transformers

BERT classification model for processing texts longer than 512 tokens. Text is first divided into smaller chunks and after feeding them to BERT, intermediate results are pooled. The implementation allows fine-tuning.

Overview about state-of-art imitation learning techniques for robotic manipulation, enabling generalization across diverse tasks and environments.

<트랜스포머를 활용한 자연어 처리> 예제 코드를 위한 저장소입니다.

303 份 AI/LLM 中文讲义，支持在线阅读、PDF 下载和 LaTeX 源码查看 | Stanford CS336/CS224R/CS25 | Berkeley LLM Agents | Agent 工程实践

X-Transformer: Taming Pretrained Transformers for eXtreme Multi-label Text Classification

A convenient way to link, deduplicate, aggregate and cluster data(frames) in Python using deep learning

State-of-the-art CLIP/SigLIP embedding models finetuned for the fashion domain. +57% increase in evaluation metrics vs FashionCLIP 2.0.

Code for Editing Factual Knowledge in Language Models

The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version.

[CVPR'22] HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening

Collection of NLP model explanations and accompanying analysis tools

Easy to use and understand multiple-choice question generation algorithm using T5 Transformers.

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series, from ServiceNow Research

Highly commented implementations of Transformers in PyTorch

AI model that understands text & humanoids.

Mantis: Lightweight Foundation Model for Time Series Classification

An repository containing all the LLM notebooks with tutorial and projects

Using pre-trained BERT models for Chinese and English NER with 🤗Transformers

[CVPR'24 Spotlight] The official implementation of "State Space Models for Event Cameras"

Implementation of Diffusion Policy, Toyota Research's supposed breakthrough in leveraging DDPMs for learning policies for real-world Robotics