Update README.md

README.md

@@ -49,9 +49,11 @@ Than you need to initialize a model and a pipeline:
 ```python
 from gliclass import GLiClassModel, ZeroShotClassificationPipeline
 from transformers import AutoTokenizer
+
 model = GLiClassModel.from_pretrained("knowledgator/gliclass-modern-large-v2.0-init")
 tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-modern-large-v2.0-init")
 pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
+
 text = "One day I will see the world!"
 labels = ["travel", "dreams", "sport", "science", "politics"]
 results = pipeline(text, labels, threshold=0.5)[0] #because we have one text
@@ -59,6 +61,15 @@ for result in results:
  print(result["label"], "=>", result["score"])
 ```
 
+If you want to use it for NLI type of tasks, we recommend representing your premise as a text and hypothesis as a label, you can put several hypotheses, but the model works be
+```python
+# Initialize model and multi-label pipeline
+text = "The cat slept on the windowsill all afternoon"
+labels = ["The cat was awake and playing outside."]
+results = pipeline(text, labels, threshold=0.0)[0]
+print(results)
+```
+
 ### Benchmarks:
 Below, you can see the F1 score on several text classification datasets. All tested models were not fine-tuned on those datasets and were tested in a zero-shot setting.
 | Model                       | IMDB | AG_NEWS | Emotions |