Create README.md

README.md

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+---
+language: 
+- en
+- fr
+- ro
+- de
+- multilingual
+
+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"
+
+tags:
+- text2text-generation
+
+datasets:
+- svakulenk0/qrecc
+- taskmaster2
+- djaym7/wiki_dialog
+- deepmind/code_contests
+- lambada
+- gsm8k
+- aqua_rat
+- esnli
+- quasc
+- qed
+
+
+license: apache-2.0
+---
+
+# 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 detail are in the original [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl)