 I am working on a project where I need to cluster similar images of street art. How can I get the relevant feature vectors from these images for clustering?\n
 Use this API documentation for reference:  {"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), output_shape=(1280,))\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained MobileNetV2 model for extracting feature vectors from images, using TensorFlow Hub."}}


 We want to develop a simple application where users can take a photo of any object and find the description of it. Help us with creating a model that extracts features from the object's image.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model using MobileNet V2 architecture with 100% depth and 224x224 input size. It can be used for image classification, object detection, and other image-based tasks."}}


 I have a set of food images and I want to create a model that differentiates between different types of cuisine.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "model = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                   input_shape=(224,224,3),\n                   trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This API provides a feature vector for an image using the MobileNet V2 architecture trained on the ImageNet dataset. The feature vector can be used as input for training other models or for image similarity tasks."}}


 Show me the right API call to generate image feature vectors using MobileNet V2 architecture so that I can perform a classification task.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Generate image feature vectors", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4", "output_shape": "[1280]", "trainable": "False"}, "python_environment_requirements": ["tensorflow>=2.0.0", "tensorflow-hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                    output_shape=[1280],\n                    trainable=False),\n    tf.keras.layers.Dense(5, activation='softmax')\n])\nmodel.build([None, 224, 224, 3])\nmodel.summary()", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNet V2 architecture on ImageNet dataset. Can be used for transfer learning and generating image feature vectors."}}


 My dog is always getting into things that can be dangerous. Can you help me identify the object my dog is about to eat?\n
 Use this API documentation for reference:  {"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))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\npredictions = model(image)\nprint(predictions)", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "description": "A pre-trained image classification model available on TensorFlow Hub that can be used to classify images into one of the 1000 ImageNet classes. The example provided shows how to load the Inception V3 model, preprocess an image, and obtain predictions."}}


 A photo is sent to me, and I would like to know what objects are in the photo.\n
 Use this API documentation for reference:  {"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."}}


 Analyze customer reviews and identify positive and negative sentiments, so please convert the text reviews 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_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 0.803"}, "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."}}


 Recommend a method to extract image feature vectors with MobileNetV2. \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,)"}, "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', input_shape=(224, 224, 3)),\n  tf.keras.layers.Dense(num_classes, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset."}}


 Write an example code to use a pre-trained model to convert a sentence \"My cat is sitting on the porch\" into high-dimensional vectors.\n###Input: My cat is sitting on the porch\n
 Use this API documentation for reference:  {"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."}}


 Analyze a collection of images of pets to obtain meaningful features and club them into categories based on their appearance and other characteristics.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract feature vectors from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_image": "A 224x224x3 RGB image", "output_feature_vector": "A 1x1280 vector"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')\n# Preprocess your image, e.g. resize to 224x224 and normalize pixel values\nprocessed_image = ... \n# Extract feature vector\nfeature_vector = model(processed_image)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained MobileNetV2 model that takes an input image and outputs a 1280-dimensional feature vector. Useful for transfer learning applications."}}


 We are building a model for car brand recognition. Before training, we need to extract features from car images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract features from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\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 Keras layer that extracts features from images using the MobileNetV2 model trained on ImageNet dataset."}}


 I have a list of sentences and I want to find which ones are most similar. How can I do that efficiently?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Convert text into dense 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')\ntext = ['This is a sentence.']\nembedding = embed(text)", "performance": {"dataset": "STS benchmark", "accuracy": "0.78 Pearson correlation"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks."}}


 I have a customer service chatbot, and I would like to analyze the similarity between customer queries using NLP.\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": "URL of the pre-trained model", "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": "SNLI, MultiNLI, and STS Benchmark", "accuracy": "85% on SNLI, 76.9% on MultiNLI, and 0.803 on STS Benchmark"}, "description": "A pre-trained model for embedding text into a high-dimensional vector that can be used for various natural language processing tasks"}}


 I have hundreds of images and I want to group them in categories, but first I need to extract features from those images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract image features", "api_name": "imagenet_mobilenet_v2", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input": "image_tensor", "output": "feature_vector"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.8"}, "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])\nimage_tensor = tf.random.uniform(shape=(1, 224, 224, 3))\nfeatures = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNet V2 architecture on ImageNet dataset. It can be used to extract features from an input image tensor."}}


