 I have a fashion catalog and I want to find visually similar clothes from the catalog using the images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained model\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture with input size 224x224 and trained on ImageNet dataset.\'}', metadata={})]


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


 Could you identify the object in an image? Which pre-trained model can I use?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/d0/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/d0/1\'\\ndetector = hub.load(model_id)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'N/A\'}, \'description\': \'Load a pre-trained model from TensorFlow Hub for object detection in images.\'}', metadata={})]


 Find out what an image is about by using a pre-trained image classification model, considering its contents.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading a pre-trained model for image classification\', \'api_name\': \'imagenet_mobilenet_v2_130_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not provided\'}, \'description\': \'Load a pre-trained MobileNetV2 model for image classification using TensorFlow Hub.\'}', metadata={})]


 Develop a model for product categories from images I provide.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_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_130_224/classification/4\'\\nmodel = hub.load(model_id)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'N/A\'}, \'description\': \'A pre-trained image classification model available on TensorFlow Hub for classifying images into one of 1000 categories.\'}', metadata={})]


 Can you please provide an API that will help us find semantic similarity between multiple sentences?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Universal Sentence Encoder\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'A list of sentences or phrases\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.80\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 Describe an object in an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 I have a collection of paintings and I'm looking for a way to generate a list of their most common features.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 We have new images containing scenes from different locations. Let's classify these locations by implementing a pre-trained image classification model based on MobileNet V2.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained model for image classification\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'URL of the pre-trained model\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture trained on ImageNet dataset.\'}', metadata={})]


 Can you help extract meaning from a list of sentences by converting them into high-dimensional vectors?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'list of sentences\', \'output\': \'list of embeddings\'}, \'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([\'sentence1\', \'sentence2\'])", \'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.\'}', metadata={})]


 We have a list of YouTube video descriptions, and we want to find similar videos based on the descriptions, so start with getting the embeddings of each description.\n
 Use this API documentation for reference: [Document(page_content='{\'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\': "embeddings = embed([\'Hello world\', \'Embedding with TensorFlow Hub\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803\'}, \'description\': \'A pre-trained model that converts text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 I want a tool to help me count how many cars are parked in our parking lot.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 Can you help me classify the species of a bird that I have in my photo? \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification model\', \'api_name\': \'imagenet-mobilenet_v2_130_224-classification\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\', \'output_shape\': \'[1001]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\ninput_array = tf.keras.preprocessing.image.img_to_array(image)\\ninput_array = tf.expand_dims(input_array, 0)\\n\\npredictions = model.predict(input_array)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.856\'}, \'description\': \'This model is a pre-trained image classification model based on MobileNetV2 architecture. It can classify images into 1000 different categories.\'}', metadata={})]


 Can you help me analyze the contents of a photograph?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 I have some random objects lying around my home office desk. Could you please create a model that can classify these objects for me?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1\'\\ndetector = hub.load(model_id)\\n\\ninput_image = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\ninput_image = tf.image.resize(input_image, (512, 512))\\n\\ninput_tensor = tf.expand_dims(input_image, axis=0)\\n\\nresult = detector(input_tensor)\\n\\n# Display results\\nprint(result)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'0.42 mAP\'}, \'description\': \'A pre-trained TensorFlow Hub model for detecting objects in images using the CenterNet Hourglass architecture\'}', metadata={})]


 Analyze various photograps and determine what objects each one contains.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1\'\\ndetector = hub.load(model_id)\\n\\ninput_image = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\ninput_image = tf.image.resize(input_image, (512, 512))\\n\\ninput_tensor = tf.expand_dims(input_image, axis=0)\\n\\nresult = detector(input_tensor)\\n\\n# Display results\\nprint(result)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'0.42 mAP\'}, \'description\': \'A pre-trained TensorFlow Hub model for detecting objects in images using the CenterNet Hourglass architecture\'}', metadata={})]


