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  # RobBERT finetuned for sentiment analysis on DBRD
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- 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
 
 
 
 
 
 
 
 
 
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  # Training data and setup
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  We used the [Dutch Book Reviews Dataset (DBRD)](https://huggingface.co/datasets/dbrd) from van der Burgh et al. (2019).
 
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  # RobBERT finetuned for sentiment analysis on DBRD
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+ 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 exactly amazing.
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+ We released a distilled model and a `base`-sized model. Both models perform quite well, so there is only a slight performance tradeoff:
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+ | Model | Identifier | #Params. | Accuracy |
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+ |----------------|------------------------------------------------------------------------|-----------|-----------|
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+ | RobBERT (v2) | [`DTAI-KULeuven/robbert-v2-dutch-sentiment`](https://huggingface.co/DTAI-KULeuven/robbert-v2-dutch-sentiment) | 110 M |93.3* |
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+ | Merged (p=0.5)| [`DTAI-KULeuven/robbertje-merged-dutch-sentiment`](https://huggingface.co/DTAI-KULeuven/robbertje-merged-dutch-sentiment) | 74 M |92.9 |
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+ *The results of RobBERT are of a different run than the one reported in the paper.
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  # Training data and setup
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  We used the [Dutch Book Reviews Dataset (DBRD)](https://huggingface.co/datasets/dbrd) from van der Burgh et al. (2019).