 Analyze the given image dataset containing healthy and unhealthy plants by extracting features using pre-trained MobileNetV2 model.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "(224, 224, 3)", "output_shape": "(1280,)"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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])\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)\nfeatures = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained model for extracting feature vectors from images using MobileNetV2 architecture trained on ImageNet dataset"}}


 Find out what's in the image taken by a wildlife photographer, so we can determine the main subject of the picture.\n###Input: An image taken by a wildlife photographer.\n
 Use this API documentation for reference:  {"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."}}


 Can you tell me the type of food in this image?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification model", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\n# Load the model\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/4'\nmodel = hub.load(model_id)\n# Load an image and preprocess it\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\n# Predict the class\npredictions = model(image[None, ...])\n# Get the class label\npredicted_class = tf.keras.applications.inception_v3.decode_predictions(predictions.numpy())[0][0][1]", "performance": {"dataset": "ImageNet", "accuracy": "Top-1 Accuracy: 77.9%, Top-5 Accuracy: 93.7%"}, "description": "This API provides a pre-trained image classification model using TensorFlow Hub, which can be used to classify images into various categories. The model is trained on the ImageNet dataset and can be loaded using the `hub.load()` function."}}


 Identify the semantic theme of the given list of customer support tickets.\n###Input: ['The payment did not go through.', 'Issues with the payment method.', 'I need help with my payment.', 'Unable to complete payment.', 'Payment error encountered.']\n
 Use this API documentation for reference:  {"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."}}


 I want a system that will read the image of my room and identify all the objects present in the room along with their confidence score.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in images", "api_name": "ssd_mobilenet_v2", "api_call": "hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')", "api_arguments": {"image_tensor": "Input tensor of shape [1, None, None, 3]", "detection_boxes": "Output tensor of shape [1, num_boxes, 4]", "detection_scores": "Output tensor of shape [1, num_boxes]", "detection_classes": "Output tensor of shape [1, num_boxes]", "num_detections": "Output tensor of shape [1]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')\n# Load image and preprocess\nimage = tf.image.decode_jpeg(tf.io.read_file('path/to/image.jpg'))\nimage = tf.image.resize(image, (300, 300))\nimage = tf.expand_dims(image, axis=0)\n# Run object detection\noutput = model(image)\n# Extract detection boxes, scores, and classes\nboxes = output['detection_boxes'].numpy()\nscores = output['detection_scores'].numpy()\nclasses = output['detection_classes'].numpy()\nnum_detections = int(output['num_detections'])", "performance": {"dataset": "COCO", "accuracy": "mAP of 22"}, "description": "A pre-trained SSD MobileNet V2 model for object detection in images, capable of detecting multiple objects with bounding boxes and confidence scores. The model is trained on the COCO dataset and achieves a mean average precision (mAP) of 22."}}


 My company has a database of product descriptions and I want to convert them into vectors for better search functionality.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into a fixed-size vector", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "input: Text data", "python_environment_requirements": "TensorFlow, TensorFlow Hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello, world!', 'Embedding with TensorFlow Hub'])", "performance": {"dataset": "Various", "accuracy": "Not specified"}, "description": "A pre-trained model for encoding text into high-dimensional vectors that can be used for various natural language processing tasks."}}


 I have a project which I need to quickly recognize animals, and I want to use the pre-trained model as a starting point.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract high-level features from images", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]", "trainable": "False"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                   input_shape=(224, 224, 3), trainable=False),\n    tf.keras.layers.Dense(1, activation='sigmoid')\n])\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It extracts high-level features from images and can be used for tasks like image classification, object detection, and transfer learning."}}


 I need an easy way to sort out thousands of travel photos by recognizing landmarks and other objects.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('image_path', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model(image_array)\nclass_index = tf.argmax(predictions, axis=-1).numpy()[0]", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture, which can recognize 1000 different classes from the ImageNet dataset."}}


