update pre-filled examples + make pronunciation more explicit

README.md

@@ -6,32 +6,71 @@ license: apache-2.0
 widget:
 - text: "A is the son's of B's uncle. What is the family relationship between A and B?"
 - text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."
-- text: "It's rainy today but it will stop in a few hours, when should I go for my run?"
-- text: "How many hydrogen atoms are in a water molecule?"
 - text: "Task: copy but say the opposite.\n
 PSG won its match against Barca."
 - text: "Is this review positive or negative? Review: Best cast iron skillet you will every buy."
-- text: "Question A:How is air traffic controlled?
+- text: "Question A: How is air traffic controlled?
 \nQuestion B: How do you become an air traffic controller?\nPick one: these questions are duplicates or not duplicates."
 - text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had foreign affairs experience as a former First Lady.
 \nIn the previous sentence, decide who 'her' is referring to."
 - text: "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.\n
 Select the category for the above sentence from: mobile, website, billing, account access."
-- text: "I don't have the proper tool to whisk a bowl of eggs.
-\nWhat should I use instead? Choose between a knife, a pen and a pair of chopsticks."
+- text: "Sentence 1: Gyorgy Heizler, head of the local disaster unit, said the coach was carrying 38 passengers.\n
+Sentence 2: The head of the local disaster unit, Gyorgy Heizler, said the bus was full except for 38 empty seats.\n\n
+Do sentences 1 and 2 have the same meaning?"
+- text: "Here's the beginning of an article, choose a tag that best describes the topic of the article: business, cinema, politics, health, travel, sports.\n\n
+The best and worst fo 007 as 'No time to die' marks Daniel Craig's exit.\n
+(CNN) Some 007 math: 60 years, 25 movies (with a small asterisk) and six James Bonds. For a Cold War creation, Ian Fleming's suave spy has certainly gotten around, but despite different guises in the tuxedo and occasional scuba gear, when it comes to Bond ratings, there really shouldn't be much argument about who wore it best."
+- text: "Max: Know any good websites to buy clothes from?\n
+Payton: Sure :) LINK 1, LINK 2, LINK 3\n
+Max: That's a lot of them!\n
+Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.\n
+Max: I'll check them out. Thanks.\n\n
+Who or what are Payton and Max referring to when they say 'them'?"
+- text: "Is the word 'table' used in the same meaning in the two previous sentences?\n\n
+Sentence A: you can leave the books on the table over there.\n
+Sentence B: the tables in this book are very hard to read."
+- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.\n
+The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.\n\n
+Which book is the leftmost book?"
+- text: "The word 'binne' means any animal that is furry and has four legs, and the word 'bam' means a simple sort of dwelling.\n\n
+Which of the following best characterizes binne bams?\n
+- Sentence 1: Binne bams are for pets.\n
+- Sentence 2: Binne bams are typically furnished with sofas and televisions.\n
+- Sentence 3: Binne bams are luxurious apartments.\n
+- Sentence 4: Binne bams are places where people live."
 ---
 
 # Model Description
 
-T0* is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.
+T0* (pronounce "T Zero") is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.
 
 # Intended uses
 
 You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"*, and the model will hopefully generate *"Positive"*.
 
+A few other examples that you can try:
+- *A is the son's of B's uncle. What is the family relationship between A and B?*
+- *Question A: How is air traffic controlled?<br>
+Question B: How do you become an air traffic controller?<br>
+Pick one: these questions are duplicates or not duplicates.*
+- *Is the word 'table' used in the same meaning in the two previous sentences?<br><br>
+Sentence A: you can leave the books on the table over there.<br>
+Sentence B: the tables in this book are very hard to read.*
+- *Max: Know any good websites to buy clothes from?<br>
+Payton: Sure :) LINK 1, LINK 2, LINK 3<br>
+Max: That's a lot of them!<br>
+Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.<br>
+Max: I'll check them out. Thanks.<br><br>
+Who or what are Payton and Max referring to when they say 'them'?*
+- *On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.<br>
+The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.<br><br>
+Which book is the leftmost book?*
+- *Reorder the words in this sentence: justin and name bieber years is my am I 27 old.*
+
 # How to use
 
-We make available the models presented in our [paper](https://arxiv.org/abs/2110.08207) along with the ablation models. We recommend using the [T0pp](https://huggingface.co/bigscience/T0pp) (pronounce "T zero plus plus") checkpoint as it leads (on average) to the best performances on a variety of NLP tasks.
+We make available the models presented in our [paper](https://arxiv.org/abs/2110.08207) along with the ablation models. We recommend using the [T0pp](https://huggingface.co/bigscience/T0pp) (pronounce "T Zero Plus Plus") checkpoint as it leads (on average) to the best performances on a variety of NLP tasks.
 
 |Model|Number of parameters|
 |-|-|
@@ -256,7 +295,7 @@ To measure the extent to which our model reproduces gender biases, we evaluate o
 
 ```bibtex
 @misc{sanh2021multitask,
-      title={Multitask Prompted Training Enables Zero-Shot Task Generalization}, 
+      title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
       author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
       year={2021},
       eprint={2110.08207},