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and is trained on the COCO dataset. It can be used to detect multiple objects in an image and returns their class, bounding box coordinates, and confidence scores."}}


 Detect objects and their locations on this image I just took of my room.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Object detection and localization", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2'\ndetector = hub.load(model_id)\nresult = detector(image_tensor)", "performance": {"dataset": "COCO", "accuracy": "mAP@0.5IOU"}, "description": "A pre-trained object detection model that can detect and localize objects in an image using TensorFlow Hub."}}


 Create a flower classification model using feature vectors pre-extracted from images.\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": ">=0.12.0"}, "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'])\n# Train and evaluate the model\n# model.fit(train_images, train_labels, epochs=5)\n# model.evaluate(test_images, test_labels)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "The MobileNetV2 100 224 feature vector model is a pre-trained neural network that extracts features from images. It is trained on the ImageNet dataset and can be used for various image classification and feature extraction tasks."}}


 I am running a wildlife conservation project, and I need help to identify different species of animals and their locations in the images taken by camera traps.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Object detection", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2'\ndetector = hub.load(model_id)\nresult = detector(image_tensor)", "performance": {"dataset": "COCO", "accuracy": "Not specified"}, "description": "A pre-trained object detection model that can detect multiple objects in an image. The model is based on the SSD Mobilenet V2 architecture and is trained on the COCO dataset."}}


 We need to detect spam emails based on their subjects. Retrieve a high-dimensional vector from each email subject to be used for classification.\n
 Use this API documentation for reference:  {"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'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation 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 are a bunch of user reviews about our new product, and we need assistance categorizing them into different sentiments.\n
 Use this API documentation for reference:  {"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."}}


 We have images that we want to analyze, and I hope I can find a way to extract features from these images for further analysis.\n
 Use this API documentation for reference:  {"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."}}


 What is a good way to find the similarity between two sentences?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text using pre-trained models", "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(['text to be embedded'])\nprint(embeddings)", "performance": {"dataset": "SQuAD, SNLI, and others", "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. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs."}}


 I'm a clothing store owner, and I want to create a recommendation system that suggests similar clothes by comparing their features.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "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), trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model using MobilenetV2 architecture trained on ImageNet dataset for extracting features from images."}}


 I want to build a plant identifier app, and I need to determine the type of plant in a picture taken from the app.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "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": "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": ["import tensorflow as tf", "import tensorflow_hub as hub"], "load_model": "model = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')", "prepare_image": ["import numpy as np", "import PIL.Image as Image", "img = Image.open('path/to/your/image.jpg').resize((224, 224))", "img_array = np.array(img) / 255.0", "img_tensor = tf.expand_dims(img_array, 0)"], "classify_image": "predictions = model(img_tensor)"}, "performance": {"dataset": "ImageNet", "accuracy": "Top-1 accuracy: 75.2%, Top-5 accuracy: 92.5%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset."}}


 Can you please identify whether these two sentenes share a similar meaning or not?\n###Input: \"The quick brown fox jumps over the lazy dog.\", \"A speedy, sly fox leaps over a lounging canine.\"\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed 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."}}


 I want to create a custom image classifier, but first I need to use a pre-trained model to extract features from my dataset images.\n
 Use this API documentation for reference:  {"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": "[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/4',\n                   input_shape=[224, 224, 3])\n])\n", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature extraction model using MobileNetV2 architecture on ImageNet dataset."}}


 Recommend a mobile app for automatically classifying images taken by the user.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading and running a 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')\n# Load 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# Make a prediction\npredictions = model(input_image)\n# Decode the predictions\nclass_names = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=1)\nprint(class_names)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model based on MobileNetV2 architecture, trained on ImageNet dataset, and available on TensorFlow Hub."}}


 We want to find semantic similarity.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Convert text into a dense vector representation", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "Text or list of texts"}, "python_environment_requirements": {"tensorflow": ">= 2.0", "tensorflow_hub": ">= 0.8"}, "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.78"}, "description": "The Universal Sentence Encoder encodes text into a dense vector representation that can be used for various natural language processing tasks."}}


 We want to develop a smart surveillance system to monitor intruders. Please help us identify humans present in the images.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Load and use an object detection model", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')", "api_arguments": {"model_id": "The id of the model to be loaded from TensorFlow Hub"}, "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/tensorflow/ssd_mobilenet_v2/2'\nmodel = hub.load(model_id)\nimage = tf.image.decode_jpeg(tf.io.read_file('path/to/image.jpg'))\ndetected_objects = model(image[tf.newaxis, ...])", "performance": {"dataset": "COCO", "accuracy": "Unknown"}, "description": "Load and use an object detection model from TensorFlow Hub for detecting objects in images."}}


