> 🎯 TL;DR: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. This model is part of the Ettin suite — the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories. — 📊 Performance Highlights — 🚀 Quick Start — Model Description — Training Data — 🤖 Model Family — Encoder Models — Decoder Models — Cross-Objective Models — Accessing Training Checkpoints — 🔬 Research Applications — Training Details — Model Architecture — Usage Examples — Fine-tuning Examples — 📋 Training and Evaluation — ❓ FAQ — Citation — License — GLUE Average: 88.9 vs 88.4 (Base), 90.8 vs 90.4 (Large) — MTEB v2 English Retrieval: 45.7 vs 43.9 (Base), 48.4 vs 47.0 (Large)
Модальности:
Генерация текста
Области применения:
Генерация кода
Задача: Генерация текста
Автор: jhu-clsp
Теги: modernbert-decoder, ettin, decoder, en, endpoints_compatible
Лайков: 4 | Загрузок: 209
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
