Update README.md

README.md

@@ -3,11 +3,33 @@ license: mit
 language:
 - de
 ---
-It is a pre-trained German science education BERT for written German science assessments.
+## G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German
+This model developed a contextualized German Science Education BERT (G-SciEdBERT),
+an innovative large language model tailored for scoring German-written responses to science tasks.
+Using G-BERT, we pre-trained G-SciEdBERT on a corpus of 50K German written science responses with 5M tokens to the Programme for International Student Assessment (PISA) 2015.
+We fine-tuned G-SciEdBERT on 59 assessment items and examined the scoring accuracy. We then compared its performance with G-BERT.
+Our findings reveal a substantial improvement in scoring accuracy with G-SciEdBERT, demonstrating a 10% increase of quadratic weighted kappa compared to G-BERT
+(mean accuracy difference = 0.096, SD = 0.024). These insights underline the significance of specialized language models like G-SciEdBERT,
+which is trained to enhance the accuracy of automated scoring, offering a substantial contribution to the field of AI in education.
+
+## Dataset
+It is a pre-trained German science education BERT for written German science assessments of PISA test.
+PISA is an international test to monitor education trends led by OECD (Organisation for Economic Co-operation and Development).
+PISA items are developed to assess scientific literacy, highlighting real-world problem-solving skills and the need of future workforce.
+This study analyzed data collected for 59 construct response science assessment items in German at the middle school level. 
+A total of 6,116 German students from 257 schools participated in PISA 2015.
+Given the geographical diversity of participants, PISA data reflect the general German students' science literacy.
+
+The PISA items selected require either short (around one sentence) or extended (up to five sentences) responses.
+The minimum score for all items is 0, with the maximum being 3 or 4 for short responses and 4 or 5 for extended responses.
+Student responses have 20 words on average. Our pre-training dataset contains more than 50,000 student-written German responses,
+which means approximately 1,000 human-scored student responses per item for contextual learning through fine-tuning.
+More than 10 human raters scored each response in the training dataset organized by OECD.
+The responses were graded irrespective of the student's ethnicity, race, or gender to ensure fairness.
 
 ## Usage
 
-With Transformers >= 2.3 our German BERT models can be loaded like:
+With Transformers >= 2.3 our German BERT models can be loaded like this:
 
 ```python
 from transformers import AutoModel, AutoTokenizer
@@ -17,4 +39,18 @@ model = AutoModel.from_pretrained("ai4stem-uga/G-SciEdBERT")
 ```
 
 # Acknowledgments
-This project is supported by the Alexender von Humboldt Foundation (PI Xiaoming Zhai, xiaoming.zhai@uga.edu).
+This project is supported by the Alexender von Humboldt Foundation (PI Xiaoming Zhai, xiaoming.zhai@uga.edu).
+
+## Citation
+
+```bibtex
+    @InProceedings{Latif_2024_G-SciEdBERT,
+        author    = {Latif, Ehsan and Lee, Gyeong-Geon and Neuman, Knut and Kastorff, Tamara and Zhai, Xiaoming},
+        title     = {G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German},
+        journal   = {arXiv preprint arXiv:2301.12031},
+        year      = {2024}
+        pages     = {1-9}
+    }
+```
+
+*This model is trained and shared by Ehsan Latif, Ph.D (ehsan.latif@uga.edu)