 My niece wants to learn the names of different birds so we can play a game where she sees a bird picture and the app provides a guess about the bird's species.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 224, 224, 3])\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Varies depending on the model\'}, \'description\': \'A collection of pre-trained image classification models available on TensorFlow Hub, which can be used to classify images into various categories.\'}', metadata={})]


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


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


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


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


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


 I would like to use the features extracted from pictures of different outfits for clustering. \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 background noise of an audio file and provide the corresponding embeddings to help classify the type of environment it was recorded in.\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={})]


 We built a mobile app for people to identify dogs' breeds. Let's build a model to extract features from dog images and classify them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 We have a group of 10 people discussing different topics. Analyze their texts and find the most similar pair of text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "embeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.768\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 Can you help me classify the contents of an image I took during my vacation?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained model for image classification\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'URL of the pre-trained model\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture trained on ImageNet dataset.\'}', metadata={})]


 I have a list of headlines and would like to find which ones are semantically similar. Can you help me get the high-dimensional vectors for my headlines?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'list of sentences\', \'output\': \'list of embeddings\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'sentence1\', \'sentence2\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.\'}', metadata={})]


 Suggest a way to compute the similarity between two sentences using embeddings to help us identify which sentences are discussing the same topic.\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={})]


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


 My cousin has created an album of zoo animals he captured, and I want to classify the animals using a pre-trained TensorFlow model.\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\': \'mobilenet_v2_100_224\', \'api_call\': "tf.keras.Sequential([hub.KerasLayer(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\')])", \'api_arguments\': [\'model_url\', \'output_shape\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\\n        \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\',\\n        output_shape=[1001]\\n    )\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'Load a pre-trained MobileNetV2 model for image classification from TensorFlow Hub.\'}', metadata={})]


 We want to create a Q&A system, so we need to be able to find the similarity between the questions that website visitors have asked.\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 have a couple of videos in my library, can you suggest a model to classify them based on their content?\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={})]


 A political analyst is preparing a report and needs to identify different political ideologies present in the statements made by politicians. Can you provide the embeddings of those statements to create a clustering algorithm?\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={})]


 My company is developing a furniture recommendation system, and we need to extract features from furniture images to find similar items.\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 creating a search engine that checks for similar images for a given image. I want to extract the features from images for creating an image search algorithm.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'image_input\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')\\nimage_input = tf.zeros([1, 224, 224, 3])\\nfeatures = model(image_input)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture, trained on ImageNet dataset, which can be used to extract features from images for various tasks like image classification or object detection.\'}', metadata={})]


 I have a folder with a lot of images, and I want to cluster them by similarity but I do not know how to represent an image in a consistent way.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


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


 I have several photos of objects, and I want to get compact feature vectors for each.\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={})]


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


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


 I want to know how to get a compact representation of images using MobileNetV1.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet_mobilenet_v3_small_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/feature_vector/5\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1001,)\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/feature_vector/5\',\\n                   input_shape=(224,224,3))\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'60.3%\'}, \'description\': \'A compact, pre-trained image feature vector model, using MobileNetV3 architecture, trained on ImageNet dataset.\'}', metadata={})]


 Please help me find Latent Semantic Analysis for sentences, and it should convert text into meaningful 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={})]


 I am working on a project and need to classify images of objects, such as cars or animals, quickly and efficiently.\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={})]


 Analyze a photo of a car taken during a trip to identify the make and model of the car.\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={})]


 In a wildlife app, I want to recognize animals from users' images. Fetch the model that can help to classify those 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={})]


 Suggest me a method to find semantic similarity between different user queries.\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={})]


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


 I want to identify the contents of various images, can you recommend a pre-trained model for this 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)\\nresult = model(image_tensor)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Varies depending on the model\'}, \'description\': \'TensorFlow Hub provides a variety of pre-trained image classification models that can be easily loaded and used for various image classification tasks. The models are trained on the ImageNet dataset and their performance varies depending on the specific model chosen.\'}', metadata={})]


 When eating out or ordering takeout, I would like to know the type of food within 2 seconds by capturing the food 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={})]


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


 Tell me which car it is based on its picture.\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={})]


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


 Create a simple image classifier that can predict if an object is a cat or a dog.\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.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\': "import tensorflow_hub as hub\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n])\\n\\npredictions = model.predict(images)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture on ImageNet dataset\'}', metadata={})]