 My friends uploaded a collection of photos from our trip, and I want to organize them by identifying what is in each photo.\n###Input: <noinput>\n
 Use this API documentation for reference:  {"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 provided"}, "description": "Load a pre-trained image classification model from TensorFlow Hub for image classification tasks."}}


 Identify which model is used to encode text for natural language processing tasks like text classification and semantic similarity.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embeds 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\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!', 'Embedding is fun!'])", "performance": {"dataset": "SNLI, MultiNLI, and others", "accuracy": "80-90%"}, "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."}}


 Calculate the similarity between the following two sentences: \"The quick brown fox jumps over the lazy dog\" and \"The fast auburn fox leapt over the idle canine.\"\n###Input: \"The quick brown fox jumps over the lazy dog\", \"The fast auburn fox leapt over the idle canine.\"\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Text embedding using a pre-trained model", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello, world!', 'Embed text with Universal Sentence Encoder'])", "performance": {"dataset": "various", "accuracy": "N/A"}, "description": "A pre-trained text embedding model that converts sentences into fixed-size vector representations, enabling efficient semantic similarity calculations and other natural language processing tasks."}}


 Construct a recommendation engine for a forum that finds similar threads based on the text content.\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": "Text input", "python_environment_requirements": "TensorFlow, TensorFlow Hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello, world!', 'How are you?'])", "performance": {"dataset": "Various text sources", "accuracy": "High similarity scores for semantically similar sentences"}, "description": "A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks."}}


 They have a dataset of stained glass images and want to organize them into categories or classes. They need a pre-trained model.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models for image classification", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/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": "Not specified"}, "description": "Load a pre-trained model from TensorFlow Hub for image classification using the Inception V3 architecture."}}


 Provide me with a method to understand the semantic similarity between movie reviews.\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."}}


 We have a large photo archive, and we need a model to recognize and categorize pictures based on their content.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "model_id", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"model_id": "string", "input_shape": "tuple", "trainable": "boolean"}, "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/feature_vector/4'\nmodel = tf.keras.Sequential([\n    hub.KerasLayer(model_id, input_shape=(224, 224, 3), trainable=True),\n    tf.keras.layers.Dense(10, activation='softmax')\n])\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(x_train, y_train, epochs=5)", "performance": {"dataset": "ImageNet", "accuracy": "Top-1 accuracy"}, "description": "A pre-trained image feature vector model for image classification and transfer learning, based on MobileNetV2 architecture."}}


 After a dinner party, I want to save the pictures taken during the party and find similar images for decoration purposes by extracting an image feature vector.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Image 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]", "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/imagenet/mobilenet_v2_100_224/feature_vector/4\", input_shape=[224, 224, 3])\n])\nimage = tf.keras.Input(shape=(224, 224, 3))\nfeatures = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature extraction model using MobileNet V2 architecture with 224x224 input size and 1280-dimensional output feature vector. The model is trained on ImageNet dataset and can be used for various image recognition tasks."}}


 I am trying to create clusters of similar customer feedback for a product.\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 as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\ntext_input = ['This is a sample text']\nembeddings = embed(text_input)", "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 a wide range of tasks such as semantic similarity, clustering, and text classification."}}


 I have pictures of animals, and I need to find out what type of animals they are by leveraging pre-trained models.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification using pre-trained models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": [{"name": "model_id", "type": "string", "description": "The identifier of the pre-trained model to be loaded from TensorFlow Hub"}], "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", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load and use pre-trained image classification models from TensorFlow Hub for classifying images"}}


 We are working on an app that can identify plants. We have the images of the leaves, so all we need is a pre-trained model to classify the images.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification using pre-trained 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", "numpy": ">=1.19.5", "PIL": ">=8.3.2"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nfrom PIL import Image\nimport numpy as np\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n])\nimage = Image.open('path/to/your/image.jpg').resize((224, 224))\ninput_image = np.array(image) / 255.0\ninput_image = np.expand_dims(input_image, axis=0)\npredictions = model.predict(input_image)\npredicted_class = np.argmax(predictions[0])\nprint('Predicted class:', predicted_class)", "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."}}


