 I found a bird on my trip outside, and I took a photo. Can you identify 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={})]


 Figure out the category of my image by using a pre-trained Inception model.\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\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(299, 299))\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\npredictions = model(input_image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'A pre-trained model for image classification using Inception V3 architecture, available on TensorFlow Hub. The model has been trained on the ImageNet dataset and can be used to classify images into 1000 different classes.\'}', metadata={})]


 Images have come from users, and we need to recognize their categories for better curation.\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 several support tickets in our system, find a way to cluster them to find the most occurring topics.\n
 Use this API documentation for reference: [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={})]


 We need to create an image classifier using transfer learning. I want to get feature vectors from pre-trained models.\n
 Use this API documentation for reference: [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 to create fixed-sized vector representation for every input phrases in a dataframe?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', trainable=False)", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', trainable=False),\\n    tf.keras.layers.Dense(1000, activation=\'softmax\')\\n])\\n\\nmodel.build([None, 299, 299, 3])\\nmodel.summary()", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'This API provides a compact representation of images using the Inception V3 model from TensorFlow Hub. It extracts feature vectors from images, which can be used for various machine learning tasks such as classification and clustering.\'}', metadata={})]


 I need an API to help me classify pet images. I'd like to use the extracted features from these images to make better predictions.\n
 Use this API documentation for reference: [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={})]


 Help me with identifying the object present in my new picture, using a ready-to-use model.\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 want to analyze images of my coin collection and build a classifier to identify coins. Provide me with a method to extract image features.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/4\')", \'api_arguments\': [\'image\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/4\',\\n                   input_shape=(299, 299, 3))\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(299, 299))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.expand_dims(image, 0)\\n\\nfeatures = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'78.1%\'}, \'description\': \'This API allows you to extract feature vectors from images using the Inception V3 model pretrained on the ImageNet dataset.\'}', metadata={})]


 We are doing data pre-processing for a project and our aim is to understand if the given two product titles are semantically similar or not. We first need to convert these product titles into high-dimensional vectors.\n
 Use this API documentation for reference: [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={})]


 I'm working on a plant recognition app, and I want to classify a plant's image taken by users.\n
 Use this API documentation for reference: [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={})]


 How can I calculate the similarity between two sentences based on their embeddings?\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 have an image of my backyard. Identify if the BBQ grill is active.\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 am building a phone app that recognizes food. We need a lightweight model to identify different types of food.\n
 Use this API documentation for reference: [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={})]


 We are interesting in analyzing text messages to recommend interesting articles to our users. For this, we need to create an embedding for each message.\n
 Use this API documentation for reference: [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={})]


 Could you help me to find a quick way to rate similarities between my blog posts?\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 have a lot of vacation pictures and want to find those with similar content. Can you help me?\n
 Use this API documentation for reference: [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 doing holiday gift shopping online, and I want to know what category a picture of a gift item belongs to.\n
 Use this API documentation for reference: [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={})]


 Can you find an efficient image classification model to use on my smartphone for recognizing food items in my fridge?\n
 Use this API documentation for reference: [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={})]


 Our art gallery is conducting an exhibition and we want to classify the images of paintings into different genres.\n
 Use this API documentation for reference: [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={})]


 We are setting up an AI system to help classify images from outdoor events into different categories based on their content.\n
 Use this API documentation for reference: [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={})]


 I am creating a shopping app and I want to suggest similar looking items from our database using item's image.\n
 Use this API documentation for reference: [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 image and I want to recognize the object in this image, so I need to access a pre-trained image classification model.\n
 Use this API documentation for reference: [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/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow-hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(299, 299))\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\n\\npredictions = model(input_image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'This API provides pre-trained image classification models using TensorFlow Hub for various architectures like Inception V3, MobileNet, and others. These models are trained on the ImageNet dataset and can be used for transfer learning or fine-tuning on custom datasets.\'}', metadata={})]


 I have taken some pictures of my dog, and I want to organize them in a way that those which are similar to each other are together.\n
 Use this API documentation for reference: [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={})]


 We want to find the most similar questions from user inputs to give them the best answer.\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 am working on a product recommendation system for a flower shop. Extract feature vectors from flower images to be used in the system.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', trainable=False)", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', trainable=False),\\n    tf.keras.layers.Dense(1000, activation=\'softmax\')\\n])\\n\\nmodel.build([None, 299, 299, 3])\\nmodel.summary()", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'This API provides a compact representation of images using the Inception V3 model from TensorFlow Hub. It extracts feature vectors from images, which can be used for various machine learning tasks such as classification and clustering.\'}', metadata={})]