 Develop a mobile application to identify the objects in real-world images and display its category.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained models for image classification", "api_name": "imagenet_mobilenet_v2_130_224_classification", "api_call": "hub.load('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": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5')\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "94.1%"}, "description": "MobileNetV2 is a family of neural network architectures for efficient on-device image classification and related tasks, originally published by Mark Sandler, et al. This model is pre-trained on the ImageNet dataset and can be used for various image classification tasks."}}


 I need to analyze user feedback and compare it to a list of predefined topics.\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')\ntext_input = ['This is a sample text']\nvector_output = embed(text_input)", "performance": {"dataset": "STS Benchmark", "accuracy": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 We need to recognize the flowers in the pictures from our botanical garden.\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)\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, 0)\npredictions = model(image)\nprint(predictions)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained image classification model from TensorFlow Hub and use it to predict the class of an input image."}}


 I want a solid text preprocessing method to make my model more efficient in dealing with different texts.\n
 Use this API documentation for reference:  {"domain": "Text preprocessing", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "tensorflow_hub.KerasLayer", "api_call": "hub.KerasLayer('https://tfhub.dev/google/nnlm-en-dim128/2')", "api_arguments": {"handle": "A string, the handle of the Hub module to load", "kwargs": "Optional keyword arguments"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer(handle=\"https://tfhub.dev/google/nnlm-en-dim128/2\")\n])", "performance": {"dataset": "NNLM", "accuracy": "N/A"}, "description": "Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models."}}


 A fashion store wants to categorize newly arrived clothes into different classes. Please make sure the right category is assigned to each clothing item.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4'\nmodel = hub.load(model_id)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "Load a pre-trained image classification model from TensorFlow Hub and use it to classify images."}}


 To match clothes in my wardrobe, I need to extract features from images and organize them based on similarity.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                   input_shape=(224, 224, 3),\n                   trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\nhistory = model.fit(train_batches, epochs=10, validation_data=validation_batches)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This module uses MobileNet v2 architecture for image feature vector extraction. It takes an input image of shape (224, 224, 3) and outputs a 1280-dimensional feature vector."}}


 We will launch a new product that relies on user reviews, analyze reviews, and generate insights based on their sentiments.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data for machine learning models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1')", "api_arguments": {"model_id": "A string representing the URL or path to the TensorFlow Hub model"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1'\nembed = hub.load(model_id)\nembeddings = embed(['Hello, world!'])\nprint(embeddings)", "performance": {"dataset": "Wikipedia", "accuracy": "Not specified"}, "description": "Load a pre-trained text embedding model from TensorFlow Hub, and use it to convert text data into fixed-size vectors that can be used as input for machine learning models."}}


 Find me an API that will help me extract features of my images for building a recommendation system.\n
 Use this API documentation for reference:  {"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', input_shape=(224, 224, 3))\n])\nimage = tf.zeros([1, 224, 224, 3])  # A batch of one 224x224x3 image\nfeatures = model(image)  # Features with shape [batch_size, 1280]", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture for image classification tasks."}}


 I want to extract important features from car images of different angles to predict the brand of the car.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Load and use a pre-trained image feature vector model", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')\nimage = tf.zeros([1, 224, 224, 3])\nfeatures = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "This module uses the MobileNet v2 architecture to generate image feature vectors. The model has been pre-trained on the ImageNet dataset and can be used for various image classification tasks."}}


 I want to create a phone cover design recommendation system. For that, I need to get some features of cover images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v1_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1024]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "model = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/4', input_shape=[224, 224, 3], trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "70.9%"}, "description": "A compact representation of the API for extracting image feature vectors using MobileNetV1 on TensorFlow Hub."}}


 We are trying to group tweets with similar emotions. How can we represent the text in the tweet so we can quantify the similarity among tweets?\n
 Use this API documentation for reference:  {"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'])", "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 are planning a security system for a warehouse, and we need to know the objects in our surveillance feed.\n
 Use this API documentation for reference:  {"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)\n# Load an image\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg')\n# Convert the image to a tensor\ninput_tensor = tf.keras.preprocessing.image.img_to_array(image)\n# Run the object detection\nresults = detector(input_tensor)", "performance": {"dataset": "COCO", "accuracy": "Not specified"}, "description": "A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNet V2."}}