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


 We want to build a tool to recommend similar art pictures. Please extract features from the image provided.\n###Input: Example image of an art picture (with RGB values and size 224x224 pixels)\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract features from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'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\', input_shape=[224, 224, 3], output_shape=[1280])\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\nfeatures = model.predict(image_array)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained model that extracts 1280-dimensional feature vectors from 224x224 RGB images using MobileNetV2 architecture.\'}', metadata={})]


 What do I need to do if I want to use your image classification model to label categories of animal faces?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\'\\nmodel = hub.load(model_id)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'N/A\'}, \'description\': \'A pre-trained image classification model available on TensorFlow Hub for classifying images into one of 1000 categories.\'}', metadata={})]


 I want to calculate the semantic similarity between job descriptions so I can quickly identify related jobs. Generate embeddings for these texts.\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={})]


 Recognize and classify objects in an image using a pre-trained model from TensorFlow Hub.\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/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\')\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(image_array, 0)\\n\\nresult = detector(input_image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not specified\'}, \'description\': \'Detect objects in an image using a pre-trained model from TensorFlow Hub\'}', metadata={})]


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


 We have got a CCTV footage of a bird feeding station outside our house. Now we want to count different birds coming at different times during the day.\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={})]


 My daughter wants to identify butterflies in her butterfly book. I want to use my phone camera to identify them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\'\\ndetector = hub.load(model_id)\\nimage = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\nresult = detector(image[tf.newaxis, ...])", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP\'}, \'description\': \'This API allows you to detect objects in images using a pre-trained model from TensorFlow Hub. The model is based on the SSD MobileNet V2 architecture and has been trained on the COCO dataset.\'}', metadata={})]


 I need to analyze customer reviews for various products. Can you provide me with a representation of each review in a high-dimensional vector?\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={})]


 Suggest a way to compare two images based on their features vectors extracted from a pre-trained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Extract image features\', \'api_name\': \'image_feature_vector\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'image_input\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')\\nimage_input = tf.zeros([1, 224, 224, 3])\\nfeatures = model(image_input)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture, trained on ImageNet dataset, which can be used to extract features from images for various tasks like image classification or object detection.\'}', metadata={})]


 My clients are dog breeders and they requested a tool that can identify different breeds of dogs automatically.\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={})]


 Estimate the similarity between two given sentences by embedding them using Universal Sentence Encoder.\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\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'sentence1\', \'sentence2\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for various natural language processing tasks. It is pre-trained on a variety of data sources and can be fine-tuned for specific tasks.\'}', metadata={})]


 Suppose we have two movie reviews. We would like to know how semantically similar these two movie reviews are.\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={})]


 How can I compare the similarity between two sentences? I need a numerical representation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'Text or list of texts\', \'output\': \'Array of embeddings\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.KerasLayer(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\', \'Embedding is fun!\'])", \'performance\': {\'dataset\': \'STS Benchmark\', \'accuracy\': \'0.803\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 We need to find the semantic similarity between different product reviews.\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={})]


 Find the similarities between my favorite quotes from different books so I can group them together.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Speech-to-Text\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Speech Recognition\', \'api_name\': \'wav2vec2_large_960h\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/vasudevgupta7/wav2vec2/1\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'tensorflow, tensorflow_hub\', \'example_code\': \'\', \'performance\': {\'dataset\': \'LibriSpeech\', \'accuracy\': \'0.960\'}, \'description\': \'A pre-trained model for converting spoken language in audio files into written text using TensorFlow Hub.\'}', metadata={})]


 I want to provide a semantic search on my website, and I need to compare the similarity between the user query and my database of questions to find the top relevant matches.\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 need to find similarity between different reviews. First, could you create a vector representation for each review?\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={})]


 Create a customer support ticket classifier to automatically determine the issue category based on the ticket description.\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={})]


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


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


 I have a list of product reviews and I need a program to find the reviews which are most similar to each other.\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={})]


 After finishing a hike, I want to know which environment I have been to during the hike. My friends provided some pictures of the scenery during the hike.\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 want to construct an automatic tagging system that assigns meaningful tags to content pieces based on the text.\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={})]


