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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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