 For arranging my documents, I want to compute the semantic similarity between a set of sentences.\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!'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation of 0.78"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for various natural language processing tasks."}}


 I would like to group similar user reviews together. To do this, I need to transform the text reviews into high dimensional vectors.\n
 Use this API documentation for reference:  {"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."}}


 I need to find if a word is a positive or negative sentiment. I need to process my text before feeding it to my sentiment analysis model.\n
 Use this API documentation for reference:  {"domain": "Text preprocessing", "framework": "TensorFlow Hub", "functionality": "Text preprocessing", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"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(['sample sentence'])", "performance": {"dataset": "Various datasets", "accuracy": "Not mentioned"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


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


 Create a list of similar art pictures by comparing the feature vectors of the existing art 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": {"input_shape": "[224, 224, 3]", "output_shape": "[1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "model = tf.keras.Sequential([\n  hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4', input_shape=[224, 224, 3], output_shape=[1280]),\n  tf.keras.layers.Dense(num_classes, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280]."}}


 Discover a person's sentiment after reading their review\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>=2.0, 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": "0.8"}, "description": "A pre-trained text embedding model that converts sentences into fixed-size vectors, suitable for various NLP tasks."}}


 We have several images of clothing items and want to differentiate them by their features. Generate a model that will create a feature vector for each item.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "model = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                    input_shape=(224,224,3),\n                    trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\n              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained model that takes an image input and outputs a 1280-dimensional feature vector representing the image, which can be used for various tasks such as classification, clustering, and similarity search."}}


 Create a mobile app that takes a photo of a plant and identifies its species. To do this, use an image classifier to process the photo.\n
 Use this API documentation for reference:  {"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": {"model_url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"}, "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_130_224/classification/4')\n# Load and preprocess an image\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\ninput_array = tf.expand_dims(input_array, 0)\ninput_array = input_array / 255.0\n# Make predictions\npredictions = model(input_array)\n# Get the top predicted class\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", "performance": {"dataset": "ImageNet", "accuracy": "Top-1 accuracy of 80.1% and Top-5 accuracy of 95.1%"}, "description": "A pre-trained model for image classification using MobileNetV2 architecture with an input size of 224x224 pixels."}}


 I want to use an API to find the degree of similarity between sentences. Firstly, I need to obtain their high-dimensional vector representations.\n
 Use this API documentation for reference:  {"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"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!']); print(embeddings)", "performance": {"dataset": "STSbenchmark", "accuracy": "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."}}


 Let's observe a random street to identify various elements like cars, people and street signs in a photo.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in an image", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1'\ndetector = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg')\nimage_np = tf.keras.preprocessing.image.img_to_array(image)\ninput_tensor = tf.convert_to_tensor(image_np)\ninput_tensor = input_tensor[tf.newaxis, ...]\noutput_dict = detector(input_tensor)\nfor i in range(output_dict['detection_scores'][0].shape[0]):\n    if output_dict['detection_scores'][0][i] > 0.5:\n        print(output_dict['detection_class_entities'][0][i].numpy().decode('utf-8'), ':', output_dict['detection_scores'][0][i].numpy())", "performance": {"dataset": "OpenImages V4", "accuracy": "mAP@0.5IOU: 37.6%"}, "description": "A pre-trained Faster R-CNN model with Inception-ResNet-v2 feature extractor on OpenImages V4 dataset for object detection."}}


 Organize my photos taken during the vacation by identifying what the photo is showing.\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", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": {"module_url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4", "input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 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_100_224/classification/4\")\n])\n# Prepare an image\nimage = tf.keras.preprocessing.image.load_img(\"path/to/your/image.jpg\", target_size=(224, 224))\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\ninput_array = tf.expand_dims(input_array, 0)\n# Make a prediction\npredictions = model.predict(input_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset. It can be used for classifying images into 1000 different categories."}}


 Create a document classifier, and identify the genre of a movie poster by classifying the image content.\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('image.jpg', target_size=(299, 299))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_tensor = tf.expand_dims(image_array, 0)\npredictions = model(image_tensor)\n", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "This API allows users to perform image classification using pre-trained models from TensorFlow Hub. Users can load a model using its model_id and then use it to classify images."}}


