
# Intro

OpenDiffusion's SentimentCheck is an AI model built upon Tensorflow+Keras+Pickles.  SentimentCheck harnesses the power of deep learning algorithms to accurately classify sentiment in text, making it a flexible tool for businesses, researchers, and developers.

## Usage
---
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%


{}
keras bert region:us
TAGS
#keras #bert #region-us 

Intro
=====


OpenDiffusion's SentimentCheck is an AI model built upon Tensorflow+Keras+Pickles. SentimentCheck harnesses the power of deep learning algorithms to accurately classify sentiment in text, making it a flexible tool for businesses, researchers, and developers.


Usage
-----




---


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:



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: 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%



TAGS
#keras #bert #region-us 