 Determine the type of animal we found during the hike. It's important to know if it could be dangerous.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/d0/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/d0/1\'\\ndetector = hub.load(model_id)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'N/A\'}, \'description\': \'Load a pre-trained model from TensorFlow Hub for object detection in images.\'}', metadata={})]


 Expedia wants to recommend hotels based on the image of their rooms. They need a way to extract features from these images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 Automate the process of training a deep learning model to distinguish between images of cats and dogs.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/d0/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/d0/1\'\\ndetector = hub.load(model_id)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'N/A\'}, \'description\': \'Load a pre-trained model from TensorFlow Hub for object detection in images.\'}', metadata={})]


 We need to detect broken objects in an assembly line from images. Suggest an appropriate API call that can help us generate feature vectors for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate feature vectors from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\')", \'api_arguments\': {\'input\': \'image_tensor\', \'output\': \'feature_vector\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\')\\nimage_tensor = tf.zeros([1, 224, 224, 3])\\nfeature_vector = model(image_tensor)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture on TensorFlow Hub. It generates feature vectors from input images for various applications like image classification, object detection, and image similarity.\'}', metadata={})]


 I need a model that can classify different architectural styles in images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained model for image classification\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'URL of the pre-trained model\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture trained on ImageNet dataset.\'}', metadata={})]


 Provide me a pre-trained model to classify imported images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 224, 224, 3])\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Varies depending on the model\'}, \'description\': \'A collection of pre-trained image classification models available on TensorFlow Hub, which can be used to classify images into various categories.\'}', metadata={})]


 I'd like to automatically classify food images at a grocery store to add labels to the products.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_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_130_224/classification/4\'\\nmodel = hub.load(model_id)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'N/A\'}, \'description\': \'A pre-trained image classification model available on TensorFlow Hub for classifying images into one of 1000 categories.\'}', metadata={})]


 Analyze a collection of images to help me recognize my dog's breed.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 Create a feature vector from an image using a pre-trained model for use in an image similarity search.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate feature vectors from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\')", \'api_arguments\': {\'input\': \'image_tensor\', \'output\': \'feature_vector\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\')\\nimage_tensor = tf.zeros([1, 224, 224, 3])\\nfeature_vector = model(image_tensor)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture on TensorFlow Hub. It generates feature vectors from input images for various applications like image classification, object detection, and image similarity.\'}', metadata={})]


 I have an app that allows users to take pictures of objects with their phones, and I want to know what those objects are.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\nresult = detector(image[tf.newaxis, ...])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP\'}, \'description\': \'This API allows you to detect objects in images using a pre-trained model from TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and has been trained on the COCO dataset.\'}', metadata={})]


 What is the similarity between two sentences? First encode them as vectors, and then calculate the cosine similarity.\n###Input: {\"sentence1\": \"The quick brown fox jumps over the lazy dog.\", \"sentence2\": \"A sly red vixen quickly leaps above the dozing hound.\"}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'list of sentences\', \'output\': \'list of embeddings\'}, \'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([\'sentence1\', \'sentence2\'])", \'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.\'}', metadata={})]


 My CEO sent me an email, and he is asking me to analyze the emails' tone to see whether it is positive or negative. We need to get an embedding for this email before classifying the sentiment.\n
 Use this API documentation for reference: [Document(page_content='{\'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\': [\'text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "embeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.768\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 We need recommendations for similar restaurants. Calculate the embedding of restaurant descriptions to create a list of the most similar establishments.\n
 Use this API documentation for reference: [Document(page_content='{\'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\': "embeddings = embed([\'Hello world\', \'Embedding with TensorFlow Hub\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803\'}, \'description\': \'A pre-trained model that converts text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 Convert a book summary into a high-dimensional vector for determining similarity to another book's summary.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text: A string or a list of strings to be embedded\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs.\'}', metadata={})]


 Tell me how to convert post titles into numerical vectors so that I can find similar titles later on.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text: A string or a list of strings to be embedded\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs.\'}', metadata={})]


