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


 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\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', 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 feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', 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 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\': [{\'name\': \'model_id\', \'type\': \'string\', \'description\': \'The unique identifier of the model to be loaded from TensorFlow Hub\'}], \'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.keras.preprocessing.image.load_img(\'image.jpg\')\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\n\\nresult = detector(image[np.newaxis, ...])", \'performance\': {\'dataset\': \'Open Images V4\', \'accuracy\': \'Not specified\'}, \'description\': \'Loads a TensorFlow Hub model for image object detection, allowing the detection of objects within images using the Faster R-CNN architecture with Inception ResNet V2.\'}', 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\': \'Image segmentation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image segmentation model\', \'api_name\': \'deeplabv3_257_mv_gpu\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': \'import tensorflow_hub as hub\\nmodel = hub.load("https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1")\\nsegmentation = model(input_image)\', \'performance\': {\'dataset\': \'PASCAL VOC 2012\', \'accuracy\': \'0.897\'}, \'description\': \'DeepLabv3 is a state-of-the-art deep learning model for image segmentation. It assigns a semantic label to each pixel in the input image, allowing for a more detailed understanding of the image content. This model is trained on the PASCAL VOC 2012 dataset and achieves a mean intersection-over-union (mIoU) score of 0.897.\'}', metadata={})]


 Tell me which car it is based on its picture.\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_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[None, 224, 224, 3]\', \'output_shape\': \'[None, 1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224,224,3), trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, which is optimized for mobile and edge devices. It is trained on ImageNet dataset and can be used for various image classification tasks.\'}', 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\': \'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={})]


 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\': \'Loading pre-trained models for image classification\', \'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 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\ndef load_image(file_path):\\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\\n    return img_array\\n\\n# Predict the class of the image\\nimg_array = load_image(\'path/to/your/image.jpg\')\\npredictions = model(img_array)\\n\\n# Get the class with the highest probability\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\'}', 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\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\', \'I am learning about embeddings.\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'Various tasks\', \'accuracy\': \'State-of-the-art\'}, \'description\': \'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language 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\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'(224, 224, 3)\', \'output_shape\': \'(1280,)\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   output_shape=(1280,))\\n])\\n\\n# Use the model to extract features from an image\\nfeature_vector = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\'}', 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 feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', 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\': \'Audio embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate embeddings for audio files\', \'api_name\': \'audio_embedding_model\', \'api_call\': "hub.load(\'https://tfhub.dev/google/yamnet/1\')", \'api_arguments\': {\'audio_inputs\': \'Tensor of shape [batch_size, num_samples] containing audio signals\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/yamnet/1\')\\n\\n# Load an audio file\\naudio_data = ... # Load audio data as a tensor\\n\\n# Generate embeddings\\nembeddings = model(audio_data)", \'performance\': {\'dataset\': \'YAMNet\', \'accuracy\': \'81.6% top-1 accuracy on AudioSet\'}, \'description\': \'This API allows you to generate embeddings for audio files using a pre-trained YAMNet model from TensorFlow Hub. The embeddings can be used for various tasks such as audio classification, similarity search, and more.\'}', 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\n# Example: Load an image and classify it\\n# image = ...  # Load an image\\n# result = model(image)\\n# print(result)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not provided\'}, \'description\': \'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\'}', 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\': \'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={})]


 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 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\': "model = 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  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', 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 feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', 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\': \'Extract image features\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.5.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),  # Can be True, see below.\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A compact representation of MobileNetV2 model trained on ImageNet dataset for extracting image features using TensorFlow Hub.\'}', 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\': \'Feature extraction from images\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'False\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n  hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', trainable=False),\\n  tf.keras.layers.Dense(10, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This API provides a pre-trained image feature vector model based on MobileNetV2 architecture. It extracts features from images and can be used as a base model for various image classification tasks.\'}', 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'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 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\ndef load_image(file_path):\\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\\n    return img_array\\n\\n# Predict the class of the image\\nimg_array = load_image(\'path/to/your/image.jpg\')\\npredictions = model(img_array)\\n\\n# Get the class with the highest probability\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\'}', 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\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', 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\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', 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\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', metadata={})]


