 I am trying to create clusters of similar customer feedback for a product.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Audio feature extraction\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/model_id/1\')", \'api_arguments\': \'None\', \'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/model_id/1\')\\nembeddings = model(samples, sample_rate)", \'performance\': {\'dataset\': \'Unknown\', \'accuracy\': \'Not provided\'}, \'description\': \'This API allows you to extract audio features from audio samples using a pre-trained model from TensorFlow Hub.\'}', metadata={})]


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


 We are working on an app that can identify plants. We have the images of the leaves, so all we need is a pre-trained model to classify the images.\n
 Use this API documentation for reference: [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={})]


 Analyze the sentiments of these customer reviews before processing their feedback.\n###Input: [\"I loved the service and the food!\", \"The staff was rude, and I had a terrible experience.\", \"Not too bad, could be better.\"]\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Text preprocessing\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'text_input\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': "import tensorflow_hub as hub; embed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\'); embeddings = embed([\'Hello world!\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'0.78\'}, \'description\': \'This module uses the Universal Sentence Encoder model to preprocess text by converting it into embeddings.\'}', metadata={})]


 We want to obtain the location of a boat in an image. Detect objects in the given image and provide the location of the boat if found.\n
 Use this API documentation for reference: [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={})]


 Create a system to find the similarity between two sentences.\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'm building a recommendation system for similar pictures. Provide me with a vector representation of a picture to measure image similarity.\n
 Use this API documentation for reference: [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={})]


 Recommend to me some suitable products based on the textual similarity of their descriptions from a database.\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={})]


 Extract audio features to classify sounds in the environment.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio event classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Classify audio events\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A TensorFlow Hub model for classifying audio events.\'}', metadata={})]


 Write an AI-powered program to summarize movie plots while retaining maximum details from the given plot.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Video classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A video classification model using TensorFlow Hub.\'}', metadata={})]


 Help me compare similarities between user reviews about two different products.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Video classification model\', \'api_name\': \'Video_classification_model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/Video_classification_model_id/1\')", \'api_arguments\': [], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Kinetics-400\', \'accuracy\': \'\'}, \'description\': \'A pre-trained video classification model available on TensorFlow Hub for classifying videos based on their content.\'}', metadata={})]


 Provided with images of paintings, I want to determine which art era they belong to.\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={})]


 Understand the relationship between two movie synopses and determine whether they seem similar or not.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Video classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A video classification model using TensorFlow Hub.\'}', metadata={})]


 Suggest personalized ads to users by creating a semantic similarity model that recommends advertisements based on textual similarity to user preferences stored in the description field. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Text embedding model\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "embeddings = embed([\'Hello world\', \'Embedding with TensorFlow Hub\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'High\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 There is a list of customer reviews and your task is to calculate the semantic similarity between each pair of reviews.\n
 Use this API documentation for reference: [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={})]


 Expose a method to compute the semantic similarity of pairs of sentences by comparing the high-dimensional vectors.\n
 Use this API documentation for reference: [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 am analyzing tweets, and I want to know the sentiment of each tweet. Please help me convert tweets into meaningful representations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Speech-to-Text\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Speech Recognition\', \'api_name\': \'wav2vec2_large_960h\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/vasudevgupta7/wav2vec2/1\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': \'\', \'performance\': {\'dataset\': \'LibriSpeech\', \'accuracy\': \'0.960\'}, \'description\': \'A pre-trained model for converting spoken language in audio files into written text using TensorFlow Hub.\'}', metadata={})]


 I need to build a machine learning model that can automatically detect and classify different food items from images.\n
 Use this API documentation for reference: [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={})]


 Identify the animal in the photo I have taken.\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={})]


 Generate an appropriate label for instance of image.\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'm traveling to a new city and I want to use my phone to identify different objects in my surroundings.\n
 Use this API documentation for reference: [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={})]


 Analyze a list of movie reviews and group them based on semantic similarity by first converting the reviews into embeddings.\n
 Use this API documentation for reference: [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 am working on a car accident detection system. Show me how to detect object and classify them in images\n
 Use this API documentation for reference: [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={})]


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


 We are creating a visual recommender system based on images. We need to extract features from them.\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={})]


 In order to analyze the sentiment of tweets on Twitter, I need the Universal Sentence Encoder model to convert tweets into embeddings for analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Text tokenization and vectorization\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'N/A\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. It is a pre-trained model available on TensorFlow Hub.\'}', metadata={})]


 Detect and count the number of different objects in a picture I took at a beach.\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={})]


 I am visiting different places and I want to know which landmark I am visiting.\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={})]


 Identify different types of birds when hikers send the images to our system.\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={})]