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


 I took a picture of my park today. I want to know what kinds of objects I captured in the picture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 Our application needs to identify objects in an image. Extract them using pre-trained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/d0/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/d0/1\'\\ndetector = hub.load(model_id)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'N/A\'}, \'description\': \'Load a pre-trained model from TensorFlow Hub for object detection in images.\'}', metadata={})]


 Compare the similarity of two customer reviews for a product and see if they are positive or negative.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'import tensorflow_hub as hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1\'\\nloaded_model = hub.load(model_id)", \'performance\': {\'dataset\': \'gnews-swivel-20dim\', \'accuracy\': \'N/A\'}, \'description\': \'Load a text classification model from TensorFlow Hub using a model_id.\'}', metadata={})]


 I took a photo of my backyard and want to find similar images online. Please extract features from the photo so I can search for similar photos.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/1\'\\nmodel = hub.load(model_id)\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(299, 299))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.expand_dims(image, axis=0)\\nfeatures = model(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'This API allows you to extract image features using a pre-trained model from TensorFlow Hub. The model is trained on the ImageNet dataset and can be used to generate feature vectors for various image classification tasks.\'}', metadata={})]


 I want to compare the meaning similarity of two sentences. Find a way to encode the sentences and calculate their similarity.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Universal Sentence Encoder\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'A list of sentences or phrases\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.80\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 My retail client wishes to recognize when a doorbell is rang in audio files of their store.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio event classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Classify audio events\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A TensorFlow Hub model for classifying audio events.\'}', metadata={})]


 For a building project, we need to analyze pictures of different rocks and determine their structures. Obtain the feature vector of a rock image first.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract feature vector from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\', output_shape=[1280])", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\', output_shape=[1280]),\\n    tf.keras.layers.Dense(1)\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\\nimage = tf.expand_dims(image, 0)\\n\\nfeature_vector = model.predict(image)\\nprint(feature_vector)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API allows you to extract a 1280-dimensional feature vector from an image using the MobileNetV2 architecture, which is pre-trained on the ImageNet dataset. The feature vector can be used for various tasks, such as image classification, object detection, and transfer learning.\'}', metadata={})]


 I need to know what is in this photo I took. Can you tell me?\n###Input: {\"image\": \"path/to/image.jpg\"}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 Identify objects in an image to let me know what objects are in the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 My friend sent me an image of an animal, and I am not sure what kind of animal it is. Can you tell me what animal is in the image?\n###Input: \"<path_to_image>\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 For an image classification task, we need a model capable of recognizing and differentiating between various object types. Please load a pre-trained model for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\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.\'}', metadata={})]


 In order to analyze customer feedback from my online store, I need a way to convert their feedback into a numeric format that can be processed by my algorithms.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 I'm working on an app for smart parking stations, and I would like to detect and recognize the car plates from our CCTV footages.\n
 Use this API documentation for reference: [Document(page_content='{\'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\\n\\ndetector = hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1\')\\n\\n# Preprocess image\\ninput_image = tf.image.resize(image, (640, 640))\\ninput_image = tf.expand_dims(input_image, axis=0)\\n\\n# Run detector\\noutput = detector(input_image)\\n\\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.\'}', metadata={})]


 We need to build a car and bike image classifier. First, we need to extract features from the images to use for training the model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'image_input\'], \'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/feature_vector/4\')\\nimage_input = tf.zeros([1, 224, 224, 3])\\nfeatures = model(image_input)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture, trained on ImageNet dataset, which can be used to extract features from images for various tasks like image classification or object detection.\'}', metadata={})]


 I want to compare the semantic similarity between two sentences. Give me the embeddings using pre-trained models.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text using pre-trained models\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'List of sentences\'}, \'python_environment_requirements\': [\'pip install tensorflow\', \'pip install tensorflow-hub\'], \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\', \'Embedding with TensorFlow Hub is easy!\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'N/A\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. It is pre-trained on a variety of data sources and can be used with TensorFlow Hub.\'}', metadata={})]