 We need to find the semantic similarity between different product reviews.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', 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\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', 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\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Load and use pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\'\\nmodel = hub.load(model_id)\\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\\npredictions = model(image_array)\\npredicted_class = tf.argmax(predictions[0])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\'}', 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\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'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 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\ndef load_image(file_path):\\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\\n    return img_array\\n\\n# Predict the class of the image\\nimg_array = load_image(\'path/to/your/image.jpg\')\\npredictions = model(img_array)\\n\\n# Get the class with the highest probability\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\'}', 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\': \'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\': "model = 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  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', 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 image feature vectors\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'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), trainable=False)\\n])\\n\\n# Load an image and preprocess it\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.keras.applications.mobilenet_v2.preprocess_input(image)\\n\\n# Extract feature vector\\nfeature_vector = model.predict(image[np.newaxis, ...])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture on ImageNet dataset.\'}', 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\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'94.1%\'}, \'description\': \'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\'}', 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'94.1%\'}, \'description\': \'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\'}', 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\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', 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\': \'Loading a pre-trained image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'model_id\': \'URL of the TensorFlow Hub model\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Depends on the specific model\'}, \'description\': \'This API allows you to load a pre-trained image classification model from TensorFlow Hub for use in your applications.\'}', metadata={})]


 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 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 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\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image 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\': {\'url\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   trainable=False)\\n])\\n\\nx = tf.keras.layers.Input(shape=(224, 224, 3))\\nfeatures = model(x)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This module uses MobileNet V2 architecture for image feature vector extraction. It accepts a batch of images of shape (224, 224, 3) and returns a 1280-dimensional feature vector for each image.\'}', 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 pre-trained models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\n# Example: Load an image and classify it\\n# image = ...  # Load an image\\n# result = model(image)\\n# print(result)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not provided\'}, \'description\': \'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\'}', 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'imagenet_mobilenet_v2_130_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': \'URL of the pre-trained model\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')\\n\\n# Prepare an image for input to the model\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\n\\n# Predict the class of the image\\npredictions = model(input_image)\\npredicted_class_index = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained model for image classification using MobileNetV2 architecture and trained on ImageNet dataset\'}', metadata={})]


 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\': \'Convert text into a dense vector representation\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': {\'input\': \'Text or list of texts\'}, \'python_environment_requirements\': {\'tensorflow\': \'>= 2.0\', \'tensorflow_hub\': \'>= 0.8\'}, \'example_code\': "import tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\'])", \'performance\': {\'dataset\': \'STS benchmark\', \'accuracy\': \'Pearson correlation coefficient of 0.78\'}, \'description\': \'The Universal Sentence Encoder encodes text into a dense vector representation that can be used for various natural language processing 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 feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = 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  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', 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\': \'Loading pre-trained models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/inception_v3/classification/5\'\\nmodel = hub.load(model_id)\\n\\n# Example: Load an image and classify it\\n# image = ...  # Load an image\\n# result = model(image)\\n# print(result)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not provided\'}, \'description\': \'Load a pre-trained image classification model from TensorFlow Hub and use it to classify images.\'}', 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'94.1%\'}, \'description\': \'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\'}', 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 object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Identify and locate objects in images\', \'api_name\': \'ssd_mobilenet_v2\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': {\'input\': \'Tensor of shape [batch_size, height, width, 3]\', \'output\': "Dictionaries with keys \'detection_boxes\', \'detection_classes\', \'detection_scores\'"}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\ndetector = hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\'path/to/image.jpg\'))\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\nresult = detector(image_tensor)\\nprint(result)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP of 0.22\'}, \'description\': \'A pre-trained model for object detection using SSD MobileNet V2 architecture, capable of identifying and locating objects in images.\'}', 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\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', 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\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\', \'I am learning about embeddings.\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'Various tasks\', \'accuracy\': \'State-of-the-art\'}, \'description\': \'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', 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 feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = 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  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', 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\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[224, 224, 3]\', \'output_shape\': \'[1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = 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  tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This model extracts feature vectors from images using the MobileNetV2 architecture trained on the ImageNet dataset. The input images should have a shape of [224, 224, 3] and the output feature vectors have a shape of [1280].\'}', 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\': \'Detect objects in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow>=2.0\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\'\\ndetector = hub.load(model_id)\\n\\nimage_tensor = tf.image.decode_jpeg(tf.io.read_file(\'path/to/image.jpg\'))\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\n\\nresult = detector(image_tensor)\\nprint(result)", \'performance\': {\'dataset\': \'Open Images v4\', \'accuracy\': \'0.545 mAP\'}, \'description\': \'A pre-trained image object detection model based on the SSD architecture with MobileNetV2 as the backbone. The model is capable of detecting objects in images and returning their class labels and bounding box coordinates.\'}', metadata={})]


