---
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:nnayak2@cs.brown.edu)

## 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}}
```