Merge pull request #2419 from chrismoroney/cmoroney-nlp-last-reviewed-10-2025

NLP on Arm Update

Author: jasonrandrews

content/learning-paths/servers-and-cloud-computing/nlp-hugging-face/pytorch-nlp-hf.md

@@ -96,6 +96,8 @@ The output from this script should look like:
 3) positive 0.0477
 ```
 
+You should see three lines, each with a rank, sentiment label (negative / neutral / positive), and confidence score. The first line is the model’s strongest guess. The three probabilities should sum to 1. In this example, the model is confident the sentiment is negative.
+
 You have successfully performed sentiment analysis on the input text, all running on your Arm AArch64 CPU. You can change the input text in your example and re-run the classification example.
 
 Now that you have run the model, let's add the ability to profile the model execution. You can use the [PyTorch Profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html) to analyze the execution time on the CPU. Copy the contents shown below into a file named `sentiment-analysis-profile.py`:
@@ -177,7 +179,7 @@ Self CPU time total: 51.903ms
 2) neutral 0.2287
 3) positive 0.0477
 ```
-In addition to the classification output from the model, you can now see the execution time for the different operators. 
+In addition to the classification output from the model, you can now see the execution time for the different operators. The table shows how much time each operation takes on the CPU, both by itself and including any child operations. 
 
 You can experiment with the [BFloat16 floating-point number format](/install-guides/pytorch#bfloat16-floating-point-number-format) and [Transparent huge pages](/install-guides/pytorch#transparent-huge-pages) settings with PyTorch and see how that impacts the performance of your model.