This is a multi-head transformer regression model based on GroNLP/bert-base-dutch-cased, fine-tuned to predict four separate text quality scores for Dutch texts.
The final aggregate metric re-computes a combined score from the four heads and compares it to the actual aggregate.

📈 Training & Evaluation

Epoch	Loss (Train)	Loss (Val)	RMSE (delta_cola)	R² (delta_cola)	RMSE (delta_perplexity)	R² (delta_perplexity)	RMSE (iter_to_final)	R² (iter_to_final)	RMSE (robbert_delta_blurb)	R² (robbert_delta_blurb)	Mean RMSE
1	0.0185	0.0152	0.1436	0.4447	0.1062	0.6066	0.1269	0.8500	0.1138	0.7446	0.1226
2	0.0141	0.0145	0.1400	0.4722	0.0988	0.6590	0.1231	0.8587	0.1156	0.7364	0.1194
3	0.0115	0.0146	0.1409	0.4656	0.0991	0.6571	0.1253	0.8537	0.1135	0.7458	0.1197
4	0.0094	0.0154	0.1468	0.4197	0.0985	0.6613	0.1297	0.8433	0.1164	0.7327	0.1228
5	0.0079	0.0154	0.1462	0.4246	0.1009	0.6444	0.1276	0.8482	0.1172	0.7291	0.1230

Final aggregate performance:

Aggregate RMSE: 0.0845
Aggregate R²: 0.8146

🧾 Notes

This model uses four regression heads for: delta_cola_to_final, delta_perplexity_to_final_large, iter_to_final_simplified, and robbert_delta_blurb_to_final.
The final performance aggregates the individual predictions back into a combined quality score for more robust quality measurement.
Based on the Dutch BERT (GroNLP/bert-base-dutch-cased).

Felixbrk/bert-base-dutch-cased-multi-score-text-only

transformer_multi_head

📈 Training & Evaluation

🧾 Notes

Felixbrk/
bert-base-dutch-cased-multi-score-text-only