readme: ---
  language:
  - en
  - nl
  - de
  - fr
  - it
  - es
  
  license: mit
  ---
  
  # bert-base-multilingual-uncased-sentiment
  
  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).
  
  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.
  
  ## Training data
  
  Here is the number of product reviews we used for finetuning the model: 
  
  | Language | Number of reviews |
  | -------- | ----------------- |
  | English  | 150k           |
  | Dutch    | 80k            |
  | German   | 137k           |
  | French   | 140k           |
  | Italian  | 72k            |
  | Spanish  | 50k            |
  
  ## Accuracy
  
  The finetuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages:
  
  - Accuracy (exact) is the exact match on the number of stars.
  - 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. 
  
  
  | Language | Accuracy (exact) | Accuracy (off-by-1) |
  | -------- | ---------------------- | ------------------- |
  | English  | 67%                 | 95%
  | Dutch    | 57%                 | 93%
  | German   | 61%                 | 94%
  | French   | 59%                 | 94%
  | Italian  | 59%                 | 95%
  | Spanish  | 58%                 | 95%
  
  ## Contact 
  
  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/). 
  
  Feel free to contact us for questions, feedback and/or requests for similar models.