Create README.md

README.md

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+---
+language: "nl"
+thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
+tags:
+- Dutch
+- Flemish
+- RoBERTa
+- RobBERT
+- RobBERTje
+license: mit
+datasets:
+- oscar
+- oscar (NL)
+- dbrd
+- lassy-ud
+- europarl-mono
+- conll2002
+widget:
+- text: "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."
+---
+
+<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](http://github.com/iPieter/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](http://github.com/iPieter/robbertje) for updates.
+
+# News
+- **July 2, 2021**: Publicly released 4 RobBERTje models.
+- **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) 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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://arxiv.org/abs/2101.05716) 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     |