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sileod/mdeberta-v3-base-tasksource-nli
mdeberta-v3-base-tasksource-nli is a zero-shot classification model from sileod. Use it when you need labels you did not train the model on. It is set up for transformers. The card lists the license as apache-2.0.
Multilingual mdeberta-v3-base with 30k steps multi-task training on mtasksource This model can be used as a stable starting-point for further fine-tuning, or directly in zero-shot NLI model or a zero-shot pipeline.
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From the Hugging Face model README
Multilingual mdeberta-v3-base with 30k steps multi-task training on mtasksource This model can be used as a stable starting-point for further fine-tuning, or directly in zero-shot NLI model or a zero-shot pipeline. In addition, you can use the provided adapters to directly load a model for hundreds of tasks.
!pip install tasknet, tasksource -q
import tasknet as tn
pipe=tn.load_pipeline(
'sileod/mdeberta-v3-base-tasksource-nli',
'miam/dihana')
pipe(['si','como esta?'])
For more details, see deberta-v3-base-tasksource-nli and replace tasksource by mtasksource.
https://github.com/sileod/tasksource/ https://github.com/sileod/tasknet/
For help integrating tasksource into your experiments, please contact [email protected].
For more details, refer to this article:
@article{sileo2023tasksource,
title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
author={Sileo, Damien},
url= {https://arxiv.org/abs/2301.05948},
journal={arXiv preprint arXiv:2301.05948},
year={2023}
}