 How can I compare the similarity between multiple sentences?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text data\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'List of sentences\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "import tensorflow_hub as hub; embed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\'); embeddings = embed([\'Hello world\', \'Embedding with TensorFlow Hub\'])", \'performance\': {\'dataset\': \'STS Benchmark\', \'accuracy\': \'0.803\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs.\'}', metadata={})]


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


 Determine the top 3 closest category matches for a given image using the Inception V3 pre-trained model.\n
 Use this API documentation for reference: [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={})]


 I need a simple way to encode text into vectors for my clustering algorithm.\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={})]


 Find a tool to extract features from images and create a model to identify different types of plants.\n
 Use this API documentation for reference: [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 do I create image-feature-vector for the last travel destination photo album to organize similar pictures together?\n
 Use this API documentation for reference: [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={})]


 Identify the make and model of a car using an image posted on an online car marketplace.\n###Input: \"https://url-to-image-of-car-from-marketplace\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading a pre-trained image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'model_id\': \'URL of the TensorFlow Hub model\'}, \'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)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Depends on the specific model\'}, \'description\': \'This API allows you to load a pre-trained image classification model from TensorFlow Hub for use in your applications.\'}', metadata={})]


 Assist me in identifying animal species present in a collection of images for an ecological study.\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 am building a tool to measure the similarity between different product descriptions. To do this, I need to first get embeddings for each description.\n
 Use this API documentation for reference: [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={})]


 Show me how to classify an image of a car into one of the 1000 categories.\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={})]


 We need to create an image classifier for recognizing dog breeds. Before creating the classifier, we should extract a fixed-length feature vector from each image.\n
 Use this API documentation for reference: [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 create a machine learning model to identify whether a solar panel is damaged or not using images.\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={})]


 Suppose you want to build a search engine that can find the most visually similar products in your online shop. You need to extract feature vectors from product images.\n
 Use this API documentation for reference: [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 am a teacher and want to classify images related to educational material. Find out the category of educational images by providing the image's URL.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification model\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'image_url\': \'URL of the image to be classified\', \'image_size\': \'224x224 pixels\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\ndef classify_image(image_url):\\n  image = tf.keras.utils.get_file(\'image.jpg\', image_url)\\n  image = tf.keras.preprocessing.image.load_img(image, target_size=(224, 224))\\n  image = tf.keras.preprocessing.image.img_to_array(image)\\n  image = tf.expand_dims(image, 0)\\n  return model(image)\\n\\nresult = classify_image(\'https://example.com/image.jpg\')", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API provides a pre-trained image classification model based on MobileNetV2 architecture, which can classify images into 1000 different categories. The model is trained on ImageNet dataset and can be used for various image classification tasks.\'}', metadata={})]


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


 An airport authority wants to detect the presence of drones close to the airport runway.\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 have a lot of images and I want to categorize them based on the objects present in the images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 I'm planning to do sentiment analysis on a bunch of news articles. Help me convert the article content to 20-dimensional vectors.\n
 Use this API documentation for reference: [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={})]


 In order to perform image classification using a machine learning model, I need to generate feature vectors for the given images.\n
 Use this API documentation for reference: [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={})]


 Generate embeddings for movie descriptions to create a recommendation system based on semantic similarity.\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={})]


 Match products descriptions to our customer search query in our e-commerce store. First, encode them into vectors.\n
 Use this API documentation for reference: [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={})]


 For a fashion e-commerce website, I want to build a recommendation system on clothing items based on their similarity. How do I extract feature vectors from clothing images for comparison?\n
 Use this API documentation for reference: [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={})]


 For my language model, I require preprocessed text. Use an appropriate method to assist me in preparing the input text for further processing.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'import tensorflow_hub as hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/google/universal-sentence-encoder/4\'\\nmodel = hub.load(model_id)", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': "Load a pre-trained text preprocessing model from TensorFlow Hub using the model\'s unique identifier"}', metadata={})]


 We are trying to cluster movie reviews from the Internet. First, we want to transform the reviews into fixed dimension 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\'], \'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={})]


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


 I want to recommend restaurants based on their reviews. Cluster similar reviews together.\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 to identify different handbags from pictures by extracting features.\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={})]


 Can you help me create a new image classification model by using a pre-trained model to extract image features and then build my custom classifier on top?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'imagenet_mobilenet_v2_130_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')\\n\\n# Prepare an image for input to the model\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\n\\n# Predict the class of the image\\npredictions = model(input_image)\\npredicted_class_index = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained model for image classification using MobileNetV2 architecture and trained on ImageNet dataset\'}', metadata={})]


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


 I need a way to identify celebrities in pictures I take on my phone. Can you use image classification to do this?\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 primary sound category in an audio clip.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio event classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Classify audio events\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A TensorFlow Hub model for classifying audio events.\'}', metadata={})]


