Popular GitHub Repositories for Error Classes

Distilled variant of Whisper for speech recognition. 6x faster, 50% smaller, within 1% word error rate.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)

Evaluate your speech-to-text system with similarity measures such as word error rate (WER)

A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

A fast, low-resource Natural Language Processing and Text Correction library written in Rust.

Use AI-powered code edits, explanations, code generation, error diagnosis, and chat in Visual Studio Code with the official OpenAI API.

Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN (GRU and LSTM) and Word Embeddings on Tensorflow.

Failure archive for ChatGPT and similar models

Anno-Mage: A Semi Automatic Image Annotation Tool which helps you in annotating images by suggesting you annotations for 80 object classes using a pre-trained model

Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN (Convolutional Neural Network) and Word Embeddings on Tensorflow.

EdgeChains.js is Full-Stack GenAI library. Front-end, backend, apis, prompt management, distributed computing. All core prompts & chains are managed declaratively in jsonnet (and not hidden in classes)

[ICLR 2024 Oral] Supervised Pre-Trained 3D Models for Medical Image Analysis (9,262 CT volumes + 25 annotated classes)

3.41% and 17.11% error on CIFAR-10 and CIFAR-100

Tensor network based quantum software framework for the NISQ era

Python module for evaluating ASR hypotheses (e.g. word error rate, word recognition rate).

PyTorch impelementations of BERT-based Spelling Error Correction Models. 基于BERT的文本纠错模型，使用PyTorch实现。

UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

Control adaptive filters with neural networks.