
# bert-base-multilingual-uncased-sentiment

This is 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 fine-tuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages:

- Accuracy (exact) is the exact match for 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 

If you found this model useful, you can buy me a coffee at https://www.buymeacoffee.com/yvespeirsman.

In addition to this model, [NLP Town](http://nlp.town) offers custom models for many languages and NLP tasks.

Feel free to contact me for questions, feedback and/or requests for similar models.
{"language": ["en", "nl", "de", "fr", "it", "es"], "license": "mit"}
transformers pytorch tf jax bert text-classification en nl de fr it es doi:10.57967/hf/1515 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #jax #bert #text-classification #en #nl #de #fr #it #es #doi-10.57967/hf/1515 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

bert-base-multilingual-uncased-sentiment
========================================


This is 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:



Accuracy
--------


The fine-tuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages:


* Accuracy (exact) is the exact match for 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: English, Accuracy (exact): 67%, Accuracy (off-by-1): 95%
Language: Dutch, Accuracy (exact): 57%, Accuracy (off-by-1): 93%
Language: German, Accuracy (exact): 61%, Accuracy (off-by-1): 94%
Language: French, Accuracy (exact): 59%, Accuracy (off-by-1): 94%
Language: Italian, Accuracy (exact): 59%, Accuracy (off-by-1): 95%
Language: Spanish, Accuracy (exact): 58%, Accuracy (off-by-1): 95%


Contact
-------


If you found this model useful, you can buy me a coffee at URL


In addition to this model, NLP Town offers custom models for many languages and NLP tasks.


Feel free to contact me for questions, feedback and/or requests for similar models.



TAGS
#transformers #pytorch #tf #jax #bert #text-classification #en #nl #de #fr #it #es #doi-10.57967/hf/1515 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #tf #jax #bert #text-classification #en #nl #de #fr #it #es #doi-10.57967/hf/1515 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
