Model Card for DeBERTa-v3-base-tasksource-nli
NOTE
Deprecated: use https://huggingface.co/tasksource/deberta-small-long-nli for longer context and better accuracy.
This is DeBERTa-v3-base fine-tuned with multi-task learning on 600+ tasks of the tasksource collection.
This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for:
- Zero-shot entailment-based classification for arbitrary labels [ZS].
- Natural language inference [NLI]
- Hundreds of previous tasks with tasksource-adapters [TA].
- Further fine-tuning on a new task or tasksource task (classification, token classification or multiple-choice) [FT].
[ZS] Zero-shot classification pipeline
from transformers import pipeline
classifier = pipeline("zero-shot-classification",model="sileod/deberta-v3-base-tasksource-nli")
text = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(text, candidate_labels)
NLI training data of this model includes label-nli, a NLI dataset specially constructed to improve this kind of zero-shot classification.
[NLI] Natural language inference pipeline
