tasksource/zero-shot-label-nli
tasksource classification tasks recasted as natural language inference. This dataset is intended to improve label understanding in zero-shot classification HF pipelines. Inputs that are text pairs are separated by a newline (\n). from transformers import pipeline classifier = pipeline(model="sileod/deberta-v3-base-tasksource-nli") classifier( "I have a problem with my iphone that needs to be resolved asap!!", candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"], )… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/zero-shot-label-nli.
tasksource classification tasks recasted as natural language inference. This dataset is intended to improve label understanding in zero-shot classification HF pipelines.
Inputs that are text pairs are separated by a newline (\n).
from transformers import pipeline
classifier = pipeline(model="sileod/deberta-v3-base-tasksource-nli")
classifier(
"I have a problem with my iphone that needs to be resolved asap!!",
candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
)deberta-v3-base-tasksource-nli now includes label-nli in its training mix (a relatively small portion, to keep the model general, but note that nli models work for label-like zero shot classification without specific supervision (https://aclanthology.org/D19-1404.pdf).
@article{sileo2023tasksource,
title={tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation},
author={Sileo, Damien},
year={2023}
}