bert-base-multilingual-uncased-sentiment
Maintainer: nlptown
258
🏷️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The bert-base-multilingual-uncased-sentiment model is a BERT-based model that has been fine-tuned for sentiment analysis on product reviews across six languages: English, Dutch, German, French, Spanish, and Italian. This model can predict the sentiment of a review as a number of stars (between 1 and 5). It was developed by NLP Town, a provider of custom language models for various tasks and languages.
Similar models include the twitter-XLM-roBERTa-base-sentiment model, which is a multilingual XLM-roBERTa model fine-tuned for sentiment analysis on tweets, and the sentiment-roberta-large-english model, which is a fine-tuned RoBERTa-large model for sentiment analysis in English.
Model inputs and outputs
Inputs
- Text: The model takes product review text as input, which can be in any of the six supported languages (English, Dutch, German, French, Spanish, Italian).
Outputs
- Sentiment score: The model outputs a sentiment score, which is an integer between 1 and 5 representing the number of stars the model predicts for the input review.
Capabilities
The bert-base-multilingual-uncased-sentiment model is capable of accurately predicting the sentiment of product reviews across multiple languages. For example, it can correctly identify a positive review like "This product is amazing!" as a 5-star review, or a negative review like "This product is terrible" as a 1-star review.
What can I use it for?
You can use this model for sentiment analysis on product reviews in any of the six supported languages. This could be useful for e-commerce companies, review platforms, or anyone interested in analyzing customer sentiment. The model could be used to automatically aggregate and analyze reviews, detect trends, or surface particularly positive or negative feedback.
Things to try
One interesting thing to try with this model is to experiment with reviews that contain a mix of languages. Since the model is multilingual, it may be able to correctly identify the sentiment even when the review contains words or phrases in multiple languages. You could also try fine-tuning the model further on a specific domain or language to see if you can improve the accuracy for your particular use case.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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