 Sniff out the odd one among the following sentences: \"The party was amazing.\", \"Call a doctor.\", \"The conference was well organized.\"\n###Input: ['The party was amazing.', 'Call a doctor.', 'The conference was well organized.']\n
 Use this API documentation for reference: [Document(page_content='{\'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\': \'text_input\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow_hub as hub; embed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\'); embeddings = embed([\'Hello world!\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'0.78\'}, \'description\': \'This module uses the Universal Sentence Encoder model to preprocess text by converting it into embeddings.\'}', metadata={})]


 We want to recommend similar fashion items based on a user's uploaded image. Extract image features and compare them with our fashion database.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 When creating a mobile application that uses image recognition, it should be able to determine what items are in the photo.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow>=2.0\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\n\\nresult = detector(image_tensor)\\nprint(result)", \'performance\': {\'dataset\': \'Open Images v4\', \'accuracy\': \'0.545 mAP\'}, \'description\': \'A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates.\'}', metadata={})]


 I have some images and I want to compare them efficiently. Extract the features of the images so I can perform similarity analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'image_input\'], \'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/feature_vector/4\')\\nimage_input = tf.zeros([1, 224, 224, 3])\\nfeatures = model(image_input)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture, trained on ImageNet dataset, which can be used to extract features from images for various tasks like image classification or object detection.\'}', metadata={})]


 Manage the camera in the smartphone to recognize multiple objects during a forest walk.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'ssd_mobilenet_v2_fpnlite_640x640\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2_fpnlite_640x640/1\')", \'api_arguments\': {\'input_tensor\': \'4D image tensor\', \'output_tensor\': \'list of tensors representing bounding boxes, classes, scores, and number of detections\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ndetector = hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2_fpnlite_640x640/1\')\\nimage_tensor = tf.convert_to_tensor(image, dtype=tf.uint8)\\ninput_tensor = tf.expand_dims(image_tensor, axis=0)\\noutput_dict = detector(input_tensor)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP of 0.32\'}, \'description\': \'A pre-trained object detection model that detects objects in images using the Single Shot MultiBox Detector (SSD) architecture with MobileNet V2 FPN-Lite feature extractor.\'}', metadata={})]


 A tourist application wants to give real-time object recognition to help tourists when they take a picture of an object in the city.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/d0/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/d0/1\'\\ndetector = hub.load(model_id)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'N/A\'}, \'description\': \'Load a pre-trained model from TensorFlow Hub for object detection in images.\'}', metadata={})]


 We want to help users identify the species of a bird in a given picture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 I want to analyze a list of customer reviews to gain insight into their sentiment.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


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


 Use a pre-trained model to find out traits that are similar to buildings in images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 224, 224, 3])\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Varies depending on the model\'}, \'description\': \'A collection of pre-trained image classification models available on TensorFlow Hub, which can be used to classify images into various categories.\'}', metadata={})]


 I need to measure the semantic similarity between pairs of sentences. Help me to get the embeddings of the sentences.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'list of sentences\', \'output\': \'list of embeddings\'}, \'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([\'sentence1\', \'sentence2\'])", \'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.\'}', metadata={})]


 I work in a post office, and need to classify bills and documents from a mixed package. Please extract image features so that we can use these features for classification.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 We are building an AI app that can help us determine what kind of food is in a picture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 Categorize images captured from a drone in different landscapes like forests, rivers, and mountains.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 I am planning to sort and label my photo collection. Help me identify different objects in my master album.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/d0/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/d0/1\'\\ndetector = hub.load(model_id)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'N/A\'}, \'description\': \'Load a pre-trained model from TensorFlow Hub for object detection in images.\'}', metadata={})]


