Update README.md

README.md

@@ -319,7 +319,8 @@ pipeline_tag: zero-shot-classification
 This is [DeBERTa-v3-base](https://hf.co/microsoft/deberta-v3-base) fine-tuned with multi-task learning on 600 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 entailment-based classification pipeline (similar to bart-mnli), see [ZS].
-- Natural language inference, and many other tasks with tasksource-adapters, see [TA].
+- Natural language inference [NLI]
+- Hundreds of other tasks with tasksource-adapters, see [TA].
 - Further fine-tuning with a new task or tasksource task (classification, token classification or multiple-choice) [FT].
 
 # [ZS] Zero-shot classification pipeline
@@ -333,6 +334,15 @@ classifier(text, candidate_labels)
 ```
 NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.
 
+# [NLI] Natural language inference pipeline
+
+```python
+from transformers import pipeline
+nli_pipe = pipeline("text-classification",model="sileod/deberta-v3-base-tasksource-nli")
+nli_pipe([dict(text="there is a cat",text_pair="there is a black cat")])
+# [{'label': 'neutral', 'score': 0.9952911138534546}]
+```
+
 # [TA] Tasksource-adapters: 1 line access to hundreds of tasks 
 
 ```python