 Recommend a playlist to me based on the audio characteristics of this song clip I'm sending you.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from audio\', \'api_name\': \'yamnet\', \'api_call\': "hub.load(\'https://tfhub.dev/google/yamnet/1\')", \'api_arguments\': [], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'YAMNet is a deep net that predicts audio events from the AudioSet ontology.\'}', metadata={})]


 We are building a car recognition system. We need to extract features from car images to feed them into the classifier.\n
 Use this API documentation for reference: [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={})]


 Please help me with a system that studies product descriptions so that we can group products with similar features.\n
 Use this API documentation for reference: [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 need to find features in the images to perform image 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 need to find similar news articles from a large corpus. Please embed 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 strings\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\', \'This is a test sentence\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'Not specified\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. It is trained on a variety of data sources and a range of tasks.\'}', metadata={})]


 Discover all the elements present within a photograph of a street scene.\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 own a car dealer business and want to develop an app that categorizes different car categories based on uploaded images.\n
 Use this API documentation for reference: [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={})]


 Given images of painted landscapes, we want to find similar images based on their visual features. Which API can we use for extracting the feature vector?\n
 Use this API documentation for reference: [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={})]


 We need to know what is in these photos so we can report to our officials.\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\\n\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\nconverted_image = tf.image.convert_image_dtype(image, tf.float32)[tf.newaxis, ...]\\nresult = detector(converted_image)", \'performance\': {\'dataset\': \'OpenImages V4\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained image object detection model that detects objects in images using the Faster R-CNN architecture with Inception-ResNet V2 as the feature extractor.\'}', metadata={})]


 We have a collection of animal photos, and we want to obtain a feature vector from each photo in order to properly organize them.\n
 Use this API documentation for reference: [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 took a picture of a street and want to separate the vehicles, people, and buildings in the scene.\n
 Use this API documentation for reference: [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 need to quickly classify images of various objects on my phone. Which pre-trained model should I use for efficient processing in a mobile environment?\n
 Use this API documentation for reference: [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={})]


 I want to enhance my wildlife images and need to extract an image feature vector to use in advanced image processing tasks.\n
 Use this API documentation for reference: [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={})]


 My computer has folders with many images, and I want to classify these images into categories, such as animals, buildings, and so on.\n
 Use this API documentation for reference: [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={})]


 Show me how to classify an image of a dog using a pre-trained model with TensorFlow Hub and Inception V3 architecture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading\', \'api_name\': \'inception_v3\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\ndata_root = tf.keras.utils.get_file(\'flower_photos\',\'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\', untar=True)\\nimage_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255)\\nimage_data = image_generator.flow_from_directory(str(data_root), target_size=IMAGE_SHAPE)\\nfeature_extractor_url = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nfeature_extractor_layer = hub.KerasLayer(feature_extractor_url, input_shape=(299,299,3))\\nfeature_extractor_layer.trainable = False\\nmodel = tf.keras.Sequential([\\n  feature_extractor_layer,\\n  tf.keras.layers.Dense(image_data.num_classes, activation=\'softmax\')\\n])\\nmodel.compile(optimizer=tf.keras.optimizers.Adam(), loss=\'categorical_crossentropy\', metrics=[\'acc\'])\\nsteps_per_epoch = np.ceil(image_data.samples/image_data.batch_size)\\nhist = model.fit(image_data, epochs=6, steps_per_epoch=steps_per_epoch)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'Inception V3 model for image classification, pretrained on ImageNet dataset.\'}', metadata={})]


 Recommend a suitable movie based on a given movie's plot. For better recommendations, obtain the plot's embedding.\n
 Use this API documentation for reference: [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={})]


 Obtain image features from an image dataset to use in a product recommendation system\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 took a photo of a car and I want to extract its features in a vector form to use it for classification tasks later.\n
 Use this API documentation for reference: [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={})]


 Calculate the similarity between two sentences and determine if they have the same meaning.\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={})]


 We have an image dataset and want a pre-trained model as a feature extractor.\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={})]


 We are creating a custom image search engine that can give similar image results. Extract features from images.\n
 Use this API documentation for reference: [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={})]


 Can you compare two sentences similarly using the embedding generation?\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={})]