 Create a model for bird species identification that extracts features considering memory constraints when deploying on an IoT device.\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={})]


 Analyze the similarity between movie reviews, obtain the embeddings for a given list of sentences so we can compare them.\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={})]


 Develop a mobile app to differentitate various car models from their photographs.\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={})]


 In order to recommend news articles to users based on their interests, I need to identify similar articles to their preferences. Can you help me obtain the text embeddings for comparison?\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={})]


 Create a program that can load an image from a web store and recognize the type of product it is.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\'\\nmodel = hub.load(model_id)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\'}', metadata={})]


 Identify a suitable vector representation for the given sentence to be used later for semantic textual similarity tasks.\n###Input: \"I am at the airport waiting for my flight.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Text tokenization and vectorization\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'N/A\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. It is a pre-trained model available on TensorFlow Hub.\'}', metadata={})]


 I need to match new customers' feedbacks to the closest existing feedbacks to avoid duplicating work. Help me obtain an embedding for each customer feedback.\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={})]


 We have images of cars, and we want to extract their features and then create a classifier to classify them based on their make and 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={})]


 I am trying to build a mobile app to identify different food items. Can you suggest an effective pre-trained model for that purpose?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\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={})]


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


 We are organizing an event in a park and we want to monitor the crowd by detecting the number of people in the area.\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={})]


 Analyze a video for content and classify it into an appropriate category.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Video classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Video classification\', \'api_name\': \'Video_Classification_Model\', \'api_call\': "hub.load(\'https://tfhub.dev/google/Video_Classification_Model/1\')", \'api_arguments\': [\'video_url\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/google/Video_Classification_Model/1\')\\nresult = model(video_url)", \'performance\': {\'dataset\': \'Kinetics-700\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained video classification model available on TensorFlow Hub, capable of classifying videos into one of 700 action classes.\'}', metadata={})]


 Estimate semantic similarity of two movie descriptions for a recommendation system.\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 want to predict breed of dogs among 10 different breeds using images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.KerasLayer(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\nprint(predictions)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'This API allows users to load pre-trained image classification models from TensorFlow Hub and make predictions on images.\'}', metadata={})]


 Imagine you are developing an app integrated with augmented reality that develops a customized filter based on what the user points their camera to. You need to extract features from images to create this filter.\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={})]


 Analyze an image of a cat and extract useful information that can help classify it with other animals.\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={})]


 Analyze customer support email messages and get an idea about their satisfaction by generating embeddings for each email message.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Text embedding generation\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/google/universal-sentence-encoder/4\'\\nembed = hub.load(model_id)\\ntext = [\'This is an example sentence.\']\\nembeddings = embed(text).numpy()", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.8\'}, \'description\': \'This API generates text embeddings using TensorFlow Hub, which can be used for various natural language processing tasks such as semantic similarity, clustering, and classification.\'}', metadata={})]


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


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


 Create a system to verify if different product images in our ecommerce store belong to the same category.\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 an image of a cake and want to know if the model can correctly identify it.\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={})]


 I need to automatically organize a collection of product descriptions by similarity. Can you please generate embeddings for these 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\': [\'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 need to implement a classifier using MobileNetV2 to label the content of an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification using a pre-trained model\', \'api_name\': \'mobilenet_v2\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\')", \'api_arguments\': {\'model_url\': \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', \'input_shape\': \'IMAGE_SHAPE+(3,)\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nIMAGE_SHAPE = (224, 224)\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\', input_shape=IMAGE_SHAPE+(3,))\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\', target_size=IMAGE_SHAPE)\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\nresult = model.predict(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'MobileNetV2 is a pre-trained image classification model that can classify images into 1000 different categories. It is lightweight and optimized for mobile devices.\'}', metadata={})]


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


 I have a set of images from a recent trip, and I'd like to automatically classify the most prominent subject in each photo.\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={})]


 A sports broadcaster wants to automatically detect different players on the field during a soccer match.\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={})]


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


 I am comparing sneakers in a catalog, and I want to extract the image features from the sneakers' images to cluster them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extracting feature vectors from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[batch_size, height, width, 3]\', \'output_shape\': \'[batch_size, 1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')\\n\\nimage = tf.zeros([1, 224, 224, 3])\\nfeatures = model(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This model takes an input image and outputs a 1280-dimensional feature vector. It is based on the MobileNetV2 architecture and pretrained on the ImageNet dataset.\'}', metadata={})]


 I have an application that recognizes objects in images, and I need a pre-trained model to perform the classification task.\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={})]


 Recommend me an API that could classify thousands of emails into categories: office, financial, social, commercial, travel.\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={})]


 Let's build an image classifier for recognizing dog breeds using the pre-trained MobileNet V2 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\': \'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={})]


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