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--- |
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language: "nl" |
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thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png" |
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tags: |
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- Dutch |
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- Flemish |
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- RoBERTa |
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- RobBERT |
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- RobBERTje |
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license: mit |
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datasets: |
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- oscar |
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- oscar (NL) |
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- dbrd |
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- lassy-ud |
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- europarl-mono |
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- conll2002 |
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widget: |
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- text: "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven." |
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--- |
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<p align="center"> |
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<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%"> |
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</p> |
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# About RobBERTje |
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RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case. |
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We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates. |
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# News |
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- **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)! |
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- **July 2, 2021**: Publicly released 4 RobBERTje models. |
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- **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation! |
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# The models |
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| Model | Description | Parameters | Training size | Huggingface id | |
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|--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------| |
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| Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) | |
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| Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-shuffled) | |
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| Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | this model | |
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| BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) | |
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# Results |
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## Intrinsic results |
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We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution. |
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| Model | PPPL | |
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|-------------------|-----------| |
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| RobBERT (teacher) | 7.76 | |
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| Non-shuffled | 12.95 | |
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| Shuffled | 18.74 | |
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| Merged (p=0.5) | 17.10 | |
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| BORT | 26.44 | |
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## Extrinsic results |
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We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well. |
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| Model | DBRD | DIE-DAT | NER | POS |SICK-NL | |
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|------------------|-----------|-----------|-----------|-----------|----------| |
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| RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 | |
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| Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 | |
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| Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 | |
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| Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 | |
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| BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 | |
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