sentiment
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by
VaibhavGp69
- opened
README.md
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# bert-base-multilingual-uncased-sentiment
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This
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This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above or for further finetuning on related sentiment analysis tasks.
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## Training data
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## Accuracy
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The
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- Accuracy (exact) is the exact match
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- Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer.
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## Contact
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Feel free to contact me for questions, feedback and/or requests for similar models.
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# bert-base-multilingual-uncased-sentiment
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This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).
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This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above, or for further finetuning on related sentiment analysis tasks.
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## Training data
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## Accuracy
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The finetuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages:
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- Accuracy (exact) is the exact match on the number of stars.
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- Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer.
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## Contact
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In addition to this model, [NLP Town](https://www.nlp.town) offers custom, monolingual sentiment models for many languages and an improved multilingual model through [RapidAPI](https://rapidapi.com/nlp-town-nlp-town-default/api/multilingual-sentiment-analysis2/).
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Feel free to contact us for questions, feedback and/or requests for similar models.
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