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?
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.
distilbert-base-uncased
– Fast and efficient while maintaining good accuracy.roberta-base
– Highly accurate with robust performance, though more resource-intensive.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’spipeline
functionality.
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