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) to train Bonito.
from datasets import load_dataset
dataset = load_dataset("BatsResearch/ctga-v1")
Dataset Description
- Repository: Github Repo
- Paper: Arxiv
- Point of Contact: Nihal V. Nayak
Dataset Creation
The dataset is derived from P3 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
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}}