 Provide me with an encoded version of the following review, so I can later use it for sentiment analysis: \"What an amazing product! I've been using it for a few weeks now and couldn't be happier.\"\n###Input: \"What an amazing product! I've been using it for a few weeks now and couldn't be happier.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Transforms text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS Benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'description\': \'The Universal Sentence Encoder (USE) is a pre-trained model that encodes text into 512-dimensional vectors. It can be used for various natural language processing tasks such as text classification, semantic similarity, and clustering.\'}', metadata={})]


 I have several images of various objects; can you check if there are any boats in them?\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\', \'api_call\': "hub.load(\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\')", \'api_arguments\': \'image, score_threshold\', \'python_environment_requirements\': \'Python 3, TensorFlow 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\ndetector = hub.load(\'https://tfhub.dev/google/openimages_v4/ssd_mobilenet_v2/1\')\\nresult = detector(image, score_threshold=0.5)", \'performance\': {\'dataset\': \'Open Images v4\', \'accuracy\': \'mAP@0.5IOU = 32.8%\'}, \'description\': \'A pre-trained SSD MobileNet V2 model for object detection in images, trained on the Open Images v4 dataset. Detects objects and outputs their bounding boxes and class labels with associated confidence scores.\'}', metadata={})]


 We are building a software that can recognize similar objects in a series of images. We need feature vectors for each image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', trainable=False)", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/inception_v3/feature_vector/5\', trainable=False),\\n    tf.keras.layers.Dense(1000, activation=\'softmax\')\\n])\\n\\nmodel.build([None, 299, 299, 3])\\nmodel.summary()", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'0.779\'}, \'description\': \'This API provides a compact representation of images using the Inception V3 model from TensorFlow Hub. It extracts feature vectors from images, which can be used for various machine learning tasks such as classification and clustering.\'}', metadata={})]


 Can you help me obtain the embeddings of a list of sentences for a research on similarity between news headlines?\n
 Use this API documentation for reference: [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 have an image dataset and I want to compute feature vectors to perform similarity computation using nearest neighbor searching.\n
 Use this API documentation for reference: [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={})]


 A dating website wants to auto-match users based on the similarity of their profiles. How can we do that using text embeddings?\n
 Use this API documentation for reference: [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 are creating a mobile app where users can take photos of objects, and our app should identify what the object is.\n
 Use this API documentation for reference: [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={})]


 We are building a Tinder-clone app for pets adoption. When user swipes, we want to show them the most similar pets based on images. Therefore, in order to find similar pets, we need to get the feature vector of each pet photo.\n
 Use this API documentation for reference: [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 pictures on my phone and want to organize them into categories. Help me identify what each picture represents.\n
 Use this API documentation for reference: [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={})]


 Identify the category for these images to decide which category I should tag them on my blog.\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={})]


 Provide an API call that can detect any landmark in a given photo.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image pose detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect human poses in images\', \'api_name\': \'movenet\', \'api_call\': "hub.load(\'https://tfhub.dev/google/movenet/singlepose/lightning/4\')", \'api_arguments\': {\'input_image\': \'A single image or batch of images with shape [batch_size, height, width, 3]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.5.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\npose = hub.load(\'https://tfhub.dev/google/movenet/singlepose/lightning/4\')\\ninput_image = tf.zeros([1, 192, 192, 3])\\noutput = pose(input_image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'0.720\'}, \'description\': \'MoveNet is a model that detects human poses in images. It is trained on the COCO dataset and available on TensorFlow Hub.\'}', metadata={})]


 We have a car selling website and want to identify the model of the car based on the images uploaded by the seller.\n
 Use this API documentation for reference: [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'd like to look into a list of articles and rank them by their similarity to the provided statement. \n###Input: <noinput>\n
 Use this API documentation for reference: [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={})]


 Can you suggest how to extract features from animal images and classify them into their respective species?\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 an image-based recommendation system to identify visually similar products.\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 company wants to create a product recommender that sorts products based on visual similarity, help us identify the products in images.\n
 Use this API documentation for reference: [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 a list of short movie descriptions and want to get an embedding 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\': [\'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={})]


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


 Determine which politicians are similar to each other based on their quotes by obtaining an embedding for each quote.\n
 Use this API documentation for reference: [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={})]


