 I own a collection of paintings and I need to categorize them based on their depicted object.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "inception_v3", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/inception_v3/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\ndata_root = tf.keras.utils.get_file('flower_photos','https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz', untar=True)\nimage_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255)\nimage_data = image_generator.flow_from_directory(str(data_root), target_size=IMAGE_SHAPE)\nfeature_extractor_url = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nfeature_extractor_layer = hub.KerasLayer(feature_extractor_url, input_shape=(299,299,3))\nfeature_extractor_layer.trainable = False\nmodel = tf.keras.Sequential([\n  feature_extractor_layer,\n  tf.keras.layers.Dense(image_data.num_classes, activation='softmax')\n])\nmodel.compile(optimizer=tf.keras.optimizers.Adam(), loss='categorical_crossentropy', metrics=['acc'])\nsteps_per_epoch = np.ceil(image_data.samples/image_data.batch_size)\nhist = model.fit(image_data, epochs=6, steps_per_epoch=steps_per_epoch)", "performance": {"dataset": "ImageNet", "accuracy": "0.779"}, "description": "Inception V3 model for image classification, pretrained on ImageNet dataset."}}


 I want to create an app that suggests dog breeds based on the images uploaded by users. Recommend an API for me to use.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "model_id", "api_call": "tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/classification/5')])", "api_arguments": {"handle": "string", "trainable": "boolean (optional)"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = tf.keras.Sequential([hub.KerasLayer(model_id)])\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.keras.applications.inception_v3.preprocess_input(image)\nimage = tf.expand_dims(image, axis=0)\npredictions = model.predict(image)", "performance": {"dataset": "ImageNet", "accuracy": "0.779"}, "description": "Load a pre-trained Inception V3 model from TensorFlow Hub for image classification."}}


 Can you help me classify hotel reviews as positive or negative?\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": "A pre-trained model that encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks"}}


 My kid took a photo of an animal. Please tell me its species.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification model", "api_name": "mobilenet_v2_140_224", "api_call": "hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "api_arguments": {"handle": "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4", "input_shape": "(224, 224, 3)"}, "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/tf2-preview/mobilenet_v2/classification/4', input_shape=(224,224,3))\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model.predict(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture with input size of 224x224 pixels."}}


 I have images of food items and I need help categorizing them as per the ImageNet categories.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "imagenet_mobilenet_v2_100_224_classification", "api_call": "tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')])", "api_arguments": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4"}, "python_environment_requirements": {"tensorflow": "2.x", "tensorflow_hub": "0.12.0"}, "example_code": "", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset with 100% depth and 224x224 input size."}}


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


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


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


 We want to analyze paintings based on their visual characteristics and need a way to represent them numerically.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Convert images into feature vectors", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"python_version": "3", "tensorflow_version": ">=2.0.0", "tensorflow_hub_version": ">=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])", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image feature vector model that converts images into a 1280-dimensional feature vector using MobileNetV2 architecture trained on ImageNet dataset."}}


 Our company has a lot of images that we need to categorize into one of 1000 classes. We're looking for a pretrained model that could help.\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.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_tensor = tf.image.resize(image, (224, 224))\npredictions = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "Top-1 Accuracy: 71.0%, Top-5 Accuracy: 90.1%"}, "description": "A pre-trained image classification model using TensorFlow Hub that can classify images into 1000 different categories. The model is based on the MobileNetV2 architecture and has been trained on the ImageNet dataset."}}


 I have a collection of images and I need to categorize them. Please generate predictions using a pre-trained model.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "mobilenet_v2", "api_call": "hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')", "api_arguments": {"handle": "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4", "output_shape": "[1001]"}, "python_environment_requirements": {"tensorflow": "2.0", "tensorflow_hub": "0.7"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4', output_shape=[1001])\n# Load an image\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\nimage = tf.expand_dims(image, 0)\n# Make predictions\npredictions = model(image)\n# Decode predictions\ndecoded_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=5)\nfor i, (imagenet_id, label, score) in enumerate(decoded_predictions[0]):\n    print('{}. {}: {:.2f}%'.format(i + 1, label, score * 100))", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "MobileNetV2 is a lightweight deep learning model for image classification. It is pre-trained on the ImageNet dataset and can be loaded using TensorFlow Hub."}}