 Analyze the sentiments of these customer reviews before processing their feedback.\n###Input: [\"I loved the service and the food!\", \"The staff was rude, and I had a terrible experience.\", \"Not too bad, could be better.\"]\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Universal Sentence Encoder", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "A list of sentences or phrases"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello, world!', 'How are you?'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.80"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 We want to obtain the location of a boat in an image. Detect objects in the given image and provide the location of the boat if found.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in images", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/efficientdet/d0/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/tensorflow/efficientdet/d0/1'\ndetector = hub.load(model_id)", "performance": {"dataset": "COCO", "accuracy": "N/A"}, "description": "Load a pre-trained model from TensorFlow Hub for object detection in images."}}


 Create a system to find the similarity between two sentences.\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', 'Embedding text with TensorFlow Hub'])", "performance": {"dataset": "SNLI", "accuracy": "86.9%"}, "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 building a recommendation system for similar pictures. Provide me with a vector representation of a picture to measure image similarity.\n
 Use this API documentation for reference:  {"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."}}


 Recommend to me some suitable products based on the textual similarity of their descriptions from a database.\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!'])", "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."}}


 Extract audio features to classify sounds in the environment.\n
 Use this API documentation for reference:  {"domain": "Audio embedding", "framework": "TensorFlow Hub", "functionality": "Feature extraction from audio", "api_name": "yamnet", "api_call": "hub.load('https://tfhub.dev/google/yamnet/1')", "api_arguments": [], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "", "performance": {"dataset": "", "accuracy": ""}, "description": "YAMNet is a deep net that predicts audio events from the AudioSet ontology."}}


 Write an AI-powered program to summarize movie plots while retaining maximum details from the given plot.\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": "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. It is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 Help me compare similarities between user reviews about two different products.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into a continuous vector space", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text: A string or 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!', 'How are you?'])\nprint(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "A pre-trained model for embedding text into a 512-dimensional vector space that can be used for various natural language processing tasks such as text classification, semantic similarity, clustering, and other downstream tasks."}}


 Provided with images of paintings, I want to determine which art era they belong to.\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', input_shape=(224, 224, 3), trainable=False)\n])\nimage = tf.random.normal([1, 224, 224, 3])\nfeatures = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. The model is designed to extract features from images and can be used for various tasks such as image classification, object detection, and image similarity."}}


 Understand the relationship between two movie synopses and determine whether they seem similar or not.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional space", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "List of strings"}, "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!', 'My name is John Doe'])", "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."}}


 Suggest personalized ads to users by creating a semantic similarity model that recommends advertisements based on textual similarity to user preferences stored in the description field. \n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding", "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": "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 list of customer reviews and your task is to calculate the semantic similarity between each pair of reviews.\n
 Use this API documentation for reference:  {"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": "embeddings = embed(['Hello world'])", "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."}}


 Expose a method to compute the semantic similarity of pairs of sentences by comparing the high-dimensional vectors.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into a high-dimensional vector", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "input_text", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])\nprint(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803521"}, "description": "This API provides a method to convert text into a high-dimensional vector using the Universal Sentence Encoder from TensorFlow Hub. It can be used for tasks such as semantic similarity and clustering."}}


 I am analyzing tweets, and I want to know the sentiment of each tweet. Please help me convert tweets into meaningful representations.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text for various NLP tasks", "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": "Various", "accuracy": "Not specified"}, "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 build a machine learning model that can automatically detect and classify different food items from images.\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'\nmodel = hub.load(model_id)", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the provided model_id."}}


 Identify the animal in the photo I have taken.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "model_id", "api_call": "tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/classification/5')])", "api_arguments": {"handle": "string", "trainable": "boolean (optional)"}, "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/inception_v3/classification/5'\nmodel = tf.keras.Sequential([hub.KerasLayer(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.keras.applications.inception_v3.preprocess_input(image)\nimage = tf.expand_dims(image, axis=0)\npredictions = model.predict(image)", "performance": {"dataset": "ImageNet", "accuracy": "0.779"}, "description": "Load a pre-trained Inception V3 model from TensorFlow Hub for image classification."}}


 Generate an appropriate label for instance of image.\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)\n# Load and preprocess an image\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\n# Make a prediction\npredictions = model(image_array)\n# Decode the predictions\nclass_names = tf.keras.applications.inception_v3.decode_predictions(predictions.numpy())", "performance": {"dataset": "ImageNet", "accuracy": "77.9%"}, "description": "This API allows you to load pre-trained models for image classification using TensorFlow Hub. The example provided demonstrates how to load the Inception V3 model, preprocess an image, make a prediction, and decode the predictions into class names."}}


