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---
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language:
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- en
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- fr
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- ro
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- de
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- multilingual
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widget:
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- text: "Translate to German: My name is Arthur"
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example_title: "Translation"
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- text: "Please answer to the following question. Who is going to be the next Ballon d'or?"
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example_title: "Question Answering"
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- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
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example_title: "Logical reasoning"
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- text: "Please answer the following question. What is the boiling point of Nitrogen?"
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example_title: "Scientific knowledge"
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- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
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example_title: "Yes/no question"
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- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
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example_title: "Reasoning task"
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- text: "Q: ( False or not False or False ) is? A: Let's think step by step"
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example_title: "Boolean Expressions"
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- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
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example_title: "Math reasoning"
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- 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?"
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example_title: "Premise and hypothesis"
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tags:
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- text2text-generation
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datasets:
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- svakulenk0/qrecc
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- taskmaster2
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- djaym7/wiki_dialog
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- deepmind/code_contests
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- lambada
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- gsm8k
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- aqua_rat
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- esnli
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- quasc
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- qed
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license: apache-2.0
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---
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# Model Card for FLAN-T5 XL
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg"
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alt="drawing" width="600"/>
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# Table of Contents
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0. [TL;DR](#TL;DR)
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1. [Model Details](#model-details)
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2. [Usage](#usage)
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3. [Uses](#uses)
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4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
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5. [Training Details](#training-details)
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6. [Evaluation](#evaluation)
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7. [Environmental Impact](#environmental-impact)
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8. [Citation](#citation)
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# TL;DR
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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.
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As mentioned in the first few lines of the abstract :
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> 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.
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**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).
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# Model Details
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## Model Description
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The details are in the original [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl)
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