 A user wants to know how many objects are in an image they have taken with their phone. Calculate the count by processing the image.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Object detection in images", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1'\ndetector = hub.load(model_id)\nimage = tf.image.decode_jpeg(tf.io.read_file('image.jpg'))\nresult = detector(image[np.newaxis, ...])", "performance": {"dataset": "OpenImages V4", "accuracy": "Not specified"}, "description": "Detect objects in an image using TensorFlow Hub and the Faster R-CNN model with Inception ResNet V2"}}


 Create a program that alerts the user when a dog is at the door, by identifying the dogs in the pictures from a live webcam feed.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Object detection in images", "api_name": "ssd_mobilenet_v2", "api_call": "hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1')", "api_arguments": {"image": "Input image tensor", "num_detections": "Number of detected objects", "detection_boxes": "Bounding boxes for detected objects", "detection_classes": "Class labels for detected objects", "detection_scores": "Confidence scores for detected objects"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\ndetector = hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1')\n# Preprocess image\ninput_image = tf.image.resize(image, (640, 640))\ninput_image = tf.expand_dims(input_image, axis=0)\n# Run detector\noutput = detector(input_image)\n# Extract results\nnum_detections = output['num_detections'].numpy()\ndetection_boxes = output['detection_boxes'].numpy()\ndetection_classes = output['detection_classes'].numpy()\ndetection_scores = output['detection_scores'].numpy()", "performance": {"dataset": "COCO", "accuracy": "mAP 0.32"}, "description": "A pre-trained object detection model based on the SSD Mobilenet V2 architecture, capable of detecting multiple objects in an image. The model is trained on the COCO dataset and achieves a mean Average Precision (mAP) of 0.32."}}


 I want to analyze a group of sentences, find if there are similar ones so that I can group them together. Help me get embeddings for these sentences.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "List of strings"}, "python_environment_requirements": {"tensorflow": "2.0.0", "tensorflow_hub": "0.8.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world', 'This is a test'])", "performance": {"dataset": "STS Benchmark", "accuracy": "Pearson correlation coefficient of 0.78"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Are there any good pretrained image classifiers? I want to try one on this image and get top 5 predictions.\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/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow-hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\ninput_image = tf.expand_dims(input_image, 0)\npredictions = model(input_image)\n", "performance": {"dataset": "ImageNet", "accuracy": "0.779"}, "description": "This API provides pre-trained image classification models using TensorFlow Hub for various architectures like Inception V3, MobileNet, and others. These models are trained on the ImageNet dataset and can be used for transfer learning or fine-tuning on custom datasets."}}


 We are creating an app to recognize different dog breeds. I need to find a pre-trained model capable of helping me with this task.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4", "trainable": "False"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                   input_shape=(224, 224, 3),\n                   trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(train_data, train_labels, epochs=10, validation_data=(val_data, val_labels))", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained MobileNetV2 model for extracting image feature vectors. The model takes an image of size 224x224 and returns a 1280-dimensional feature vector."}}


 I am starting an e-commerce platform, and I want to build a feature vector database of different products to identify and categorize them.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extracts feature vectors from images", "api_name": "imagenet-mobilenet_v2", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "(224, 224, 3)", "output_shape": "(1280,)"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4', input_shape=(224, 224, 3), 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_array = tf.expand_dims(image_array, 0)\nfeature_vector = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "Top-1 Accuracy: 71.9%, Top-5 Accuracy: 91.0%"}, "description": "A pre-trained image feature vector model that extracts feature vectors from images using MobileNetV2 architecture. It is trained on the ImageNet dataset and can be used for various image classification tasks."}}


