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---
configs:
- config_name: default
data_files:
- path: train/*.arrow
split: train
task_categories:
- text-generation
language:
- en
size_categories:
- 1M<n<10M
pretty_name: conditional task generation with attributes
---
# Dataset Card for ctga-v1
## Dataset Details
`ctga-v1` or conditional task generation with attributes is a new dataset created by remixing existing instruction tuning datasets ([P3](https://github.com/bigscience-workshop/promptsource)) to train [Bonito](https://huggingface.co/BatsResearch/bonito-v1).
```python3
from datasets import load_dataset
dataset = load_dataset("BatsResearch/ctga-v1")
```
### Dataset Description
- **Repository:** [Github Repo](https://github.com/BatsResearch/bonito)
- **Paper:** [Arxiv](TODO)
- **Point of Contact:** [Nihal V. Nayak](mailto:[email protected])
## Dataset Creation
The dataset is derived from [P3](https://github.com/bigscience-workshop/promptsource) by annotating 323 prompt templates from 39 datasets with 16 task types.
The prompt templates in P3 are remixed to create the meta-templates, which, in turn, generate the training examples.
The meta-template input has a task type (<|tasktype|>) as an attribute followed by the unannotated text or context (<|context|>).
The output of the meta-template comprises the attributed task with the prompt or task description and the context ({context}) followed by a pipe symbol (<|pipe|>) and the solution to the task.
We use the <|pipe|> symbol to separate the instruction and response pair that is used for adapting the downstream model.
### Data Instances
Each data instance contains the following features: _context_, _task_input_ _task_output_ _dataset_ _dataset_config_ _task_type_ _input_ and _output_.
The (_input_, _output_) is the pair we used to train Bonito model.
### Data Fields
- 'context': input context
- 'task_input': prompted input without context
- 'task_output': corrosponding output
- 'dataset': source dataset
- 'dataset_config': source dataset configuration
- 'task_type': corrsponding task type
- 'input': reformatted input
- 'output': reformatted output
### Source Data
All the datasets are sourced from the datasets library.
- Extractive Question Answering & Question Generation
- adversarial_qa/dbert
- adversarial_qa/dbidaf
- adversarial_qa/droberta
- duorc/ParaphraseRC
- duorc/SelfRC
- squad
- Topic Classification
- ag_news
- dbpedia_14
- hellaswag
- duorc/ParaphraseRC
- duorc/SelfRC
- squad
- Sentiment Analysis
- amazon_polarity
- imdb
- rotten_tomatoes
- yelp_review_full
- Natural Language Inference
- anli
- super_glue/cb
- Multiple-Choice Question Answering
- app_reviews
- cosmos_qa
- dream
- qasc
- quail
- quartz
- race/all
- social_i_qa
- super_glue/boolq
- super_glue/record
- wiki_hop/original
- Text Generation
- app_reviews
- cnn_dailymail/3.0.0
- dream
- duorc/ParaphraseRC
- duorc/SelfRC
- gigaword
- samsum
- Summarization
- cnn_dailymail/3.0.0
- duorc/ParaphraseRC
- duorc/SelfRC
- gigaword
- multi_newspaws/labeled_final
- samsum
- xsum
- Paraphrase Generation & Identification
- glue/mrpc
- multi_newspaws/labeled_final
- Yes-No Question Answering
- race/all
- social_i_qa
- super_glue/boolq
- Sentence Completion
- hellaswag
- super_glue/copa
- Textual Entailment
- super_glue/rte
- Word Sense Disambiguation
- super_glue/wic
- Coreference Resolution
- super_glue/wsc.fixed
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@inproceedings{bonito:aclfindings24,
title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation},
author = {Nayak, Nihal V. and Nan, Yiyang and Trost, Avi and Bach, Stephen H.},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
year = {2024}}
```
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