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robbertje-1-gb-non-shuffled
DTAI-KULeuven/robbertje-1-gb-non-shuffled
robbertje-1-gb-non-shuffled is a fill-mask model from DTAI-KULeuven. Use it when you need the model to fill a missing word. It is set up for transformers. The card lists the license as mit.
<p align="center" <img src="https://github.com/iPieter/robbertje/raw/master/images/robbertjelogowithname.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%" </p
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At a glance
- Task
- Fill-Mask
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Nov 17, 2022
- SHA
- 5aa4e6ed
Try a prompt
Model card
From the Hugging Face model README
<p align="center">
<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%">
</p>
About RobBERTje
RobBERTje is a collection of distilled models based on RobBERT. There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch the repository for updates.
News
- February 21, 2022: Our paper about RobBERTje has been published in volume 11 of CLIN journal!
- July 2, 2021: Publicly released 4 RobBERTje models.
- May 12, 2021: RobBERTje was accepted at CLIN31 for an oral presentation!
The models
| Model | Description | Parameters | Training size | Huggingface id |
|---|---|---|---|---|
| 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 | this model |
| Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | DTAI-KULeuven/robbertje-1-gb-shuffled |
| 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 | DTAI-KULeuven/robbertje-1-gb-merged |
| 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 |
Results
Intrinsic results
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.
| Model | PPPL |
|---|---|
| RobBERT (teacher) | 7.76 |
| Non-shuffled | 12.95 |
| Shuffled | 18.74 |
| Merged (p=0.5) | 17.10 |
| BORT | 26.44 |
Extrinsic results
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 was also released and we evaluated our models with it as well.
| Model | DBRD | DIE-DAT | NER | POS | SICK-NL |
|---|---|---|---|---|---|
| RobBERT (teacher) | 94.4 | 99.2 | 89.1 | 96.4 | 84.2 |
| Non-shuffled | 90.2 | 98.4 | 82.9 | 95.5 | 83.4 |
| Shuffled | 92.5 | 98.2 | 82.7 | 95.6 | 83.4 |
| Merged (p=0.5) | 92.9 | 96.5 | 81.8 | 95.2 | 82.8 |
| BORT | 89.6 | 92.2 | 79.7 | 94.3 | 81.0 |
- Task
- Fill-Mask
- Library
- transformers
- Type
- roberta
- License
- mit
- Languages
- nl
- Created
- Mar 2, 2022
- Updated
- Nov 17, 2022