 I have pictures of animals and I want to identify them by their species.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained Image Classifier", "api_name": "mobilenet_v2", "api_call": "tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4')])", "api_arguments": {"url": "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4", "output_shape": "[1001]"}, "python_environment_requirements": {"tensorflow": "2.x", "tensorflow_hub": "0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4', output_shape=[1001])])\nmodel.build([None, 224, 224, 3])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on TensorFlow Hub. It can be used to classify images into 1000 different categories with high accuracy."}}


 We're creating a database of different foods, and I want to generate feature vectors for each food item from their images.\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": {"source": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4", "input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                   input_shape=(224,224,3))\n])\nimage = tf.keras.preprocessing.image.load_img('example_image.jpg', target_size=(224, 224))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\nfeature_vector = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained model that generates feature vectors from images using MobileNetV2 architecture with 100% depth and 224x224 input size. The model is trained on the ImageNet dataset and achieves 71.9% top-1 accuracy."}}


 I need to build a customer support chatbot that automatically replies to user queries. How can I represent sentences as numerical vectors for semantic similarity 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": "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!'])", "performance": {"dataset": "STS-Benchmark", "accuracy": "0.803"}, "description": "A deep averaging network (DAN) encoder that maps text inputs to 512-dimensional embeddings. Useful for various NLP tasks such as sentiment analysis, semantic similarity, and document clustering."}}


 In order to build a dogs versus cats classifier, I need a good feature extractor for images. Can you provide me with one?\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Extract feature vector from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer(\"https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\")", "api_arguments": {"input_shape": "[224, 224, 3]", "output_shape": "[1280]"}, "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(\"https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\",\n                   input_shape=(224, 224, 3),\n                   output_shape=[1280],\n                   trainable=False),\n    tf.keras.layers.Dense(1, activation='sigmoid')\n])\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "This API provides a pre-trained model to extract feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. It can be used for various image classification tasks and transfer learning."}}


 Analyze an image we have taken and categorize the primary subject of that image.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "imagenet_mobilenet_v2_100_224_classification", "api_call": "tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')])", "api_arguments": {"model_url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0", "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/classification/4')\n])\nimage = tf.keras.preprocessing.image.load_img('path_to_your_image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\npredictions = model.predict(image_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset."}}


 For analyzing customer reviews, I want to convert them into high-dimensional embeddings that we will use for sentiment analysis.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text data for machine learning models", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "List of sentences or phrases"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world', 'Embedding sentences with TensorFlow Hub'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.78"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs. It is trained on a variety of data sources and a variety of tasks with the aim of dynamically accommodating a wide variety of natural language understanding tasks."}}


 Please determine which two sentences from a group are semantically similar based on their embeddings.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text data", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input_text": "List of sentences or phrases"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": {"import tensorflow as tf": "", "import tensorflow_hub as hub": "", "embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')": "", "sentences = ['This is a test sentence.', 'Another example sentence.']": "", "embeddings = embed(sentences)": "", "print(embeddings)": ""}, "performance": {"dataset": "STSbenchmark", "accuracy": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Given a dataset of images, we need to extract their feature vectors for use in an image search engine application.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet-feature-vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/feature_vector/1')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/feature_vector/1", "trainable": "False"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\ninput = tf.keras.layers.Input(shape=(224, 224, 3))\nfeatures = hub.KerasLayer('https://tfhub.dev/google/imagenet/feature_vector/1', trainable=False)(input)\nmodel = tf.keras.Model(inputs=input, outputs=features)", "performance": {"dataset": "ImageNet", "accuracy": "N/A"}, "description": "This API provides a pre-trained image feature vector model for extracting features from images using TensorFlow Hub. The model is based on the ImageNet dataset and can be used for various image-related tasks such as classification, detection, and segmentation."}}


 I want an AI to classify what's inside a photo by loading a pre-trained model from TensorFlow Hub.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\n", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load a pre-trained image classification model from TensorFlow Hub using the specified model_id."}}


 Preserve this art piece in a digital archive. Extract its features for future analysis.\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", "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], trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture for extracting features from images."}}


