Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy

Typefaces for source code beautification

Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI

The official Python client for the Hugging Face Hub.

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

The most accurate natural language detection library for Python, suitable for short text and mixed-language text

Library of graph clustering algorithms

Full named-entity (i.e., not tag/token) evaluation metrics based on SemEval’13

XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

Romanian Named Entity Corpus (RONEC) version 2.0

Romanian WordNet (Data + API for Python)

SIngle-label RED and multi-label REDv2 - datasets for emotion detection from Romanian short texts.

Romanian Semantic Textual Similarity Dataset

Named Entity Recognition for Romanian, based on transformer models