 I'm traveling to a new city and I want to use my phone to identify different objects in my surroundings.\n
 Use this API documentation for reference:  {"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": "URL of the TensorFlow Hub model"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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 images using the SSD Mobilenet V2 architecture."}}


 Analyze a list of movie reviews and group them based on semantic similarity by first converting the reviews into embeddings.\n
 Use this API documentation for reference:  {"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 am working on a car accident detection system. Show me how to detect object and classify them in images\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in images", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1')", "api_arguments": ["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('path/to/image.jpg'))\nconverted_image = tf.image.convert_image_dtype(image, tf.float32)[tf.newaxis, ...]\nresult = detector(converted_image)", "performance": {"dataset": "OpenImages V4", "accuracy": "Not specified"}, "description": "A pre-trained image object detection model that detects objects in images using the Faster R-CNN architecture with Inception-ResNet V2 as the feature extractor."}}


 Identify the objects present in the picture I took from my last hiking trip.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Object detection", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["import tensorflow as tf", "import tensorflow_hub as hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/tensorflow/efficientdet/lite0/detection/1'\nloaded_model = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('image_path')\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = image / 255.0\nimage = tf.expand_dims(image, 0)\noutput = loaded_model(image)", "performance": {"dataset": "", "accuracy": ""}, "description": "Load a pre-trained TensorFlow Hub model for image object detection and use it to detect objects in an input image."}}


 We are creating a visual recommender system based on images. We need to extract features from them.\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."}}


 In order to analyze the sentiment of tweets on Twitter, I need the Universal Sentence Encoder model to convert tweets into embeddings for analysis.\n
 Use this API documentation for reference:  {"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."}}


 Detect and count the number of different objects in a picture I took at a beach.\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."}}


 I am visiting different places and I want to know which landmark I am visiting.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained model for image classification", "api_name": "imagenet_mobilenet_v2_100_224", "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])\n", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained model for image classification using MobileNetV2 architecture with 224x224 input size, trained on ImageNet dataset."}}


 Identify different types of birds when hikers send the images to our system.\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))\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 model for image classification using Inception V3 architecture, available on TensorFlow Hub. The model has been trained on the ImageNet dataset and can be used to classify images into 1000 different classes."}}


 How can I compare the similarity between multiple sentences?\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', 'Embedding with TensorFlow Hub'])\nprint(embeddings)", "performance": {"dataset": "Common benchmarks", "accuracy": "State-of-the-art performance"}, "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."}}


 Tell me what kind of animal/bird is in this photo I just took.\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."}}


 Determine the top 3 closest category matches for a given image using the Inception V3 pre-trained model.\n
 Use this API documentation for reference:  {"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": "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)", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the model's ID."}}


 I need a simple way to encode text into vectors for my clustering algorithm.\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."}}


 Find a tool to extract features from images and create a model to identify different types of plants.\n
 Use this API documentation for reference:  {"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], "output_shape": [1280], "trainable": false}, "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                   output_shape=[1280],\n                   trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": 0.71}, "description": "This is a feature vector obtained from the MobileNetV2 model trained on the ImageNet dataset. It can be used for transfer learning and fine-tuning on new datasets."}}


 How do I create image-feature-vector for the last travel destination photo album to organize similar pictures together?\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Create image feature vector", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v1/feature_vector/4'\nmodel = hub.load(model_id)\nfeature_vector = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load an image feature vector model from TensorFlow Hub and use it to create feature vectors for images."}}


 Identify the make and model of a car using an image posted on an online car marketplace.\n###Input: \"https://url-to-image-of-car-from-marketplace\"\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading 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": "Load a pre-trained image classification model from TensorFlow Hub using the specified model_id."}}


 Assist me in identifying animal species present in a collection of images for an ecological study.\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."}}


 I am building a tool to measure the similarity between different product descriptions. To do this, I need to first get embeddings for each description.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Converts 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?'])", "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."}}


 Show me how to classify an image of a car into one of the 1000 categories.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification model", "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", "output_shape": "[1001]"}, "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_130_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)", "performance": {"dataset": "ImageNet", "accuracy": "0.856"}, "description": "This model is a pre-trained image classification model based on MobileNetV2 architecture. It can classify images into 1000 different categories."}}


