metadata

tags:
  - generated_from_trainer
model-index:
  - name: icdar23-entrydetector_texttokens_breaks_indents_left_diff_right_ref
    results: []

icdar23-entrydetector_texttokens_breaks_indents_left_diff_right_ref

This model is a fine-tuned version of HueyNemud/das22-10-camembert_pretrained on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0448
Ebegin: {'precision': 0.9843225083986562, 'recall': 0.9788418708240535, 'f1': 0.9815745393634842, 'number': 2694}
Eend: {'precision': 0.9872036130974784, 'recall': 0.9707623982235382, 'f1': 0.9789139764881508, 'number': 2702}
Overall Precision: 0.9858
Overall Recall: 0.9748
Overall F1: 0.9802
Overall Accuracy: 0.9860

Model description

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.07	300	0.0868	0.9708	0.9867	0.9787	0.9858
0.31	0.14	600	0.0805	0.9890	0.9606	0.9746	0.9834
0.31	0.21	900	0.0758	0.9793	0.9340	0.9561	0.9733
0.1178	0.29	1200	0.0434	0.9845	0.9808	0.9826	0.9885
0.1413	0.36	1500	0.0635	0.9909	0.9687	0.9796	0.9867
0.1413	0.43	1800	0.0355	0.9848	0.9839	0.9844	0.9907
0.1699	0.5	2100	0.0327	0.9914	0.9843	0.9879	0.9920
0.1699	0.57	2400	0.0330	0.9904	0.9832	0.9868	0.9913
0.144	0.64	2700	0.0285	0.9840	0.9891	0.9865	0.9911
0.0958	0.72	3000	0.0264	0.9922	0.9836	0.9879	0.9920
0.0958	0.79	3300	0.0312	0.9912	0.9852	0.9882	0.9922
0.0585	0.86	3600	0.0296	0.9893	0.9862	0.9878	0.9919
0.0585	0.93	3900	0.0259	0.9864	0.9899	0.9881	0.9922
0.0478	1.0	4200	0.0314	0.9933	0.9649	0.9789	0.9862
0.0842	1.07	4500	0.0222	0.9887	0.9897	0.9892	0.9928
0.0842	1.14	4800	0.0189	0.9925	0.9883	0.9904	0.9937
0.075	1.22	5100	0.0241	0.9890	0.9898	0.9894	0.9930
0.075	1.29	5400	0.0242	0.9915	0.9854	0.9884	0.9924
0.0511	1.36	5700	0.0197	0.9929	0.9885	0.9907	0.9939
0.042	1.43	6000	0.0223	0.9936	0.9852	0.9894	0.9930
0.042	1.5	6300	0.0203	0.9899	0.9905	0.9902	0.9935
0.0596	1.57	6600	0.0215	0.9892	0.9914	0.9903	0.9936
0.0596	1.65	6900	0.0211	0.9922	0.9875	0.9898	0.9933
0.0489	1.72	7200	0.0212	0.9923	0.9869	0.9896	0.9931