 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\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained model for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [{\'model_id\': \'string (URL or local path to the model)\'}], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\n# Load and preprocess an image\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\n# Classify the image\\npredictions = model(image_array)\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\\nprint(\'Predicted class:\', predicted_class)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained model for image classification using TensorFlow Hub.\'}', 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'94.1%\'}, \'description\': \'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\'}', 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification models\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\nimage = tf.random.normal([1, 224, 224, 3])\\npredictions = model(image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Varies depending on the model\'}, \'description\': \'A collection of pre-trained image classification models available on TensorFlow Hub, which can be used to classify images into various categories.\'}', metadata={})]


 I 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 feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


 Show me an example where I can cluster similar movie plots.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Text embedding\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Embedding text data\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': \'Text input\', \'python_environment_requirements\': \'TensorFlow, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello, world!\', \'How are you?\'])", \'performance\': {\'dataset\': \'Various text sources\', \'accuracy\': \'High similarity scores for semantically similar sentences\'}, \'description\': \'A text embedding model that converts text data into high-dimensional vectors, capturing semantic meaning and enabling various natural language processing tasks.\'}', 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\': \'Identify and locate objects in images\', \'api_name\': \'ssd_mobilenet_v2\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')", \'api_arguments\': {\'input\': \'Tensor of shape [batch_size, height, width, 3]\', \'output\': "Dictionaries with keys \'detection_boxes\', \'detection_classes\', \'detection_scores\'"}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\ndetector = hub.load(\'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2\')\\nimage_tensor = tf.image.decode_image(tf.io.read_file(\'path/to/image.jpg\'))\\nimage_tensor = tf.expand_dims(image_tensor, 0)\\nresult = detector(image_tensor)\\nprint(result)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'mAP of 0.22\'}, \'description\': \'A pre-trained model for object detection using SSD MobileNet V2 architecture, capable of identifying and locating objects 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\': \'Image segmentation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image segmentation model\', \'api_name\': \'deeplabv3_257_mv_gpu\', \'api_call\': "hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')", \'api_arguments\': {\'input\': \'Image tensor\', \'output\': \'Segmentation mask tensor\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nmodel = hub.load(\'https://tfhub.dev/tensorflow/deeplabv3_257_mv_gpu/1\')\\nimage_tensor = tf.image.resize(image, (257, 257))\\nsegmentation_mask = model(image_tensor)", \'performance\': {\'dataset\': \'PASCAL VOC 2012\', \'accuracy\': \'0.899\'}, \'description\': \'DeepLab is a state-of-the-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. This model is trained on the PASCAL VOC 2012 dataset.\'}', metadata={})]


 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained model for image classification\', \'api_name\': \'imagenet_mobilenet_v2_100_224_classification\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'url\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/your/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\n# Make predictions\\npredictions = model(image_array)\\n\\n# Decode the predictions\\nclass_names = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy(), top=1)\\nprint(class_names)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model based on the MobileNetV2 architecture. It is trained on the ImageNet dataset and can classify images into 1000 different classes. The model is optimized for mobile and edge devices with a small memory footprint and fast inference time.\'}', metadata={})]


