Update README.md

README.md

@@ -319,6 +319,7 @@ print("Prediction:", pred_class)
 ### Example 3: Text–Text Inference (Ported SONAR)
 
 ```python
+# !pip install transformers sentencepiece  torch -q
 import torch
 from transformers import AutoTokenizer, AutoModel
 from transformers.models.m2m_100.modeling_m2m_100 import M2M100Encoder
@@ -365,7 +366,7 @@ print("Prediction:", pred_class)
 You can use the BLASER semantic score in combination with the MMNLI NLI class to get a **better understanding of the relationship** between source and candidate translations. The NLI class gives the entailment/contradiction/neutral label, while the BLASER score provides a fine-grained semantic similarity.
 
 ```python
-
+# !pip install transformers sentencepiece  torch -q
 import torch
 from transformers import AutoTokenizer, AutoModel
 from transformers.models.m2m_100.modeling_m2m_100 import M2M100Encoder