Update README.md

README.md

@@ -1,3 +1,71 @@
 ---
+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"
+}
+```