🔍 Explore pre-trained models and tools for sentiment analysis using Hugging Face's Transformers library.

📚 Overview

Hugging Face provides 🚀 powerful NLP models for sentiment analysis, including:

  • BERT-based models for fine-tuned classification
  • DistilBERT for lightweight performance
  • RoBERTa for advanced contextual understanding

For a quick start, check out our Transformers documentation 📚.

🛠️ How to Use

  1. Install the library 
    pip install transformers
  2. Load a pre-trained model 
    from transformers import pipeline  
sentiment_pipeline = pipeline("sentiment-analysis")
  3. Analyze text 
    result = sentiment_pipeline("I love programming!")  
print(result)  # Output: [{'label': 'POSITIVE', 'score': 0.999...}]

📌 Model Examples

Model Name Task Accuracy
distilbert-base-uncased-finetuned-sst-2 Sentiment Classification 95.2%
bert-base-uncased Multi-label Sentiment 93.8%
HuggingFace_sentiment_analysis

🌐 Extend Your Knowledge

Dive deeper into NLP model training guides or explore model benchmarks for performance comparisons.

For visual learners, try this interactive demo 🧪!