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Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
Easy-to-use and powerful LLM and SLM library with awesome model zoo.
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）
State of the Art Natural Language Processing
Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the CASL project: http://casl-project.ai/
Self-contained Machine Learning and Natural Language Processing library in Go
A BERT model for scientific text.
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
BlueBERT, pre-trained on PubMed abstracts and clinical notes (MIMIC-III).
bert-loves-chemistry: a repository of HuggingFace models applied on chemical SMILES data for drug design, chemical modelling, etc.
斑海豹 — Phocinae-Largha-150M-v1: a 144.3M bilingual decision model. Tiny model. Big decisions. 小海豹，大决断。
DBMDZ BERT, DistilBERT, ELECTRA, GPT-2 and ConvBERT models
Using Machine Learning to predict the type of exercise from movement data
Examples of Tensorflow Lite on Android
Cryptographic Dataset Generation & Modelling Framework
Java Scientific Computing Library - Signal Processing, FFT, Filters, PSD, DCT, SciPy-like APIs for JVM & Android used in Machine Learning and Data Science.
Plan and train German transformer models.
A library for A* pathfinding. Written for easy integration and plug-and-play
Code and data for the paper "Evaluating German Transformer Language Models with Syntactic Agreement Tests" (Zaczynska et al., 2020)