 How can we extract image features to compare the similarity between two interior designs?\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."}}


 We have a survey containing phrases on the job market. We want to get a visualization of the job market. Can you provide an embedding of the survey phrases?\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_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['text to embed'])", "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."}}


 Try to identify the object in the image using a pre-trained model built on the TensorFlow Hub platform.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": "URL of the pre-trained model", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\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": "71.9%"}, "description": "A pre-trained image classification model using MobileNet V2 architecture with 224x224 input size, available on TensorFlow Hub"}}


 In a warehouse, we would like to use a model to identify items stocked on the shelves by detecting objects in images.\n
 Use this API documentation for reference:  {"domain": "Image object detection", "framework": "TensorFlow Hub", "functionality": "Object detection in images", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2')", "api_arguments": {"model_id": "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/tensorflow/ssd_mobilenet_v2/2'\ndetector = hub.load(model_id)\nimage = tf.keras.preprocessing.image.load_img('image.jpg')\nimage = tf.keras.preprocessing.image.img_to_array(image)\nresult = detector(image[np.newaxis, ...])", "performance": {"dataset": "COCO", "accuracy": "Unknown"}, "description": "A pre-trained TensorFlow Hub model for detecting objects in images using the Single Shot MultiBox Detector (SSD) with MobileNetV2 backbone."}}


 I have a collection of butterfly images, so please find the image feature vectors for me.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Generate feature vectors for images", "api_name": "imagenet_mobilenet_v3_large_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5')", "api_arguments": {"input_shape": "(224, 224, 3)", "output_shape": "(1280,)"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5', input_shape=(224, 224, 3), output_shape=(1280,))\n])\n# Load an image and preprocess it\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\nimage_array = tf.expand_dims(image_array, 0)\n# Generate feature vector\nfeature_vector = model.predict(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "75.3%"}, "description": "This API provides a pre-trained image feature vector model, MobileNetV3-Large, from TensorFlow Hub. The model is trained on the ImageNet dataset and can be used to generate feature vectors for images of size 224x224 with 3 channels."}}


 I need to create a program that finds how similar two given sentences are. To do this, I first want to encode the sentences 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 input"}, "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'])", "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."}}


 We need to find similar phrases within a set of sentences. To do this, first, we will encode these sentences into vectors.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into a high-dimensional vector", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "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!', 'Embedding with TensorFlow Hub'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.78"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 Translate the following English text into a semantic embedding: \"The quick brown fox jumps over the lazy dog.\"\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "list of sentences"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['sentence1', 'sentence2'])", "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 various natural language processing tasks. It is pre-trained on a variety of data sources and can be fine-tuned for specific tasks."}}


 Compare the similarity of the following two sentences: \"The quick brown fox jumps over the lazy dog,\" and \"A speedy reddish-orange mammal leaps over a resting canine.\"\n###Input: \"The quick brown fox jumps over the lazy dog\", \"A speedy reddish-orange mammal leaps over a resting canine.\"\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "input_text", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!']); print(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 We would like to use transfer learning for an image classification problem. Extract features from images using a pre-trained feature vector model.\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": ">=0.12.0"}, "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 image feature vector model based on MobileNetV2 architecture, which can be used for image classification or as a feature extractor for transfer learning."}}


 Calculate the similarity between two sentences for a recommendation system based on the content.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "text input", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!']); print(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Calculate the semantic similarities between the product descriptions of two items I found on an e-commerce site.\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."}}


 As we need to find the similarity among the users' reviews, we should preprocess the raw text to remove unwanted characters and get the corresponding text embedding.\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."}}


 A real-estate company wants to use computer vision to classify images of homes into multiple categories like 'modern', 'rustic', etc. Provide a suitable API to extract image feature vectors for building the classifier.\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), trainable=False),\n    tf.keras.layers.Dense(num_classes, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image feature vector model based on MobileNetV2 architecture, which is optimized for mobile and edge devices. It is trained on ImageNet dataset and can be used for various image classification tasks."}}


