Тип модели: MT-LLaMA — это многозадачная модель с открытым исходным кодом, обученная тонкой настройке LLaMA на массивных задачах в P3 (т.е. T0 Train). Concretely, the used datasets during training and task taxonomy are listed below: Multi-choice QA: CommonsenseQA, Cosmos QA, DREAM, QuAIL, QuaRTz, QASC, QuaRel, SciQ, Social IQA, Wiki Hop, WiQA Extractive QA: Adversarial QA, DuoRC, Quoref, ROPES Close-Book QA: Hotpot QA, Wiki QA Sentiment Classification: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp Topic Classification: AG News, DBPedia, TREC Structure-to-Text Generation: Common Gen, Wiki Bio Text Summarization: CN We primarily follow the protocols of Bigscience T0 to assess the generalization capability of our Multi-task LLaMA to: (1) Unseen Datasets (i.e., datasets from seen tasks); (2) Unseen Tasks. Our training codes are largely borrowed from FastChat We are also grateful for the efforts of LLaMA (from FAIR) and T0 (from BigScience), which serve as the foundation of…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: DAMO-NLP-SG
Теги: llama, text-generation-inference, endpoints_compatible
Лайков: 2 | Загрузок: 14
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
