Update README.md

README.md

@@ -270,10 +270,23 @@ pipeline_tag: zero-shot-classification
 # Model Card for DeBERTa-v3-base-tasksource-nli
 
 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/)
-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).
-You can also load other tasks (see next paragraph) or further fine-tune the encoder for new task (classification, token classification or multiple-choice).
+This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for:
+- Natural language inference
+- Zero-shot entailment-based classification pipeline (similar to bart-mnli), see [ZS].
+- Many other tasks with tasksource-adapters, see [TA]
+- Further fine-tune for new task (classification, token classification or multiple-choice).
 
-# Tasksource-adapters: 1 line access to more than 500 tasks 
+# [ZS] Zero-shot classification pipeline
+```python
+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(sequence_to_classify, candidate_labels)
+```
+
+# [TA] Tasksource-adapters: 1 line access to more than 500 tasks 
 
 ```python
 !pip install tasknet tasksource