update README + gitignore

.gitignore

@@ -0,0 +1,114 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+# C extensions
+*.so
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+# Translations
+*.mo
+*.pot
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+# Flask stuff:
+instance/
+.webassets-cache
+# Scrapy stuff:
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+docs/_build/
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+#   intended to run in multiple environments; otherwise, check them in:
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+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
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+__pypackages__/
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+# SageMath parsed files
+*.sage.py
+# Environments
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+# Rope project settings
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+# mkdocs documentation
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+# mypy
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+# Pyre type checker
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+# pytype static type analyzer
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+# Cython debug symbols
+cython_debug/
+# Datasets locks
+*.lock

README.md

@@ -1,7 +1,19 @@
 ---
 YAML tags:
-- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
-- WIP!
+annotations_creators:
+- crowdsourced
+- expert-generated
+languages:
+- en
+licenses:
+- apache-2.0
+multilinguality:
+- monolingual
+pretty_name: P3
+size_categories:
+- 10M<n<100M
+task_categories:
+- other
 ---
 
 # Dataset Card for P3
@@ -35,16 +47,16 @@ YAML tags:
 
 - **Homepage:** https://bigscience.huggingface.co/promptsource
 - **Repository:** https://github.com/bigscience-workshop/promptsource/
-- **Paper:** TBA
+- **Paper:** TODO
 - **Point of Contact:** Victor Sanh (victor@huggingface.co)
 
 ### Dataset Summary
 
-P3 is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for *Premise, Hypothesis, Label*. An input template would be *If {Premise} is true, is it also true that {Hypothesis}?*, whereas a target template can be defined with the label choices *Choices[label]*. Here *Choices* is prompt-specific metadata that consists of the options *yes, maybe, no* corresponding to *label* being entailment (0), neutral (1) or contradiction (2).
+P3 (Pubic Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for *Premise, Hypothesis, Label*. An input template would be *If {Premise} is true, is it also true that {Hypothesis}?*, whereas a target template can be defined with the label choices *Choices[label]*. Here *Choices* is prompt-specific metadata that consists of the options *yes, maybe, no* corresponding to *label* being entailment (0), neutral (1) or contradiction (2).
 
-Prompts are collected using [Promptsource](https://github.com/bigscience-workshop/promptsource), an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts is publicly available on [Promptsource](https://github.com/bigscience-workshop/promptsource).
+Prompts are collected using [Promptsource](https://github.com/bigscience-workshop/promptsource), an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts of P3 is publicly available on [Promptsource](https://github.com/bigscience-workshop/promptsource).
 
-To train [T0*](https://huggingface.co/bigscience/T0pp_11B), we used a subset of the prompts available in Promptsource (see details [here](https://huggingface.co/bigscience/T0pp_11B#training-data)). However, some of the prompts use `random.choice`, a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. **The data available here are the materialized version of the prompted datasets used in [Multi-task enables task zero-shot generalization](TODO) which represent only a subset datasets for which there is at least one prompt on Promptsource.**
+To train [T0*](https://huggingface.co/bigscience/T0pp), we used a subset of the prompts available in Promptsource (see details [here](https://huggingface.co/bigscience/T0pp#training-data)). However, some of the prompts use `random.choice`, a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. **The data available here are the materialized version of the prompted datasets used in [{Multitask Prompted Training Enables Zero-Shot Task Generalization](TODO) which represent only a subset of the datasets for which there is at least one prompt on Promptsource.**
 
 ### Supported Tasks and Leaderboards
 
@@ -60,29 +72,35 @@ The data in P3 are in English (BCP-47 `en`).
 
 An example of "train" looks as follows:
 ```bash
+TODO
 ```
 
-To check all the prompted examples, you can use [Promptsource hosted tool](http://bigscience.huggingface.co/promptsource) and choose the `Prompted dataset viewer` mode in the left panel.
+To check all the prompted examples, you can use the [Promptsource hosted tool](http://bigscience.huggingface.co/promptsource) and choose the `Prompted dataset viewer` mode in the left panel.
 