 Compare the similarity between two sentences and provide a numerical score.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image Frame Interpolation\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Generate intermediate frames between two input frames\', \'api_name\': \'image-frame-interpolation\', \'api_call\': "hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\')", \'api_arguments\': {\'inputs\': \'A list of two input frames\'}, \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\ninputs = [frame1, frame2]\\ninterpolated_frame = hub.load(\'https://tfhub.dev/google/two-frame-vgg/1\').call(inputs)", \'performance\': {\'dataset\': \'Adobe240fps\', \'accuracy\': \'PSNR: 29.8 dB, SSIM: 0.902\'}, \'description\': \'This model generates intermediate frames between two input frames using a deep learning approach. It is useful for video frame rate up-conversion and slow motion generation.\'}', 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\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', 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\': \'Extract image features\', \'api_name\': \'imagenet_mobilenet_v2_100_224_feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input_shape\': \'[None, 224, 224, 3]\', \'output_shape\': \'[None, 1280]\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   trainable=False)\\n])\\n\\n# Example input: a batch of images with shape [batch_size, 224, 224, 3].\\nimages = tf.random.uniform((4, 224, 224, 3))\\n\\n# Extract features for the batch of images.\\nfeatures = model(images)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture on ImageNet dataset, with 100% depth multiplier and 224x224 input image resolution. Useful for extracting features from images.\'}', 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 feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', 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 classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading and using pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/5\'\\nmodel = hub.load(model_id)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\npredictions = model(image_array)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'94.1%\'}, \'description\': \'This API allows users to load pre-trained models from TensorFlow Hub for image classification tasks. It provides access to a variety of models, such as MobileNet, Inception, and ResNet, which have been trained on large datasets like ImageNet. Users can easily load a model using its model_id and perform image classification on their own images.\'}', 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\': \'Embedding text into high-dimensional vectors\', \'api_name\': \'universal-sentence-encoder\', \'api_call\': "hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\nembed = hub.load(\'https://tfhub.dev/google/universal-sentence-encoder/4\')\\nembeddings = embed([\'Hello world!\', \'I am learning about embeddings.\'])\\nprint(embeddings)", \'performance\': {\'dataset\': \'Various tasks\', \'accuracy\': \'State-of-the-art\'}, \'description\': \'Pre-trained model to embed input text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.\'}', 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\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'input\': \'image\', \'output\': \'feature_vector\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0\', \'tensorflow_hub\': \'>=0.12\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   trainable=False),\\n    tf.keras.layers.Dense(1)\\n])\\n\\nimage = tf.keras.preprocessing.image.load_img(\'path/to/image.jpg\', target_size=(224, 224))\\nimage_array = tf.keras.preprocessing.image.img_to_array(image)\\nimage_array = tf.expand_dims(image_array, 0)\\n\\nfeatures = model.predict(image_array)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API provides a compact representation of an image by extracting feature vectors using MobileNetV2 architecture pretrained on ImageNet dataset. It can be used for tasks like image classification, object detection, and image similarity.\'}', 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 preprocessing\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading\', \'api_name\': \'tensorflow_hub.KerasLayer\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/nnlm-en-dim128/2\')", \'api_arguments\': {\'handle\': \'A string, the handle of the Hub module to load\', \'kwargs\': \'Optional keyword arguments\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.8.0\'}, \'example_code\': \'import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(handle="https://tfhub.dev/google/nnlm-en-dim128/2")\\n])\', \'performance\': {\'dataset\': \'NNLM\', \'accuracy\': \'N/A\'}, \'description\': \'Loads a TensorFlow Hub module as a Keras Layer, allowing users to incorporate pre-trained models into their own custom models.\'}', metadata={})]


 Let's say my client wants to use an app to identify different dog breeds by uploading a picture. The app should inform them which breed they just uploaded.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Load and use pre-trained models for image classification\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'tensorflow\', \'tensorflow_hub\'], \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\'\\nmodel = hub.load(model_id)\\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\\npredictions = model(image_array)\\npredicted_class = tf.argmax(predictions[0])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'This API allows you to load and use pre-trained models for image classification using TensorFlow Hub. You can use different model ids to load different models and perform image classification tasks.\'}', metadata={})]


 Take a sample sentence and encode it into high-dimensional vector using available API.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Pre-trained image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')", \'api_arguments\': {\'model_id\': \'URL or path to the TensorFlow Hub model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.6.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel_id = \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\'\\nmodel = hub.load(model_id)\\nimage = tf.zeros([1, 224, 224, 3])\\noutput = model(image)\\nprint(output)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This API allows users to load a pre-trained image classification model from TensorFlow Hub and use it to classify images. The example provided demonstrates how to load the MobileNetV2 model, create a sample image input, and obtain the classification output.\'}', metadata={})]


 Analyze the news and figure out the topics of the articles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading pre-trained models for image classification\', \'api_name\': \'imagenet_mobilenet_v2_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 2.x, TensorFlow Hub\', \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = hub.load(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4\')\\n\\n# Load an image and preprocess it\\ndef load_image(file_path):\\n    img = tf.keras.preprocessing.image.load_img(file_path, target_size=(224, 224))\\n    img_array = tf.keras.preprocessing.image.img_to_array(img)\\n    img_array = tf.expand_dims(img_array, 0)  # Create a batch\\n    return img_array\\n\\n# Predict the class of the image\\nimg_array = load_image(\'path/to/your/image.jpg\')\\npredictions = model(img_array)\\n\\n# Get the class with the highest probability\\npredicted_class = tf.argmax(predictions, axis=-1).numpy()[0]", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image classification model using MobileNetV2 architecture with 100% width and 224x224 input size. The model is trained on the ImageNet dataset and can be used to classify images into 1000 different categories.\'}', metadata={})]


