tensorflow-hub

import tensorflow as tf import tensorflow_hub as hub # Load a pre-trained text embedding model from TensorFlow Hub # This model generates 128-dimensional embeddings for text inputs. model_url = "https://tfhub.dev/google/nnlm-en-dim128/2" embed = hub.KerasLayer(model_url, input_shape=[], dtype=tf.string, trainable=False) # Example usage: Generate embeddings for a list of sentences sentences = [ "Hello TensorFlow Hub!", "This is a test sentence for text embedding.", "Machine learning is fun." ] embeddings = embed(tf.constant(sentences)) print(f"Input sentences: {sentences}") print(f"Generated embeddings shape: {embeddings.shape}") print(f"First embedding (first 5 values): {embeddings[0, :5].numpy()}") # You can then use these embeddings as input to another Keras layer # for tasks like classification. classifier_model = tf.keras.Sequential([ embed, tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) classifier_model.summary()