 Determine the dominant object in an image by extracting features from the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'image_input\'], \'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/feature_vector/4\')\\nimage_input = tf.zeros([1, 224, 224, 3])\\nfeatures = model(image_input)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture, trained on ImageNet dataset, which can be used to extract features from images for various tasks like image classification or object detection.\'}', metadata={})]


 Estimate the similarity between two news headlines using pre-trained text embedding.\n
 Use this API documentation for reference: [Document(page_content='{\'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\': "embeddings = embed([\'Hello world\', \'Embedding with TensorFlow Hub\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803\'}, \'description\': \'A pre-trained model that converts text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 I want to build a simple mobile app that identifies plants by analyzing photos of their leaves.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 I want to make a food identification app that can help me classify different dishes at a restaurant.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_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_130_224/classification/4\'\\nmodel = hub.load(model_id)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'N/A\'}, \'description\': \'A pre-trained image classification model available on TensorFlow Hub for classifying images into one of 1000 categories.\'}', metadata={})]


 I want to know what is in this image I just took.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 We need to classify images taken by our surveillance camera to detect unwanted activity.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 We are working with an autonomous vehicle company, and they want to detect and classify objects in the environment while the vehicle is on the road.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/efficientdet/d0/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/efficientdet/d0/1\'\\ndetector = hub.load(model_id)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'N/A\'}, \'description\': \'Load a pre-trained model from TensorFlow Hub for object detection in images.\'}', metadata={})]


 How do I know the breed of a random dog in a picture?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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_path = \'path/to/image.jpg\'\\nimage = tf.image.decode_jpeg(tf.io.read_file(image_path))\\nresult = detector(image[tf.newaxis, ...])", \'performance\': {\'dataset\': \'OpenImages V4\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained TensorFlow Hub model for detecting objects in images using Faster R-CNN with Inception-ResNet-V2\'}', metadata={})]


 I have a picture of my room and I want to identify all the objects present in it.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 I have a collection of movie descriptions and I want to find the resemblance between them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Video classification model\', \'api_name\': \'Video_classification_model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/Video_classification_model_id/1\')", \'api_arguments\': [], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Kinetics-400\', \'accuracy\': \'\'}, \'description\': \'A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content.\'}', metadata={})]


 To find similar images in a database, we need to extract useful features to compare images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 Which API could be used to compare the semantic similarity between two given pieces of text?\n
 Use this API documentation for reference: [Document(page_content='{\'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\': [\'text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "embeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.768\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 We have some cars pictures and want to classify them, give us the API call to get a pre-trained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 224, 224, 3])\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Varies depending on the model\'}, \'description\': \'A collection of pre-trained image classification models available on TensorFlow Hub, which can be used to classify images into various categories.\'}', metadata={})]


 We have received customer complaints about our products and want to group them into different categories to address them more effectively. Help us get embeddings for the complaint text data to enable clustering.\n
 Use this API documentation for reference: [Document(page_content='{\'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.\'}', metadata={})]


 Help me convert Reddit post comments into numerical vector format that can be easily understood by machine algorithms.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'N/A\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 Organize a digital library of photos and identify the ones with similar content by comparing their image features.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 Given a collection of images, find the most likely object depicted in each image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow>=2.0\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\n\\nresult = detector(image_tensor)\\nprint(result)", \'performance\': {\'dataset\': \'Open Images v4\', \'accuracy\': \'0.545 mAP\'}, \'description\': \'A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates.\'}', metadata={})]


 I am making an image recognition app, and I want to extract features from each image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract features from images\', \'api_name\': \'imagenet_mobilenet_v2_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\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=[224, 224, 3],\\n                   output_shape=[1280])\\n])\\n\\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, axis=0)\\n\\nfeatures = model.predict(image_batch)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture. It can be used to extract features from images, which can be used for various machine learning tasks like image classification, object detection, and more.\'}', metadata={})]


 Analyze whether the descriptions of two movies are similar by generating dense vector representations of the text and comparing them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text to dense vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS Benchmark\', \'accuracy\': \'0.803\'}, \'description\': \'A text embedding model that converts input text into dense vectors of fixed size using TensorFlow Hub\'}', metadata={})]