 I want to develop an app to identify specific breeds of dogs from the user's photos of their pets. What kind of model should I use for this 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)\\nresult = model(image_tensor)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Varies depending on the model\'}, \'description\': \'TensorFlow Hub provides a variety of pre-trained image classification models that can be easily loaded and used for various image classification tasks. The models are trained on the ImageNet dataset and their performance varies depending on the specific model chosen.\'}', metadata={})]


 I have an app that needs to recognize objects in images. We need an image classification model to do that.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4\'\\nmodel = hub.load(model_id)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'N/A\'}, \'description\': \'A pre-trained image classification model available on TensorFlow Hub for classifying images into one of 1000 categories.\'}', metadata={})]


 I am working on a movie recommendation system and need a sentence embedding for each movie description.\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={})]


 Identify the primary object in this image and classify it.\n###Input: Load an image from a given path, `path/to/image.jpg`, with a target size of (224, 224).\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\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'URL of the pre-trained model\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'Loads a pre-trained MobileNetV2 model for image classification trained on the ImageNet dataset with an input size of 224x224 pixels and a depth multiplier of 1.0.\'}', metadata={})]


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


 Among a list of sentences, find the most semantically similar pairs by converting them into dense vector representations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'list of sentences\', \'output\': \'list of embeddings\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'sentence1\', \'sentence2\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.\'}', metadata={})]


 I have some images of my last trip, and I want to identify the objects in the pictures.\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={})]


 When I go shopping, I stumble upon an item I can't identify. I want to take a picture and let my phone classify it for me.\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 have an app that allows users to sort their photos by features. Extract features from images for this app.\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={})]


 My kids found some rare insects, and I need some help identifying them. They took some photos with a smartphone. Can you help classify them?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': [\'input_shape\', \'output_shape\', \'trainable\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained feature vector model that can be used for image classification and other computer vision tasks.\'}', metadata={})]


 We need a way to detect phishing email domains. I want to find similarities in domain addresses to do so. Please provide embeddings of email domains.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "embeddings = embed([\'Hello world\', \'Embedding with TensorFlow Hub\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.803\'}, \'description\': \'A pre-trained model that converts text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


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


 I have a picture of a beach party, and I want to know how many people are there in the image.\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={})]


 For document similarity, I want to convert the following sentence into its vector representation: \"Artificial Intelligence is transforming the world in ways we couldn't have imagined a decade ago.\"\n###Input: Artificial Intelligence is transforming the world in ways we couldn't have imagined a decade ago.\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={})]


 Our team needs to keep track of various laptops and their usage in the workplace. Detect the laptops in the given images and provide their locations.\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={})]


 Classify a short clip of people playing football to understand the actions and behaviors of the players.\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={})]


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


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


 I have some animal photos, I want to train a tool will tell me what animal it is.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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={})]


 Show me an example where I can cluster similar movie plots.\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={})]


 Assist me in identifying food entities in grocery store images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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={})]


 Based on the text that my customers give me in different languages, create a vector so I can do customer sentiment analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various\', \'accuracy\': \'N/A\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', metadata={})]


 I am building a mobile app that can identify different breeds of dogs, and I need fast feature extraction from images to recognize the breeds.\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={})]


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


 I need to analyze a group of images and find out which ones are similar. First, I require extracting features of each image for comparison.\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 the city, there are many parking lots. We want to count the number of cars parked in a parking lot using a security camera. Can an API help us to do this?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel_id = \'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1\'\\ndetector = hub.load(model_id)\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nresult = detector(image)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP (mean Average Precision)\'}, \'description\': \'Detect objects in images using TensorFlow Hub and a pre-trained CenterNet model with ResNet50V1 FPN backbone\'}', metadata={})]


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


 I am trying to build a recommendation system that can match similar customer reviews. Convert the given text into a high-dimensional vector that I can use.\n###Input: \"This product is absolutely amazing! I am extremely happy with my purchase and would highly recommend it to others.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Converts text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text: A string or a list of strings to be embedded\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'description\': \'The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The model is trained and optimized for greater-than-word length text, such as sentences, phrases or short paragraphs.\'}', metadata={})]


 I would like to analyze the sentiments of some sentences by creating an embedding representation of each sentence.\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={})]


 Our customer support chat service needs to rank responses by their relevance to solve users' issues. So, we need to get embeddings for a list of response options.\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={})]