 Develop a model that will extract feature vectors from images, which we will use later to classify the images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract features from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[batch_size, height, width, 3]", "output_shape": "[batch_size, 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                   trainable=False)\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_batch = tf.expand_dims(image_array, 0)\nfeatures = model.predict(image_batch)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "Extracts a 1280-dimensional feature vector from images using the MobileNetV2 architecture trained on the ImageNet dataset."}}


 I want to build a mobile image recognition app to identify the species of birds in images. Suggest a suitable pre-trained model.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2')", "api_arguments": {"model_id": "string"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2')", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "Load a pre-trained image classification model from TensorFlow Hub, providing a model_id for the desired model."}}


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


 There are comments written by users in different languages, and we want to calculate the similarity between pairs of comments.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["url"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "STSbenchmark", "accuracy": "Pearson correlation coefficient of 0.78"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs."}}


 Analyze large data sets containing various customer reviews. Transform the text from these reviews into vectors for further analysis like sentiment, similar phrases, and other natural language processing tasks.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.78"}, "description": "A pre-trained model for embedding text into high-dimensional vectors that can be used for a wide range of tasks such as sentiment analysis, semantic similarity, and classification."}}


 I am visiting the Metropolitan Museum of Art and want to quickly identify the category or genre of the artwork by taking a picture of it.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "mobilenet_v2_100_224", "api_call": "hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "api_arguments": "URL of the 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/tf2-preview/mobilenet_v2/classification/4')\ninput_image = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))\ninput_tensor = tf.keras.preprocessing.image.img_to_array(input_image)\ninput_tensor = tf.expand_dims(input_tensor, 0)\ninput_tensor = tf.keras.applications.mobilenet_v2.preprocess_input(input_tensor)\npredictions = model(input_tensor)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "Pre-trained image classification model using MobileNetV2 architecture with input size of 224x224 pixels."}}


 Could you provide an embedding for my sentence to be used in semantic similarity analysis?\n###Input: Someone is learning how to use a Python library.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Generate embeddings for text", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello, world!'])", "performance": {"dataset": "Various", "accuracy": "N/A"}, "description": "A pre-trained model for generating text embeddings using the Universal Sentence Encoder. It can be used for various NLP tasks like semantic similarity, text classification, and clustering."}}


 Identify the objects in the image for an image indexing system.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\nimage = tf.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, axis=0)\npredictions = model(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image classification model from TensorFlow Hub that can classify images into 1000 different categories using the MobileNetV2 architecture."}}


 We want to create an app that can recognize furniture in images. Find an API that can help us.\n
 Use this API documentation for reference:  {"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')\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.expand_dims(input_image, 0)\ndetections = detector(input_image)", "performance": {"dataset": "COCO", "accuracy": "Not specified"}, "description": "A pre-trained object detection model that can detect multiple objects in an image using TensorFlow Hub."}}


 Transform a list of product titles into numerical vectors to perform sentiment analysis.\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": "List of strings"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!']); print(embeddings)", "performance": {"dataset": "STSbenchmark", "accuracy": "85.1%"}, "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."}}


 Create a model that can extract features of an image to use in a fashion-based recommendation system.\n
 Use this API documentation for reference:  {"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', input_shape=(224, 224, 3), trainable=False)\n])\nfeatures = model(images)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "This API provides a pre-trained MobileNetV2 model for extracting image features. It takes an input image of shape (batch_size, 224, 224, 3) and returns a feature vector of shape (batch_size, 1280). The model is trained on the ImageNet dataset and achieves 71.8% accuracy."}}


 We are building an app to help people categorize their personal photos into categories. We need to extract features from the images.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "(224, 224, 3)", "output_shape": "(1280,)"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow-hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\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.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)\nfeatures = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture, which is optimized for performance on mobile devices and other resource-constrained environments. It can be used for various image classification tasks, transfer learning, or as a feature extractor for other models."}}


 Design a machine learning model to recognize dog breeds. We need a feature extractor from MobileNet V2 trained on ImageNet dataset.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained model", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": "URL of the pre-trained model", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": ""}, "description": "A pre-trained image feature vector model using MobileNet V2 architecture with input size 224x224 and trained on ImageNet dataset."}}