 I have a list of movie reviews and I want to find the semantic similarity between them. How can I convert theses reviews into numerical data?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Convert text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS\', \'accuracy\': \'Pearson correlation coefficient of 0.8\'}, \'description\': \'A text embedding model that converts input text into high-dimensional vectors for use in natural language processing tasks\'}', metadata={})]


 There are some insects in the park, I want to recognize them using an app.\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/resnet50v1_fpn_512x512/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1\'\\ndetector = hub.load(model_id)\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nresult = detector(image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP (mean Average Precision)\'}, \'description\': \'Detect objects in images using TensorFlow Hub and a pre-trained CenterNet model with ResNet50V1 FPN backbone\'}', metadata={})]


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


 A retail store needs a tool that will be able to detect misplaced items on their shelves.\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={})]


 I need to build a pet breed classifier from images. I want to use a pre-trained model to extract feature vectors of a pet image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract feature vectors from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[None, 224, 224, 3]\', \'output_shape\': \'[None, 1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\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                   trainable=False)\\n])\\n\\nimage = tf.keras.Input(shape=(224, 224, 3))\\nfeatures = model(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'A pre-trained image feature vector model that uses MobileNetV2 architecture to extract feature vectors from images. It is trained on the ImageNet dataset and can be used for various computer vision tasks, such as image classification, object detection, and image similarity.\'}', metadata={})]


 We have set of images and want classify them into meaningful categories.\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={})]


 Show me how to get the semantic similarity between two sentences.\n###Input: \"I am going to the store to buy groceries.\", \"I will visit the market to purchase food items.\" \n
 Use this API documentation for reference: [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={})]


 I took a photo of my friends playing soccer. I am making a collage and we need to remove the background from the photo, segmenting the players in the image.\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={})]


 Investigate art works in a museum by creating a feature vector database for all paintings on display.\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={})]


 Once a day, I take a photo of my backyard to observe how the plants change over time. Detect what is present 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={})]


 I would like to be able to identify an animal in a photo I took while traveling.\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/resnet50v1_fpn_512x512/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1\'\\ndetector = hub.load(model_id)\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nresult = detector(image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP (mean Average Precision)\'}, \'description\': \'Detect objects in images using TensorFlow Hub and a pre-trained CenterNet model with ResNet50V1 FPN backbone\'}', metadata={})]


 In order to classify animals, I need to extract image features to train my own classification model.\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={})]


 What kind of furniture is in my room? I want to take a photo and recognize which type of furniture it is.\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={})]


 Create a mobile application that recognizes dogs and cats. I need features from photos of dogs and cats.\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={})]


 Could you calculate the similarity between the sentences \"I have a cat\" and \"My pet is a kitten\"?\n###Input: [\"I have a cat\", \"My pet is a kitten\"]\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.KerasLayer(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'Text or list of texts\', \'output\': \'Array of embeddings\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.KerasLayer(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\', \'Embedding is fun!\'])", \'performance\': {\'dataset\': \'STS Benchmark\', \'accuracy\': \'0.803\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 How can I get a descriptive vector from an image to use for a custom classification task?\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={})]


 Tell me which kind of animal is in this 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={})]


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


 I need a recommendation for similar items given a product description. First, get the embeddings of product descriptions.\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={})]


 Provide me with a method to convert sentences into numerical vectors for text analysis.\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={})]


 For our e-commerce product recommendation system, we need to find similar products based on a given 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={})]


 I have a dataset of user reviews written in English, and I'd like to prepare it for a sentiment analysis task. Could you help me preprocess the texts for this purpose?\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={})]


 Create a recommender system that recommends related products based on their descriptions.\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={})]


 Analyze a photo from a running marathon to identify the objects in the scene.\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/resnet50v1_fpn_512x512/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1\'\\ndetector = hub.load(model_id)\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nresult = detector(image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP (mean Average Precision)\'}, \'description\': \'Detect objects in images using TensorFlow Hub and a pre-trained CenterNet model with ResNet50V1 FPN backbone\'}', metadata={})]


 We need to analyze security camera footage and identify any intruding person or object in the frames.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\'\\ndetector = hub.load(model_id)\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'image.jpg\'))\\nresult = detector(image[np.newaxis, ...])", \'performance\': {\'dataset\': \'OpenImages V4\', \'accuracy\': \'Not specified\'}, \'description\': \'Detect objects in an image using TensorFlow Hub and the Faster R-CNN model with Inception ResNet V2\'}', metadata={})]


 Need a code snippet to compute similar texts among thousands of sentences based on a similarity score.\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={})]