 I want to build an application that can distinguish different dog breeds from photos. Help me extract features from a set of dog images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Feature extraction from images\', \'api_name\': \'imagenet-mobilenet_v2_100_224-feature_vector\', \'api_call\': "hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\')", \'api_arguments\': {\'handle\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\', \'trainable\': \'Optional: Set to True if you want to fine-tune the model\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "model = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   trainable=False),\\n    tf.keras.layers.Dense(num_classes, activation=\'softmax\')\\n])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0% top-1 accuracy\'}, \'description\': \'A pre-trained image feature vector model based on MobileNetV2 architecture, trained on ImageNet dataset, for extracting features from images.\'}', metadata={})]


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


 Implement an image classifier to categorize the type of clothing items in a fashion eCommerce dataset.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image classifier\', \'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)\\n\\nimage = tf.keras.preprocessing.image.load_img(\'image.jpg\', target_size=(224, 224))\\ninput_image = tf.keras.preprocessing.image.img_to_array(image)\\ninput_image = tf.expand_dims(input_image, 0)\\n\\npredictions = model(input_image)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained image classifier using TensorFlow Hub to classify images using the MobileNet V2 model.\'}', metadata={})]


 Please suggest an API to quickly and easily classify animals in wildlife pictures using a pre-trained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection and classification 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)\\nresult = detector(image_tensor)", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not specified\'}, \'description\': \'This API allows you to detect and classify objects in images using TensorFlow Hub and pre-trained models such as SSD MobileNet V2.\'}', metadata={})]


 Calculate the feature vector of some images of different fruits.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image feature vector\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Image 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\': {\'url\': \'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\'}, \'python_environment_requirements\': {\'tensorflow\': \'>=2.0.0\', \'tensorflow_hub\': \'>=0.12.0\'}, \'example_code\': "import tensorflow as tf\\nimport tensorflow_hub as hub\\n\\nmodel = tf.keras.Sequential([\\n    hub.KerasLayer(\'https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\',\\n                   input_shape=(224, 224, 3),\\n                   trainable=False)\\n])\\n\\nx = tf.keras.layers.Input(shape=(224, 224, 3))\\nfeatures = model(x)\\n", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.9%\'}, \'description\': \'This module uses MobileNet V2 architecture for image feature vector extraction. It accepts a batch of images of shape (224, 224, 3) and returns a 1280-dimensional feature vector for each image.\'}', metadata={})]


 I need to classify common items found in a household. How do I do this using a pre-trained model?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image object detection\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Object detection in images\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/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 = tf.keras.preprocessing.image.img_to_array(image)\\nimage = tf.expand_dims(image, axis=0)\\n\\nresult = detector(image)\\n", \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'0.320\'}, \'description\': \'This API allows for object detection in images using a pre-trained model from TensorFlow Hub. The model used in this example is the SSD MobileNet V2, which has been trained on the COCO dataset.\'}', metadata={})]


 I'm organizing an animal-based flashcard game for kids, sort these images by animal content using a pre-trained model.\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_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\': "model = 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])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.0%\'}, \'description\': \'A pre-trained image feature vector model using MobileNetV2 architecture on the ImageNet dataset. It can be used for various image recognition tasks by extracting meaningful features from input images.\'}', metadata={})]


 We need to develop an app to quickly identify plants. Can you provide a pre-trained model to classify plant images?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Image classification\', \'framework\': \'TensorFlow Hub\', \'functionality\': \'Loading a pre-trained image classification model\', \'api_name\': \'model_id\', \'api_call\': "hub.load(\'https://tfhub.dev/google/imagenet/inception_v3/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/inception_v3/classification/4\'\\nmodel = hub.load(model_id)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Not specified\'}, \'description\': \'This API allows you to load a pre-trained image classification model from TensorFlow Hub using a specific model_id. The model can then be used to classify images into different categories.\'}', metadata={})]


 Design a mobile application which requires a custom model to classify 10 different dog breeds based on their photos.\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={})]


 We need to identify bird species from some bird images we collected.\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_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\',\\n                   input_shape=(224, 224, 3),\\n                   output_shape=(1280,))\\n])\\n\\n# Use the model to extract features from an image\\nfeature_vector = model.predict(image)", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'71.8%\'}, \'description\': \'A pre-trained image feature vector model using MobileNet V2 architecture, trained on ImageNet dataset. It can be used to extract features from images for various tasks such as image classification, object detection, and more.\'}', metadata={})]