README.md

metadata

title: Instant Sentiment Analysis
emoji: 🤗
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.29.0
app_file: app.py
pinned: false
license: apache-2.0

Instant Sentiment Analysis with bertweet-base

This Hugging Face Space hosts an interactive demo for a sentiment analysis pipeline. It uses the pre-trained finiteautomata/bertweet-base-sentiment-analysis model, which is fine-tuned on tweets, making it highly effective at understanding informal language, slang, and social media content.

This project serves as a foundational example of how to wrap a powerful, pre-trained model from the Hugging Face Hub into an interactive web application using Gradio.

🚀 How to Use the Demo

1. Enter Text: Type or paste any text into the input box.
2. See the Results: The model will instantly analyze the text and display the sentiment scores for "Positive" (POS), "Negative" (NEG), and "Neutral" (NEU).
3. Try the Examples: Click on one of the provided examples to see how the model performs on different types of sentences.

🤖 Model Details

The underlying model, bertweet-base-sentiment-analysis, is a RoBERTa-base model trained on a massive corpus of English tweets. This specific training makes it robust for analyzing sentiment in noisy, real-world text commonly found on the internet.

• Labels:
  • POS: Positive sentiment
  • NEU: Neutral sentiment
  • NEG: Negative sentiment

🛠️ Project Purpose & Portfolio Value

This project was developed to showcase several key skills in the modern AI landscape:

• Leveraging Pre-trained Models: Demonstrates the ability to identify and utilize state-of-the-art models from the Hugging Face Hub for a specific task.
• GPU-Accelerated Inference: The backend automatically uses a GPU if available, showcasing an understanding of hardware acceleration for ML models.
• Interactive Application Development: Uses Gradio to build a clean, user-friendly interface, a critical skill for making AI models accessible.
• Reproducibility: The project is fully reproducible thanks to the requirements.txt file, a best practice for collaborative and professional development.

This Space is a tangible demonstration of applying advanced NLP tools to create a practical, real-world application.