---
language: nl
license: mit
datasets:
- dbrd
model-index:
- name: robbert-v2-dutch-sentiment  Copied
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: dbrd
      type: sentiment-analysis
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.93325
widget:
- text: "Ik erken dat dit een boek is, daarmee is alles gezegd."
- text: "Prachtig verhaal, heel mooi verteld en een verrassend einde... Een topper!"
thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
tags:
- Dutch
- Flemish
- RoBERTa
- RobBERT
---

<p align="center"> 
    <img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo_with_name.png" alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
 </p>

# RobBERT finetuned for sentiment analysis on DBRD

This is a finetuned model based on [RobBERT (v2)](https://huggingface.co/pdelobelle/robbert-v2-dutch-base). We used [DBRD](https://huggingface.co/datasets/dbrd), which consists of book reviews from [hebban.nl](hebban.nl). Hence our example sentences about books. We did some limited experiments to test if this also works for other domains, but this was not 

# Training data and setup
We used the [Dutch Book Reviews Dataset (DBRD)](https://huggingface.co/datasets/dbrd) from van der Burgh et al. (2019).
Originally, these reviews got a five-star rating, but this has been converted to positive (⭐️⭐️⭐️⭐️ and ⭐️⭐️⭐️⭐️⭐️), neutral (⭐️⭐️⭐️) and negative (⭐️ and ⭐️⭐️). 
We used 19.5k reviews for the training set, 528 reviews for the validation set and 2224 to calculate the final accuracy.

The validation set was used to evaluate a random hyperparameter search over the learning rate, weight decay and gradient accumulation steps. 
The full training details are available in [`training_args.bin`](https://huggingface.co/DTAI-KULeuven/robbert-v2-dutch-sentiment/blob/main/training_args.bin) as a binary PyTorch file. 

# Limitations and biases
- The domain of the reviews is limited to book reviews.
- Most authors of the book reviews were women, which could have caused [a difference in performance for reviews written by men and women](https://www.aclweb.org/anthology/2020.findings-emnlp.292). 

## Credits and citation

This project is created by [Pieter Delobelle](https://people.cs.kuleuven.be/~pieter.delobelle), [Thomas Winters](https://thomaswinters.be) and [Bettina Berendt](https://people.cs.kuleuven.be/~bettina.berendt/).
If you would like to cite our paper or models, you can use the following BibTeX:

```
@inproceedings{delobelle2020robbert,
    title = "{R}ob{BERT}: a {D}utch {R}o{BERT}a-based {L}anguage {M}odel",
    author = "Delobelle, Pieter  and
      Winters, Thomas  and
      Berendt, Bettina",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.292",
    doi = "10.18653/v1/2020.findings-emnlp.292",
    pages = "3255--3265"
}
```