 I have a list of customer reviews and want to group them based on their similarity.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "List of strings"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!', 'This is a test sentence'])", "performance": {"dataset": "Various", "accuracy": "Not specified"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. It is trained on a variety of data sources and a range of tasks."}}


 Analyze customer feedback and group them into positive and negative sentiments.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation of 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 To classify silhouettes, extract image features with a pre-trained model then build a new classification model.\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."}}


 Measure the similarity between two strings, we want it encoded representations.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embeddings", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "text"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.8.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!'])", "performance": {"dataset": "STS benchmark", "accuracy": "0.78 Pearson correlation"}, "description": "A text embedding model that maps text inputs to a 512-dimensional vector space."}}


 I am building a news article clustering application. I need to get the embeddings of each news article to compare their similarity.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text data", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"url": "https://tfhub.dev/google/universal-sentence-encoder/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import 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 (USE) is a pre-trained model that encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks. It is designed for greater-than-word length text like sentences, phrases or short paragraphs."}}


 Help me find similar images in a large database for a given 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": "image", "output": "feature_vector"}, "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    tf.keras.layers.Dense(1)\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": "This API provides a compact representation of an image by extracting feature vectors using MobileNetV2 architecture pretrained on ImageNet dataset. It can be used for tasks like image classification, object detection, and image similarity."}}


 I want to recognize who is who in my photos, so create a model to turn the photos into vectors first.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "inception_v3", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5')", "api_arguments": {"input": "image", "output": "feature_vector"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12"}, "example_code": "import tensorflow_hub as hub\nimport tensorflow as tf\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5',\n                   input_shape=(299,299,3))\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, 0)\nfeatures = model.predict(image)", "performance": {"dataset": "ImageNet", "accuracy": "77.9%"}, "description": "Feature extraction from images using Inception V3 model trained on ImageNet dataset"}}


 Analyze the pictures animals have taken and sort them into different animal categories. To do this, derive the feature vectors from their images so that I can train a classifier.\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", "output_shape": [1280], "trainable": "False"}, "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])", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "Extracts feature vectors from images using MobileNetV2 architecture pretrained on ImageNet dataset"}}


 I am creating a question-answering system and need to find the closest matching question in my database to a new user's question. Please provide the embeddings for the questions.\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!'])", "performance": {"dataset": "STS benchmark", "accuracy": "0.803"}, "description": "A text embedding model that encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 I took some pictures during my vacation and now I want to create a collection of images and organize them based on image features.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction from images", "api_name": "imagenet_mobilenet_v2_100_224_feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "(224, 224, 3)", "output_shape": "(1280,)"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "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"}}


 I need to classify plant species using a machine learning model to know which type they are. Can I use this model to classify images of plants?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)", "performance": {"dataset": "ImageNet", "accuracy": null}, "description": "Load an image classification model from TensorFlow Hub using the model_id."}}


 We want to cluster similar product reviews to understand the customer experience better.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "URL of the model", "python_environment_requirements": "TensorFlow, TensorFlow Hub", "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello, world!', 'How are you?'])\nprint(embeddings)", "performance": {"dataset": "SNLI, MultiNLI", "accuracy": "86.9% on SNLI, 78.9% on MultiNLI"}, "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."}}


 Write a code for embedding the given sentences into high-dimensional vectors to find similarity in their meanings.\n###Input: [\"I have a cat.\", \"My pet is a feline.\"]\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world']); print(embeddings)", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "A deep-learning model for embedding text into high-dimensional vectors that can be used for various natural language processing tasks"}}


 Assist me in creating a model for image classification by extracting features from 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": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4", "trainable": "False"}, "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_100_224/feature_vector/4', trainable=False),\n  tf.keras.layers.Dense(10, activation='softmax')\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "This API provides a pre-trained image feature vector model based on MobileNetV2 architecture. It extracts features from images and can be used as a base model for various image classification tasks."}}