 We need to create an image classifier for recognizing dog breeds. Before creating the classifier, we should extract a fixed-length feature vector from each image.\n
 Use this API documentation for reference:  {"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."}}


 Help me create a machine learning model to identify whether a solar panel is damaged or not using images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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(1, activation='sigmoid')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained model that can be used to extract feature vectors from images using MobileNetV2 architecture with 224x224 input size."}}


 Suppose you want to build a search engine that can find the most visually similar products in your online shop. You need to extract feature vectors from product images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Image 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": ">=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])\nx = tf.keras.layers.Input(shape=(224, 224, 3))\nfeatures = model(x)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This module uses MobileNet V2 architecture for image feature vector extraction. It accepts a batch of images of shape (224, 224, 3) and returns a 1280-dimensional feature vector for each image."}}


 I am a teacher and want to classify images related to educational material. Find out the category of educational images by providing the image's URL.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification using pre-trained models", "api_name": "imagenet_mobilenet_v2_130_224_classification", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5"}, "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([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5')\n])\n# Load an image and preprocess it\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# Make predictions\npredictions = model.predict(input_image)\n# Decode the predictions\nclass_names = tf.keras.applications.mobilenet_v2.decode_predictions(predictions, top=1)\nprint(class_names)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "A pre-trained image classification model using MobileNetV2 architecture from TensorFlow Hub. The model is trained on ImageNet dataset and can classify images into 1000 different categories."}}


 Can you find the semantic similarity between a list of sentences given their embeddings obtained from a pre-trained model?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text using pre-trained models", "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": ["pip install tensorflow", "pip install tensorflow-hub"], "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!', 'Embedding with TensorFlow Hub is easy!'])", "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 pre-trained on a variety of data sources and can be used with TensorFlow Hub."}}


 An airport authority wants to detect the presence of drones close to the airport runway.\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.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)", "performance": {"dataset": "COCO", "accuracy": "Not specified"}, "description": "Detect objects in an image using a pre-trained model from TensorFlow Hub"}}


 I have a lot of images and I want to categorize them based on the objects present in the images.\n
 Use this API documentation for reference:  {"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-hub", "tensorflow"], "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('image.jpg')\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.expand_dims(input_image, 0)\nresult = detector(input_image)\nprint(result)", "performance": {"dataset": "COCO", "accuracy": "mAP"}, "description": "A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNet V2."}}


 I'm planning to do sentiment analysis on a bunch of news articles. Help me convert the article content to 20-dimensional vectors.\n
 Use this API documentation for reference:  {"domain": "Text language model", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1')", "api_arguments": ["model_url"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "embed = hub.load('https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1')\nembeddings = embed(['cat is on the mat', 'dog is in the fog'])", "performance": {"dataset": "Google News", "accuracy": "20 dimensions"}, "description": "A pre-trained text language model that generates embeddings for input text using TensorFlow Hub."}}


 In order to perform image classification using a machine learning model, I need to generate feature vectors for the given images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Generate feature vectors for images", "api_name": "imagenet_mobilenet_v3_large_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5')", "api_arguments": {"input_shape": "(224, 224, 3)", "output_shape": "(1280,)"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5', input_shape=(224, 224, 3), output_shape=(1280,))\n])\n# 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# Generate feature vector\nfeature_vector = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "75.3%"}, "description": "This API provides a pre-trained image feature vector model, MobileNetV3-Large, from TensorFlow Hub. The model is trained on the ImageNet dataset and can be used to generate feature vectors for images of size 224x224 with 3 channels."}}


 Generate embeddings for movie descriptions to create a recommendation system based on semantic similarity.\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 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."}}


 Match products descriptions to our customer search query in our e-commerce store. First, encode them into vectors.\n
 Use this API documentation for reference:  {"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. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 For a fashion e-commerce website, I want to build a recommendation system on clothing items based on their similarity. How do I extract feature vectors from clothing images for comparison?\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": ["URL: https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4', trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\nmodel.build([None, 224, 224, 3])\nmodel.summary()", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This API provides a pre-trained MobileNetV2 model for extracting feature vectors from images. The model is trained on the ImageNet dataset and achieves 71.9% accuracy in classification tasks."}}


 For my language model, I require preprocessed text. Use an appropriate method to assist me in preparing the input text for further processing.\n
 Use this API documentation for reference:  {"domain": "Text preprocessing", "framework": "TensorFlow Hub", "functionality": "Text preprocessing module", "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?'])", "performance": {"dataset": "Multiple", "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."}}