Welcome to the Hugging Face Sentiment Analysis Pipeline repository! This repository contains a Jupyter Notebook that provides an in-depth explanation of how to use Hugging Face's powerful sentiment analysis model through their easy-to-use pipeline.

Sentiment analysis is a natural language processing (NLP) task that involves determining the sentiment or emotion expressed in a piece of text. Hugging Face provides pre-trained models and pipelines that make it simple for developers to integrate state-of-the-art NLP models into their applications.

In this repository, we focus on the sentiment analysis pipeline offered by Hugging Face. The Jupyter Notebook provides step-by-step guidance on:

• Setting up the environment
• Loading the sentiment analysis pipeline
• Analyzing sentiment in text data
• Understanding the output

To get started with the Hugging Face sentiment analysis pipeline, follow these steps:

1. Clone the Repository:

   git clone https://github.com/Shivap-17/Huggingface---pipeline-under-the-hood.git
   

2. Install Dependencies: Ensure you have the required dependencies installed. You can use a virtual environment or install the necessary packages globally:

   pip install -r requirements.txt

3. Open Jupyter Notebook: Launch the Jupyter Notebook and open the Hugging Face.ipynb file:

   jupyter notebook Hugging Face.ipynb

4. Follow the Notebook: The notebook is designed to guide you through each step of using the Hugging Face sentiment analysis pipeline. Execute each cell to understand the process and see the results.

Hugging Face's sentiment analysis pipeline makes it easy to perform sentiment analysis without the need for complex coding. The notebook covers:

• Loading the sentiment analysis model
• Using the pipeline for sentiment analysis
• Analyzing sentiment in custom text

• Hugging Face Transformers Documentation
• Hugging Face Model Hub

If you encounter any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request. Your feedback is highly appreciated!

Happy sentiment analyzing with Hugging Face! 🚀