
# Model Card for FLAN-T5 XL

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg"
alt="drawing" width="600"/>

#  Table of Contents

0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
5. [Training Details](#training-details)
6. [Evaluation](#evaluation)
7. [Environmental Impact](#environmental-impact)
8. [Citation](#citation)

# 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](https://huggingface.co/t5-large).

# Model Details

## Model Description


The details are in the original [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl)

{"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "tags": ["text2text-generation"], "datasets": ["svakulenk0/qrecc", "taskmaster2", "djaym7/wiki_dialog", "deepmind/code_contests", "lambada", "gsm8k", "aqua_rat", "esnli", "quasc", "qed"], "widget": [{"text": "Translate to German:  My name is Arthur", "example_title": "Translation"}, {"text": "Please answer to the following question. Who is going to be the next Ballon d'or?", "example_title": "Question Answering"}, {"text": "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering.", "example_title": "Logical reasoning"}, {"text": "Please answer the following question. What is the boiling point of Nitrogen?", "example_title": "Scientific knowledge"}, {"text": "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?", "example_title": "Yes/no question"}, {"text": "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?", "example_title": "Reasoning task"}, {"text": "Q: ( False or not False or False ) is? A: Let's think step by step", "example_title": "Boolean Expressions"}, {"text": "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?", "example_title": "Math reasoning"}, {"text": "Premise:  At my age you will probably have learnt one lesson. Hypothesis:  It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?", "example_title": "Premise and hypothesis"}]}
gguf text2text-generation en fr ro de multilingual dataset:svakulenk0/qrecc dataset:taskmaster2 dataset:djaym7/wiki_dialog dataset:deepmind/code_contests dataset:lambada dataset:gsm8k dataset:aqua_rat dataset:esnli dataset:quasc dataset:qed license:apache-2.0 region:us