 We are facing an issue while categorizing different plants species in our garden. Could you please assist and identify them for us?\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models for image classification", "api_name": "imagenet_mobilenet_v2_130_224_classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow_hub as hub\nimport tensorflow as tf\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')\nimage = tf.zeros([1, 224, 224, 3])  # A batch of images with shape [batch_size, height, width, 3].\nlogits = model(image)  # Logits with shape [batch_size, num_classes].", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "A pre-trained MobileNetV2 model for classifying images using TensorFlow Hub."}}


 I want to create a custom image classifier for a project but don't want to train a model from scratch. What technology can I leverage to achieve this?\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."}}


 We're organizing a car show, and we want to classify the different car models automatically using an application.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification model", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": {"model_id": "URL or path to the TensorFlow Hub model"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\n# Load and preprocess an image\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(299, 299))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, axis=0)\n# Make predictions\npredictions = model(image)\nclass_index = tf.argmax(predictions, axis=-1).numpy()[0]", "performance": {"dataset": "ImageNet", "accuracy": "Top-1: 77.9%, Top-5: 93.7%"}, "description": "A pre-trained image classification model using Inception V3 architecture, trained on ImageNet dataset."}}


 I want to create an AI-driven customer service chatbot. How can I create a similarity measure for the customer queries and the possible answers?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional vectors", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world']); print(embeddings)", "performance": {"dataset": "STS-Benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. It is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs."}}


 For understanding the contents of user reviews, I would like to transform comments into numerical representations to be utilized in a machine learning model.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional space", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "input_text", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow_hub as hub; embed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4'); embeddings = embed(['Hello world!'])", "performance": {"dataset": "Semantic Textual Similarity", "accuracy": "0.8"}, "description": "This API provides a pre-trained model to convert text into high-dimensional space, which can be used for various natural language processing tasks such as semantic similarity, clustering, and classification."}}


 I need to create chatbot responses for similar questions. Convert these sentences into vectors that can be clustered.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into a high-dimensional vector", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!'])", "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 want to compare a large image dataset of different car models to suggest visually related recommendations to our users.\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": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n  hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4', input_shape=[224, 224, 3])\n])\n# Load an image and preprocess it\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, 0)\n# Extract features from the image\nfeatures = model.predict(image)", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained deep learning model that extracts features from images using the MobileNetV2 architecture."}}


 I am working on a project and I need to find similar images in my dataset. Extract feature vectors of images for comparison.\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": ">=0.8.0"}, "example_code": "model = tf.keras.Sequential([hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4', input_shape=[224,224,3], trainable=False)])", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "This module uses the MobileNet_v2_100_224 architecture to extract image features. It takes an input image of shape [224, 224, 3] and outputs a feature vector of shape [1280]."}}


 We need to generate embeddings for product descriptions to cluster similar products together. Get an embedding for each product description.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text into high-dimensional space", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": {"input": "List of strings"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!', 'My name is John Doe'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Capture the most populated areas of a city from aerial images taken from drones.\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": "A unique identifier for the pre-trained model"}, "python_environment_requirements": {"tensorflow": ">=2.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2'\ndetector = hub.load(model_id)\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, 0)\nresults = detector(image)\nprint(results['detection_boxes'])\nprint(results['detection_classes'])\nprint(results['detection_scores'])", "performance": {"dataset": "COCO", "accuracy": "mAP (mean average precision)"}, "description": "A pre-trained model for detecting objects in images using TensorFlow Hub"}}


 Determine what objects are visible in a given image.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image classification using pre-trained models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": ["model_id"], "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, 0)\nimage_array = tf.keras.applications.mobilenet_v2.preprocess_input(image_array)\npredictions = model(image_array)", "performance": {"dataset": "ImageNet", "accuracy": "71.8%"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset."}}


