changed model to TinySentiment

Author: dominica-of

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md

@@ -1,16 +1,15 @@
 ---
-title: Deploying DistilBERT on Arm - Training and Inference with PyTorch and ExecuTorch
+title: Deploy a Tiny Sentiment Classifier on Arm - Training and Inference with PyTorch and ExecuTorch
 
 minutes_to_complete: 120
 
 who_is_this_for: This topic is for machine learning engineers, embedded AI developers, and researchers interested in deploying TinyML models for NLP on Arm-based edge devices using PyTorch and ExecuTorch. 
 
 learning_objectives: 
-    - Fine-tune a DistilBERT model for sentiment analysis using PyTorch.
+    - Train a custom CNN-based sentiment classifier for TinyML.
     - Optimize and convert the model using ExecuTorch for Arm-based edge devices.
     - Deploy and run inference on the Corstone-320 FVP and Raspberry Pi 5.
 
-
 prerequisites:
    - Basic knowledge of machine learning concepts. 
    - It is advised to complete The Learning Path [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm) before starting this learning path. 
@@ -28,7 +27,7 @@ armips:
     - Cortex-A
 tools_software_languages:
     - tinyML 
-    - Transformers 
+    - CNN 
     - PyTorch
     - ExecuTorch
     - Raspberry Pi

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/env-setup-1.md

@@ -7,9 +7,10 @@ layout: learningpathall
 ---
 
 ## Overview 
-#TODO: Add intro on Distil
+In this course, you will learn how to train and run inference using a Tiny Sentiment Classifier. You'll deploy the model on the Arm Corstone-320 FVP and optionally on a Raspberry Pi 5 for sentiment analysis. You'll gain hands-on experience with PyTorch, ExecuTorch, and Edge Impulse to bring AI-powered sentiment analysis to embedded systems.
+
+We will train a lightweight convolutional neural network (CNN)-based sentiment classifier using synthetic text data. This model is optimized for small devices, using embedding layers and 1D convolutions for efficient text classification.
 
-In this course, you will learn how to train and run inference using DistilBERT. You'll deploy the model on the Arm Corstone-320 FVP and optionally on a Raspberry Pi 5 for sentiment analysis.
 
 ## Environment Setup
 Setup your development environment for TinyML by following the first 3 chapters of the [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm) Learning Path (LP).
@@ -25,8 +26,8 @@ The prompt of your terminal now has `(executorch)` as a prefix to indicate the v
 Run the commands below to install the dependencies.
 
 ```bash
-pip install transformers datasets torch
+pip install argparse json
 ```
-You are now ready to fine-tune the model
+You are now ready to build the model