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@@ -270,10 +270,23 @@ pipeline_tag: zero-shot-classification
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  # Model Card for DeBERTa-v3-base-tasksource-nli
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  This is [DeBERTa-v3-base](https://hf.co/microsoft/deberta-v3-base) fine-tuned with multi-task learning on 560 tasks of the [tasksource collection](https://github.com/sileod/tasksource/)
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- This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for zero-shot NLI pipeline (similar to bart-mnli but better).
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- You can also load other tasks (see next paragraph) or further fine-tune the encoder for new task (classification, token classification or multiple-choice).
 
 
 
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- # Tasksource-adapters: 1 line access to more than 500 tasks
 
 
 
 
 
 
 
 
 
 
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  ```python
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  !pip install tasknet tasksource
 
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  # Model Card for DeBERTa-v3-base-tasksource-nli
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  This is [DeBERTa-v3-base](https://hf.co/microsoft/deberta-v3-base) fine-tuned with multi-task learning on 560 tasks of the [tasksource collection](https://github.com/sileod/tasksource/)
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+ This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for:
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+ - Natural language inference
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+ - Zero-shot entailment-based classification pipeline (similar to bart-mnli), see [ZS].
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+ - Many other tasks with tasksource-adapters, see [TA]
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+ - Further fine-tune for new task (classification, token classification or multiple-choice).
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+ # [ZS] Zero-shot classification pipeline
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+ ```python
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+ from transformers import pipeline
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+ classifier = pipeline("zero-shot-classification",model="sileod/deberta-v3-base-tasksource-nli")
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+
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+ text = "one day I will see the world"
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+ candidate_labels = ['travel', 'cooking', 'dancing']
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+ classifier(sequence_to_classify, candidate_labels)
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+ ```
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+
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+ # [TA] Tasksource-adapters: 1 line access to more than 500 tasks
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  ```python
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  !pip install tasknet tasksource