 We have a set of images that we want to classify into different categories. We need a pre-trained image classification model for this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading a pre-trained model for image classification\', \'api_name\': \'imagenet_mobilenet_v2_130_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not provided\'}, \'description\': \'Load a pre-trained MobileNetV2 model for image classification using TensorFlow Hub.\'}', metadata={})]


 Our team has an image classification project. First, we need to extract features from our images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 I want to cluster customer chat messages so that their related concerns are addressed in a more meaningful way.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Speech-to-Text\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Speech Recognition\', \'api_name\': \'wav2vec2_large_960h\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/vasudevgupta7/wav2vec2/1\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': \'\', \'performance\': {\'dataset\': \'LibriSpeech\', \'accuracy\': \'0.960\'}, \'description\': \'A pre-trained model for converting spoken language in audio files into written text using TensorFlow Hub.\'}', metadata={})]


 I want to build a text classifier for articles that could also find articles with similar content. Can you suggest a way to encode the text of the articles?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'N/A\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 There is a need to segment images of a park area with trees and people, and distinguish the trees from people.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


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


 Can you recommend an API that helps to convert a research article abstract into a high-dimensional vector which we can then use to find similar articles?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'N/A\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 I need to monitor a warehouse's inventory and identify objects kept in the storage area.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in an image\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector([image])", \'performance\': {\'dataset\': \'OpenImages_v4\', \'accuracy\': \'N/A\'}, \'description\': \'This API allows you to detect objects in an image using TensorFlow Hub and a pre-trained model.\'}', metadata={})]


 Create a feature extraction model to recognize 10 different types of images from a given dataset with sizes 224x224x3.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet_mobilenet_v3_small_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/feature_vector/5\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1001,)\'}, \'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_v3_small_100_224/feature_vector/5\',\\n                   input_shape=(224,224,3))\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'60.3%\'}, \'description\': \'A compact, pre-trained image feature vector model, using MobileNetV3 architecture, trained on ImageNet dataset.\'}', metadata={})]


 I need a quick way to classify images on my phone. Can you use a computationally efficient model to do this?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification model\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'image_url\': \'URL of the image to be classified\', \'image_size\': \'224x224 pixels\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\ndef classify_image(image_url):\\n  image = tf.keras.utils.get_file(\'image.jpg\', image_url)\\n  image = tf.keras.preprocessing.image.load_img(image, target_size=(224, 224))\\n  image = tf.keras.preprocessing.image.img_to_array(image)\\n  image = tf.expand_dims(image, 0)\\n  return model(image)\\n\\nresult = classify_image(\'https://example.com/image.jpg\')", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API provides a pre-trained image classification model based on MobileNetV2 architecture, which can classify images into 1000 different categories. The model is trained on ImageNet dataset and can be used for various image classification tasks.\'}', metadata={})]


 Provide me with the facility to determine the similarity between two sentences using encoded text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'N/A\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 We want to recognize different types of dogs in images for an upcoming app. Help us with a model to get the image features.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 I am planning my garden, please help me segment plants in an image for better landscape planning.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image segmentation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image segmentation model\', \'api_name\': \'deeplabv3_257_mv_gpu\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')", \'api_arguments\': {\'input\': \'Image tensor\', \'output\': \'Segmentation mask tensor\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')\\nimage_tensor = tf.image.resize(image, (257, 257))\\nsegmentation_mask = model(image_tensor)", \'performance\': {\'dataset\': \'PASCAL VOC 2012\', \'accuracy\': \'0.899\'}, \'description\': \'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\'}', metadata={})]


 Find out the type of birds in my backyard based on smartphone photos I took.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 I'm working on a project that requires text clustering, and I need to convert a list of product reviews into vector representations in order to analyze the semantic similarities between them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text: A string or a list of strings to be embedded\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs.\'}', metadata={})]