flan-t5-base

Model Card for FLAN-T5 base Table of Contents 0. TL;DR 1. Model Details 2. Usage 3. Uses 4. Bias, Risks, and Limitations 5. Training Details 6. Evaluation 7. Environmental Impact 8. Citation 9. Model Card Authors TL;DR If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the T5 model card. Model Details Model Description **Model type:** Language model **Language(s) (NLP):** English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian **License:** Apache 2.0 **Related Models:** All FLAN-T5 Checkpoints **Original Checkpoints:** All Original FLAN-T5 Checkpoints **Resources for more information:** Research paper GitHub Repo Hugging Face FLAN-T5 Docs (Similar to T5) Usage Find below some example scripts on how to use the model in `transformers`: Using the Pytorch model Running the model on a CPU Click to expand Running