 
 ### Data Fields
 
 The data fields are the same among all splits:
-- `input_text`: the natural language input fed to the model
-- `target_text`: the natural language target that the model has to generate
-- `tokenized_input`: the tokenized `input_text` with T5's tokenizer
-- `tokenized_target`: the tokenized `target_text` with T5's tokenizer
+- `answer_choices`: the choices (in natural language) available to the model
+- `inputs_pretokenized`: the natural language input fed to the model
+- `targets_pretokenized`: the natural language target that the model has to generate
+- `inputs`: the tokenized input with T5's tokenizer
+- `targets`: the tokenized target with T5's tokenizer
+- `idx`: identifier of the (example, option) in the case of rank classification
+- `weight`: a weight for the example produced by seqio (always set to 1.0)
+- `is_correct`: whether the (example, option) is the correct one
 
 ### Data Splits
 
 |Data(sub)set|Split|Number of examples|
 |-|-|-|
+|WIP|WIP|WIP|
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-P3 relies on the Hugging Face Dataset library. Any public dataset in the Datasets library can be prompted. We select the datasets that have at least one subset in English and excluded datasets containing (predominantly) non-natural language examples.
+The Public Pool of Prompts relies on the Hugging Face Dataset library. Any public dataset in the Datasets library can be prompted. We select the datasets that have at least one subset in English and excluded datasets containing (predominantly) non-natural language examples.
 
 We conservatively decided not to prompt datasets that contain potentially harmful content (for instance, datasets built on social media content). However, we sometimes prompt datasets that are purposefully built to measure bias and fairness of trained models, and reserve these prompted datasets (the validation or test sets) for evaluation purposes.
 
@@ -91,40 +109,33 @@ We conservatively decided not to prompt datasets that contain potentially harmfu
 Here's the full list of the datasets present in the materialized version of P3:
 - Multiple-Choice QA
   - CommonsenseQA
-  - Cosmos
   - DREAM
-  - QASC
   - QUAIL
-  - Quarrel
   - QuaRTz
-  - SciQ
   - Social IQA
-  - Wiki Hop
   - WiQA
+  - Cosmos
+  - QASC
+  - Quarel
+  - SciQ
+  - Wiki Hop
   - ARC
-  - BoolQ
-  - Circa
-  - MC-TACO
-  - MultiRC
   - OpenBookQA
+  - MultiRC
   - PIQA
   - RACE
+  - HellaSwag
+  - BoolQ
 - Extractive QA
   - Adversarial QA
-  - DuoRC
   - Quoref
+  - DuoRC
   - ROPES
-  - TyDiQA
-  - CoQA
-  - DROP
-  - QA SRL
-  - QuAC
-  - ReCoRD
   - SQuAD v2
+  - ReCoRD
 - Close-book QA
   - Hotpot QA
   - Wiki QA
-  - NQ Open
   - Trivia QA
   - Web Questions
 - Structure-to-text
@@ -164,15 +175,6 @@ Here's the full list of the datasets present in the materialized version of P3:
   - HellaSwag
   - Story Cloze
 
-TODO: recheck this list to match Figure 2 in final version of paper
-
-<!-- #### Initial Data Collection and Normalization
-
-
-#### Who are the source language producers?
-
-[More Information Needed] -->
-
 ### Annotations
 
 The prompts available in Promptsource are collected as part of BigScience, one-year long research workshop on large multilingual models and datasets. 36 contributors affiliated with 24 institutions in 8 countries participated to the prompt collection. Contributors are in majority machine learning researchers or machine learning engineers.
@@ -181,38 +183,6 @@ The main annotation guideline was that prompts needed to be grammatical and unde
 
 The full annotation given to the contributors can be found [here](https://github.com/bigscience-workshop/promptsource/blob/main/CONTRIBUTING.md). *Note to self: the link is currently being updated with the)
 
-<!-- #### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed] -->
-
-<!-- ### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed] -->
-
-<!-- ### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed] -->
-
 ### Licensing Information
 
 The dataset is released under Apache 2.0.
@@ -220,7 +190,7 @@ The dataset is released under Apache 2.0.
 ### Citation Information
 
 ```bibtex
-WIP
+TODO
 ```
 
 ### Contributions