However, this drawback is inherited from the underlying model classandisnotaproperty ofourretrieval-based approach. However,thechoice of the underlying dataset as well as the overall construction strategy of this database isnot further investigated. This would beaninteresting direction forfuture work, aswealready observethatamodel trained only on ImageNet acquires strong zero-shot capabilities, see e.g. Forourmodel,this concerns the data used in training and inference, as the retrieval database can be considered as a part ofthe model. That is in contrast to the image database used for the retrieval algorithm: Here, retrieved images have a discernible effect on the output, and the database used during inference may only consist ofrelativelyfewhighquality images.

Electronic health records (EHRs) include simple features like patient age together with more complex data like care history that are informative but not easily represented as individual features. To better harness such data, we developed an interpretable hybrid feature- and similarity-based model for supervised learning that combines feature and kernel learning for prediction and for investigation of causal relationships. We fit our hybrid models by convex optimization with a sparsity-inducing penalty on the kernel. Depending on the desired model interpretation, the feature and kernel coefficients can be learned sequentially or simultaneously. The hybrid models showed comparable or better predictive performance than solely feature- or similarity-based approaches in a simulation study and in a case study to predict two-year risk of loneliness or social isolation with EHR data from a complex primary health care population. Using the case study we also present new kernels for high-dimensional indicator-coded EHR data that are based on deviations from population-level expectations, and we identify considerations for causal interpretations.

To make the predictive models more robust and outperforming, we need to use those modules and processes that are lightweight and can work faster. Ktrain is a lightweight python wrapper that provides such features to an extent. It is a lightweight wrapper for the deep learning library TensorFlow Keras that helps in building, training, and deploying neural networks and other machine learning models. In this article, we are going to discuss the ktrain package in detail. We will go through its important features and pre-trained models available with it.

This tutorial includes a simplified and clean implementation of Bert using ktrain to classify tweets which placed me in the top 15% of the leaderboard on kaggle. Predict which Tweets are about real disasters and which ones are not using Bert Model. We would be using numpy and pandas for processing our dataset, matplotlib and seaborn for data visualization, and ktrain for implementing our bert model. We would first see all the features having missing values. This would include data from both the training and testing dataset.

Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. Tasks such as text classification and image classification can be accomplished easily with only a few lines of code. Install TensorFlow 2 if it is not already installed (e.g., pip install tensorflow) The above should be all you need on Linux systems and cloud computing environments like Google Colab and AWS EC2. If you are using ktrain on a Windows computer, you can follow these more detailed instructions that include some extra steps. This code was tested on Ubuntu 18.04 LTS using TensorFlow 2.3.1 and Python 3.6.9.

The Hugging Face transformers package is an immensely popular Python library providing pretrained models that are extraordinarily useful for a variety of natural language processing (NLP) tasks. It previously supported only PyTorch, but, as of late 2019, TensorFlow 2 is supported as well. While the library can be used for many tasks from Natural Language Inference (NLI) to Question-Answering, text classification remains one of the most popular and practical use cases. The ktrain library is a lightweight wrapper for tf.keras in TensorFlow 2. It is designed to make deep learning and AI more accessible and easier to apply for beginners and domain experts. As of version 0.8, ktrain now includes a simplified interface to Hugging Face transformers for text classification.