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


 Find out what's in the picture and recommend related products to customers.\n
 Use this API documentation for reference:  {"domain": "Image feature vector", "framework": "TensorFlow Hub", "functionality": "Feature extraction", "api_name": "imagenet-mobilenet_v2_100_224-feature_vector", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4')", "api_arguments": {"input_shape": "[None, 224, 224, 3]", "output_shape": "[None, 1280]"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.12.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4',\n                   input_shape=(224, 224, 3),\n                   output_shape=(1280,))\n])", "performance": {"dataset": "ImageNet", "accuracy": "71.9%"}, "description": "A pre-trained image feature vector model based on MobileNet V2 with 100% depth and 224x224 input size. Useful for extracting features from images."}}


 Calculate the similarity between two sentences using their embeddings. \n###Input: \"The cat is a domestic animal.\", \"The cat is a household pet.\"\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_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "embeddings = embed(['Hello world', 'Embedding with TensorFlow Hub'])", "performance": {"dataset": "Various", "accuracy": "High"}, "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."}}


 How can we transform sentences into semantic vectors for a similarity search engine?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text for various NLP tasks", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "embeddings = embed(['Hello, world!'])", "performance": {"dataset": "Various", "accuracy": "Not specified"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Generate a detailed map caption of images captured by a drone over a natural disaster-affected area.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models", "api_name": "imagenet_mobilenet_v2_100_224_classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\ndef predict(image):\n    logits = model(image)\n    return logits", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "A pre-trained MobileNetV2 model for image classification on the ImageNet dataset, with a 224x224 input size and 100% depth multiplier. This model is optimized for low-latency and small size, making it suitable for mobile and edge devices."}}


 I took a photo of my dog at the park, and I want to know its breed.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Image recognition", "api_name": "imagenet_mobilenet_v2_130_224_classification", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')", "api_arguments": {"url": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4')\n# Load and preprocess an image\nimage = tf.keras.preprocessing.image.load_img('path/to/your/image.jpg', target_size=(224, 224))\nimage = tf.keras.preprocessing.image.img_to_array(image)\nimage = tf.expand_dims(image, 0)\n# Run the model\npredictions = model(image)\n# Decode the predictions\nclass_names = tf.keras.applications.mobilenet_v2.decode_predictions(predictions, top=1)\nprint(class_names)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "A pre-trained image classification model using MobileNetV2 architecture on the ImageNet dataset."}}


 Find out what kind of trees are in our park using a pre-trained deep learning model.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading a pre-trained model", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'\nmodel = hub.load(model_id)\n# Load image and preprocess\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# Make prediction\npredictions = model(tf.expand_dims(image, 0))\n# Decode predictions\nclass_names = tf.keras.applications.inception_v3.decode_predictions(predictions.numpy(), top=5)\nfor class_name in class_names[0]:\n    print(class_name)", "performance": {"dataset": "ImageNet", "accuracy": "0.779"}, "description": "This API allows you to load a pre-trained Inception V3 model from TensorFlow Hub for image classification tasks. The model is trained on the ImageNet dataset and achieves an accuracy of 77.9%."}}


 In order to analyze art pieces, extract and save feature vectors of famous paintings.\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", "tensorflow_hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4', input_shape=[224, 224, 3])\n])\nimage = tf.keras.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": "This API extracts feature vectors from images using the MobileNetV2 model pretrained on the ImageNet dataset. It can be used for tasks like image classification, object detection, and image similarity."}}


 I would like to create a mobile app to quickly identify the type of tree based on a photo of its leaves.\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"], "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/2'\nmodel = hub.load(model_id)", "performance": {"dataset": "ImageNet", "accuracy": "Not provided"}, "description": "Load pre-trained image classification models from TensorFlow Hub for transfer learning and fine-tuning."}}


 A farmer wants to analyze the images of soil to classify the type and quality of soil.\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_100_224_classification", "api_call": "hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": {"handle": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4", "trainable": "False"}, "python_environment_requirements": {"tensorflow": ">=2.0.0", "tensorflow-hub": ">=0.6.0"}, "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel = tf.keras.Sequential([\n    hub.KerasLayer('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')\n])\nimage = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))\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 image classification model using MobileNetV2 architecture trained on ImageNet dataset. The model can be used to classify images into 1000 different categories."}}


