link to t-zero repo + p3 materialized version

Author: VictorSanh

README.md

@@ -3,12 +3,14 @@ Promptsource is a toolkit for collecting and applying prompts to NLP datasets.
 
 Promptsource uses a simple templating language to programatically map an example of a dataset into a text input and a text target.
 
-Promptsource contains a growing collection of prompts (which we call **P3**: **P**ublic **P**ool of **P**rompts). As of October 18th, there are ~2'000 prompts for 170+ datasets in P3.
+Promptsource contains a growing collection of prompts (which we call **P3**: **P**ublic **P**ool of **P**rompts). As of October 18th, there are ~2'000 prompts for 170+ datasets in [P3](https://huggingface.co/datasets/bigscience/P3).
 Feel free to use these prompts as they are (you'll find citation details [here](#Citation)).
 
 Note that a subset of the prompts are still *Work in Progress*. You'll find the list of the prompts which will potentially be modified in the near future [here](WIP.md). Modifications will in majority consist of metadata collection, but in some cases, will impact the templates themselves. To facilitate traceability, Promptsource is currently pinned at version `0.1.0`.
 
-Propmtsource and P3 were originally developed as part of the paper [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207). We release T0* (pronounce "T Zero"), a series of model trained on P3. Checkpoints are available [here](https://huggingface.co/bigscience/T0pp). In particular, we recommend using T0++ (pronounce "T Zero Plus Plus") as it leads (on average) to the best performances on a variety of NLP tasks
+Propmtsource and P3 were originally developed as part of the paper [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207). We release T0* (pronounce "T Zero"), a series of model trained on [P3](https://huggingface.co/datasets/bigscience/P3). Checkpoints are available [here](https://huggingface.co/bigscience/T0pp). In particular, we recommend using T0++ (pronounce "T Zero Plus Plus") as it leads (on average) to the best performances on a variety of NLP tasks.
+
+**You will find the official repository to reproduce the results of T Zero here: https://github.com/bigscience-workshop/t-zero.**
 
 ## Setup
 1. Download the repo
@@ -52,7 +54,7 @@ from promptsource.templates import DatasetTemplates
 ag_news_prompts = DatasetTemplates('ag_news')
 # Select a prompt by name
 prompt = ag_news_prompts["classify_question_first"]
-# Apply the prompt on the example 
+# Apply the prompt on the example
 result = prompt.apply(example)
 print("INPUT: ", result[0])
 print("TARGET: ", result[1])
@@ -107,7 +109,7 @@ Promptsource was developed as part of the [BigScience project for open research
 If you want to cite this P3 or Promptsource, you can use this bibtex:
 ```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},