Project 3: Customer Feedback Analysis Using Sentiment Analysis
3. Step 1: Setting Up the Environment
Before diving into the implementation, it's crucial to set up your development environment correctly. You'll need to ensure your system has Python installed (preferably version 3.7 or higher) and that your environment is properly configured with all the necessary libraries and dependencies.
This includes the PyTorch deep learning framework for neural network operations, the Transformers library from Hugging Face for working with BERT models, and the Datasets library for efficient data handling. Additionally, you may want to consider setting up a virtual environment to isolate your project dependencies and avoid conflicts with other Python projects.
Install Required Libraries
!pip install transformers torch datasets
3. Step 1: Setting Up the Environment
Before diving into the implementation, it's crucial to set up your development environment correctly. You'll need to ensure your system has Python installed (preferably version 3.7 or higher) and that your environment is properly configured with all the necessary libraries and dependencies.
This includes the PyTorch deep learning framework for neural network operations, the Transformers library from Hugging Face for working with BERT models, and the Datasets library for efficient data handling. Additionally, you may want to consider setting up a virtual environment to isolate your project dependencies and avoid conflicts with other Python projects.
Install Required Libraries
!pip install transformers torch datasets
3. Step 1: Setting Up the Environment
Before diving into the implementation, it's crucial to set up your development environment correctly. You'll need to ensure your system has Python installed (preferably version 3.7 or higher) and that your environment is properly configured with all the necessary libraries and dependencies.
This includes the PyTorch deep learning framework for neural network operations, the Transformers library from Hugging Face for working with BERT models, and the Datasets library for efficient data handling. Additionally, you may want to consider setting up a virtual environment to isolate your project dependencies and avoid conflicts with other Python projects.
Install Required Libraries
!pip install transformers torch datasets
3. Step 1: Setting Up the Environment
Before diving into the implementation, it's crucial to set up your development environment correctly. You'll need to ensure your system has Python installed (preferably version 3.7 or higher) and that your environment is properly configured with all the necessary libraries and dependencies.
This includes the PyTorch deep learning framework for neural network operations, the Transformers library from Hugging Face for working with BERT models, and the Datasets library for efficient data handling. Additionally, you may want to consider setting up a virtual environment to isolate your project dependencies and avoid conflicts with other Python projects.
Install Required Libraries
!pip install transformers torch datasets