deberta v3 base tasksource nli EU AI Act Compliance Profile

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

from transformers import pipeline
pipe = pipeline("text-classification",model="sileod/deberta-v3-base-tasksource-nli")
pipe([dict(text='there is a cat',
  text_pair='there is a black cat')]) #list of (premise,hypothesis)
# [{'label': 'neutral', 'score': 0.9952911138534546}]

[TA] Tasksource-adapters: 1 line access to hundreds of tasks

# !pip install tasknet
import tasknet as tn
pipe = tn.load_pipeline('sileod/deberta-v3-base-tasksource-nli','glue/sst2') # works for 500+ tasksource tasks
pipe(['That movie was great !', 'Awful movie.'])
# [{'label': 'positive', 'score': 0.9956}, {'label': 'negative', 'score': 0.9967}]

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