Update README.md

README.md

@@ -96,4 +96,26 @@ Output:
   array([0.11633301, 3.63671875, 4.203125, 5.30859375]),
   array([0.11633301, 3.63671875, 4.203125, 5.30859375])
 )
-```
+```
+## Large scale use
+
+[Bojan](https://huggingface.co/Bojan) tested the example above on a large dataset. He reports execution time can be improved by a factor of five with the use of `transformers` as follows:
+
+```python
+
+from transformers import AutoModelForSequenceClassification, TextClassificationPipeline, AutoTokenizer,                                                                                               AutoConfig
+
+MODEL = "classla/xlm-r-parlasent"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+config = AutoConfig.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True,
+                                      task='sentiment_analysis', device=0, function_to_apply="none")
+pipe([
+    "I fully disagree with this argument.", 
+    "The ministers are entering the chamber.",
+    "Things can always be improved in the future.",
+    "These are great news."
+])
+```