Update README.md

README.md

@@ -6,7 +6,7 @@ base_model:
 
 # Model Card for Model ID
 
-This model is part of a series of models trained for the ML4AL paper “Gotta catch ‘em all!”: Retrieving people in Ancient Greek texts combining transformer models and domain knowledge written in the context of the KU Leuven ID-N project NIKAW (Networks of Ideas and Knowledge in the Ancient World)
+This model is part of a series of models trained for the ML4AL paper “Gotta catch ‘em all!”: Retrieving people in Ancient Greek texts combining transformer models and domain knowledge", written in the context of the KU Leuven ID-N project NIKAW (Networks of Ideas and Knowledge in the Ancient World)
 
 ## Model Details
 
@@ -26,10 +26,26 @@ This model is part of a series of models trained for the ML4AL paper “Gotta ca
 
 ### Training Data
 
-**Repository:** [https://github.com/NER-AncientLanguages/NERAncientGreekML4AL] (for data and training scripts)
+**Repository:** [https://github.com/NER-AncientLanguages/NERAncientGreekML4AL] (for data and training scripts). We thank the following projects for helping to provide the training data:
+
 
 ### Training Hyperparameters
 
+We use Weights & Biases for hyperparameter optimization with a random search strategy (10 folds), aiming to maximize the evaluation F1 score (eval_f1). 
+
+The search space includes:
+
+Learning Rate: Sampled uniformly between 1e-6 and 1e-4
+Weight Decay: One of [0.1, 0.01, 0.001]
+Number of Training Epochs: One of [3, 4, 5, 6]
+
+For the final training of this model, the hyperparameters were:
+Learning Rate: 9.889410158465026e-05
+Weight Decay: 0.1
+Number of Training Epochs: 5
+
+
+
 ## Evaluation
 
 This models was evaluation on precision, recall and macro-f1 for its entity classes. See the paper for more information.
@@ -48,6 +64,9 @@ This models was evaluation on precision, recall and macro-f1 for its entity clas
 
 If you use this work, please cite the following paper:
 
+### **APA:**
+Beersmans, M., Keersmaekers, A., de Graaf, E., Van de Cruys, T., Depauw, M., & Fantoli, M. (2024). “Gotta catch `em all!”: Retrieving people in Ancient Greek texts combining transformer models and domain knowledge. In J. Pavlopoulos, T. Sommerschield, Y. Assael, S. Gordin, K. Cho, M. Passarotti, R. Sprugnoli, Y. Liu, B. Li, & A. Anderson (Eds.), Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024) (pp. 152–164). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.ml4al-1.16
+
 ### **BibTeX**
 ```bibtex
 @inproceedings{Beersmans_Keersmaekers_de Graaf_Van de Cruys_Depauw_Fantoli_2024,
@@ -65,6 +84,3 @@ If you use this work, please cite the following paper:
   pages = {152--164}
 }
 
-
-**APA:**
-Beersmans, M., Keersmaekers, A., de Graaf, E., Van de Cruys, T., Depauw, M., & Fantoli, M. (2024). “Gotta catch `em all!”: Retrieving people in Ancient Greek texts combining transformer models and domain knowledge. In J. Pavlopoulos, T. Sommerschield, Y. Assael, S. Gordin, K. Cho, M. Passarotti, R. Sprugnoli, Y. Liu, B. Li, & A. Anderson (Eds.), Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024) (pp. 152–164). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.ml4al-1.16