 I want to find the similarity between user-generated restaurant reviews, so I need to transform these reviews into an appropriate format.\n
 Use this API documentation for reference:  {"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 0.77"}, "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."}}


 Compile a report on ongoing political conflicts in the Middle East, using an embedding to identify the key topics.\n
 Use this API documentation for reference:  {"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": "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", "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."}}


 Our school wants to analyse students' Reddit comments to find relevant topics. We need vectors to represent their comments for further processing.\n
 Use this API documentation for reference:  {"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 0.803"}, "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."}}


 Recommend me what type of plants are suitable for my garden based on the image input from the garden.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models for image classification", "api_name": "mobilenet_v2", "api_call": "hub.load('https://tfhub.dev/google/tf2-preview/mobilenet_v2/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/tf2-preview/mobilenet_v2/classification/4')\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained MobileNetV2 model for image classification using TensorFlow Hub"}}


 I am building a chatbot for a cosmetics store and want to understand the semantic similarity between different customer queries.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Text embedding model", "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!'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation of 0.78"}, "description": "A text embedding model that encodes text into 512-dimensional vectors, trained on a variety of data sources and tasks."}}


 From a collection of images, we want to extract a feature vector for each image using MobileNetV2.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import numpy as np\nimport 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 = np.random.rand(1, 224, 224, 3).astype(np.float32)\nfeature_vector = model.predict(image)\nprint(feature_vector.shape)", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This API provides a pre-trained MobileNetV2 model for extracting feature vectors from images. The input images should have a size of 224x224 and 3 color channels. The output feature vector has a dimension of 1280."}}


 Find and display Cosine Similarity between two different 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_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 0.80"}, "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. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 Help me measure the semantic similarity between two sentences, so I can find how closely related they are in meaning.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "STSbenchmark", "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."}}


 Transform the following text into its corresponding high-dimensional vector: \"I love exploring new places and trying different cuisines.\"\n###Input: I love exploring new places and trying different cuisines.\n
 Use this API documentation for reference:  {"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_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "STS Benchmark", "accuracy": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 To build an image recognition system, we need a pre-trained model to extract features from images. Provide the API call for this.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Generate image feature vectors", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4", "output_shape": "[1280]", "trainable": "False"}, "python_environment_requirements": ["tensorflow>=2.0.0", "tensorflow-hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                    output_shape=[1280],\n                    trainable=False),\n    tf.keras.layers.Dense(5, activation='softmax')\n])\nmodel.build([None, 224, 224, 3])\nmodel.summary()", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model using MobileNet V2 architecture on ImageNet dataset. Can be used for transfer learning and generating image feature vectors."}}


 I have a list of sentences that I want to preprocess and make ready to be fed to a machine learning model. Can you do that?\n
 Use this API documentation for reference:  {"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."}}


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


 I am inspecting images of license plates and need to check which ones have plates on them\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/openimages_v4/ssd/mobilenet_v2/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow>=2.0", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1'\ndetector = hub.load(model_id)\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file('path/to/image.jpg'))\nimage_tensor = tf.expand_dims(image_tensor, 0)\nresult = detector(image_tensor)\nprint(result)", "performance": {"dataset": "Open Images v4", "accuracy": "0.545 mAP"}, "description": "A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates."}}


 As a programmer, I want to analyze images and determine what objects are in them.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Detect objects in images", "api_name": "ssd_mobilenet_v2", "api_call": "hub.load('https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1')", "api_arguments": "image, score_threshold", "python_environment_requirements": "Python 3, TensorFlow 2.x, TensorFlow Hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\ndetector = hub.load('https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1')\nresult = detector(image, score_threshold=0.5)", "performance": {"dataset": "Open Images v4", "accuracy": "mAP@0.5IOU = 32.8%"}, "description": "A pre-trained SSD MobileNet V2 model for object detection in images, trained on the Open Images v4 dataset. Detects objects and outputs their bounding boxes and class labels with associated confidence scores."}}


 I have a few news articles and I want to see if they're similar. I need a tool to determine the similarity between them.\n
 Use this API documentation for reference:  {"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 a wide range of tasks such as semantic similarity, text classification, clustering, and other natural language processing tasks."}}