 Identify the contents of an image by loading a pretrained model and classify it.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Loading pre-trained models for image classification", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/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)\nimage = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(224, 224))\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\ninput_array = tf.expand_dims(input_array, 0)\npredictions = model(input_array)\n", "performance": {"dataset": "ImageNet", "accuracy": "71.0%"}, "description": "Load a pre-trained model for image classification from TensorFlow Hub and use it to classify images."}}


 While traveling, I want to use the API to detect various landmarks and monuments by snapping photos of them with my smartphone.\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_130_224/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/mobilenet_v2_130_224/classification/5'\nmodel = hub.load(model_id)\nimage = tf.random.uniform((1, 224, 224, 3))\nprediction = model(image)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "Load pre-trained image classification models from TensorFlow Hub for various tasks such as object recognition, fine-grained classification, and more."}}


 Design a system for customer reviews classification based on the level of satisfaction.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!'])\nprint(embeddings)", "performance": {"dataset": "STS Benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Provide me with a way to measure semantic similarity between two input sentences.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs."}}


 In the pet store, we discovered an unknown animal. Help us identify the animal.\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.load('https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/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/mobilenet_v2_130_224/classification/5'\nmodel = hub.load(model_id)\ninput_image = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(224, 224))\ninput_array = tf.keras.preprocessing.image.img_to_array(input_image)\ninput_batch = tf.expand_dims(input_array, 0)\npredictions = model(input_batch)\nprint(predictions)", "performance": {"dataset": "ImageNet", "accuracy": "Not specified"}, "description": "A pre-trained model for image classification using TensorFlow Hub. This example demonstrates the use of the MobileNet V2 model to classify images."}}


 We have a set of movie reviews and need to generate embeddings for each review to perform sentiment analysis.\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embedding text using TensorFlow", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": ["input_text"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!'])", "performance": {"dataset": "STS benchmark", "accuracy": "Pearson correlation coefficient of 0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 Help me load a pre-trained image classification model to categorize fruits for my new dietary app project.\n
 Use this API documentation for reference:  {"domain": "Image classification", "framework": "TensorFlow Hub", "functionality": "Pre-trained image classification models", "api_name": "model_id", "api_call": "hub.load('https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4')", "api_arguments": ["model_id"], "python_environment_requirements": ["tensorflow", "tensorflow_hub"], "example_code": "import tensorflow as tf\nimport tensorflow_hub as hub\nmodel_id = 'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4'\nmodel = hub.load(model_id)\nresult = model(image_tensor)", "performance": {"dataset": "ImageNet", "accuracy": "Varies depending on the model"}, "description": "TensorFlow Hub provides a variety of pre-trained image classification models that can be easily loaded and used for various image classification tasks. The models are trained on the ImageNet dataset and their performance varies depending on the specific model chosen."}}


 I have a collection of images from a fashion show, and I want to extract the features of each image to use in a recommendation engine.\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 am working on analyzing customer reviews of a product, and I want to represent the text data in a way that is easy to work with. What is an API to efficiently convert textual data into numerical representations?\n
 Use this API documentation for reference:  {"domain": "Text embedding", "framework": "TensorFlow Hub", "functionality": "Embed text data", "api_name": "universal-sentence-encoder", "api_call": "hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')", "api_arguments": "URL of the model", "python_environment_requirements": "tensorflow, tensorflow_hub", "example_code": "import tensorflow_hub as hub\nembed = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')\nembeddings = embed(['Hello world!'])", "performance": {"dataset": "STS benchmark", "accuracy": "0.803"}, "description": "The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks."}}


 We are developing an article recommendation system. Preprocess the following article title: \"Latest Advances in Artificial Intelligence and Machine Learning\".\n###Input: Latest Advances in Artificial Intelligence and Machine Learning\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_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": "A pre-trained text encoder that converts text into a 512-dimensional vector. Useful for a wide range of natural language processing tasks."}}