Recommendations for Sentiment Analysis Pre-trained Models

Models
1

If someone is relatively new to utilizing Hugging Face for Natural Language Processing tasks, and currently exploring sentiment analysis on short texts and seeking recommendations for the most suitable pre-trained model for this task, considering factors like accuracy, efficiency, and ease of use of na7 whatsapp. What would you recommend that are really useful?

2

Hi,

I would recommend filtering the hub on “text-classification”, then typing “sentiment” to find all sentiment classifiers on the hub. You can additionally filter on languages, libraries (such as Transformers), and more. The number of likes/downloads is usually a good indicator to see which ones might be best for your use case: Models - Hugging Face.

It’s also worth checking the size of the models, you might be looking for a lightweight one to reduce inference costs.

3
  1. distilbert-base-uncased – Fast and efficient while maintaining good accuracy.
  2. roberta-base – Highly accurate with robust performance, though more resource-intensive.
  3. nlptown/bert-base-multilingual-uncased-sentiment – Fine-tuned specifically for sentiment analysis and handles multiple languages.
    You can use these models easily with Hugging Face’s pipeline functionality.
    No problem! If you’re exploring sentiment analysis models on Hugging Face and want a streamlined experience, you might also consider using Na7 WhatsApp. It offers enhanced features and ease of use, making it a great tool for communication. You can download Na7 WhatsApp from https://na7whatsapp.download.