 I need to find similar listings for an Airbnb property, can you find its vector representation for further comparison?\n
 Use this API documentation for reference:  {"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": "multiple", "accuracy": "varies"}, "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 useful information from an image for further processing by minimizing the input.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "image_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5')", "api_arguments": "input_shape, input_tensor, trainable, 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/inception_v3/feature_vector/5',\n                   input_shape=(299, 299, 3),\n                   trainable=False)\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(299, 299))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\nfeatures = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "78.1%"}, "description": "A pre-trained model that extracts feature vectors from images using Inception V3 architecture"}}


 We need to incorporate an API to differentiate between species of flowers.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_050_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_050_224/feature_vector/4')", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[1280]"}, "python_environment_requirements": {"tensorflow": ">= 2.0", "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_050_224/feature_vector/4',\n                   input_shape=(224, 224, 3),\n                   trainable=False),\n    tf.keras.layers.Dense(10, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "A pre-trained image feature vector model that extracts useful features from input images using the MobileNetV2 architecture with a width multiplier of 0.5 and an input size of 224x224 pixels."}}


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


 Can you tell me the breed of my dog if I send you a picture of it? \n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Load and use pre-trained models for image classification", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/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": "N/A"}, "description": "Load and use a pre-trained image classification model from TensorFlow Hub to classify images."}}


 I need to sort my photos into different folders, based on what the main object is in each photo.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained models for image classification", "api_name": "model_id", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/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/mobilenet_v2_100_224/classification/4'\nmodel = tf.keras.Sequential([\n    hub.KerasLayer(model_id, input_shape=(224, 224, 3), trainable=True)\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model.predict(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "Top-1 Accuracy: 71.0%, Top-5 Accuracy: 90.1%"}, "description": "Pre-trained image classification models available on TensorFlow Hub for easy integration and usage."}}


 I want to make a recommendation system for similar images, can you recommend an API for image feature extraction?\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": "[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                   output_shape=(1280,),\n                   trainable=False)\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This API provides a feature vector for images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have shape (224, 224, 3), and the output feature vector has 1280 dimensions. The model is available on TensorFlow Hub and can be used for transfer learning applications."}}


 Analyze the following list of tweets and create a vector to find if two of them have similar meaning.\n###Input: [\"I love going to the beach\", \"The beach is my favorite place to visit\", \"What a good day for ice cream\"]\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "input_text", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello, world!', 'My name is John Doe.'])", "performance": {"dataset": "STS Benchmark", "accuracy": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 I need to analyze numerous article headlines and sort them based on their similarity.\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."}}


 We want to know which animal we will find in the picture taken during our vacation.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained models for image classification", "api_name": "model_id", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/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/classification/4'\nmodel = tf.keras.Sequential([\n    hub.KerasLayer(model_id, input_shape=(224, 224, 3))\n])\n# Load an image and make predictions\nfrom PIL import Image\nimport numpy as np\nimage = Image.open('path/to/your/image.jpg')\nimage = image.resize((224, 224))\ninput_data = np.array(image) / 255.0\ninput_data = np.expand_dims(input_data, axis=0)\npredictions = model.predict(input_data)\nprint(predictions)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture from TensorFlow Hub."}}


 Summarize an article by representing its content in a high-dimensional vector format.\n
 Use this API documentation for reference:  {"domain": "Text preprocessing", "framework": "TensorFlow Hub", "functionality": "Text preprocessing and 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": "Pearson correlation coefficient of 0.78"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. It is pre-trained on a large corpus of text and can be fine-tuned on specific tasks."}}


 Show me how I can convert this sentence \"I enjoy machine learning and natural language processing\" into a high-dimensional vector.\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."}}


 Provide a solution to recognize different types of fruits through image processing.\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]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model based on MobileNetV2, designed for efficient feature extraction from images."}}


 I have recorded some animal sounds and I want to analyze them to identify the animal species.\n
 Use this API documentation for reference:  {"domain": "Audio embedding", "framework": "TensorFlow Hub", "functionality": "Audio processing", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "", "performance": {"dataset": "", "accuracy": ""}, "description": "Load and use an audio embedding model from TensorFlow Hub to process audio data."}}