7 open-source projects similar to toddkramer/documentclassifier, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks. The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes
Instructor-embedding is a natural language processing framework designed to transform unstructured text into high-dimensional numerical vectors. By utilizing a transformer-based encoder architecture, the system facilitates semantic retrieval, data classification, and similarity analysis across large datasets. The framework distinguishes itself through instruction-conditioned vector projection, which incorporates natural language instructions directly into the embedding process to improve performance for specific tasks without requiring additional training. It functions as a contrastive learni
🏷 iOS11 demo application for predicting gender from first names.
Swift Core ML 3 implementations of GPT-2, DistilGPT-2, BERT, and DistilBERT for Question answering. Other Transformers coming soon!
Profiler Application using Sentiment Analysis
Simple iMessage app to detect whether a message is spam. Model trained using the SMS Spam Collection Dataset in sklearn and converted using coremltools into .mlmodel format. After downloading and building project, copy a message to the clipboard and hit the Guess button.
😃 iOS11 demo application for sentiment polarity analysis.