videberta-base-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov

This model is a fine-tuned version of Fsoft-AIC/videberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0921
• Accuracy: 0.9816
• F1: 0.0426
• Precision: 0.25
• Recall: 0.0233

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2.5e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	250	0.0910	0.9825	0.0	0.0	0.0
0.1567	2.0	500	0.0949	0.9825	0.0	0.0	0.0
0.1567	3.0	750	0.0959	0.9825	0.0	0.0	0.0
0.0772	4.0	1000	0.0962	0.9825	0.0	0.0	0.0
0.0772	5.0	1250	0.0975	0.9825	0.0	0.0	0.0
0.0767	6.0	1500	0.0969	0.9825	0.0	0.0	0.0
0.0767	7.0	1750	0.0984	0.9825	0.0	0.0	0.0
0.0758	8.0	2000	0.0966	0.9825	0.0	0.0	0.0
0.0758	9.0	2250	0.0960	0.9825	0.0	0.0	0.0
0.0739	10.0	2500	0.0955	0.9825	0.0	0.0	0.0
0.0739	11.0	2750	0.0958	0.9825	0.0	0.0	0.0
0.0711	12.0	3000	0.0940	0.9825	0.0	0.0	0.0
0.0711	13.0	3250	0.0942	0.9820	0.0	0.0	0.0
0.0672	14.0	3500	0.0958	0.9825	0.0	0.0	0.0
0.0672	15.0	3750	0.0943	0.9825	0.0851	0.5	0.0465
0.0639	16.0	4000	0.0926	0.9829	0.0455	1.0	0.0233
0.0639	17.0	4250	0.0964	0.9820	0.0435	0.3333	0.0233
0.0611	18.0	4500	0.0970	0.9820	0.0435	0.3333	0.0233
0.0611	19.0	4750	0.0969	0.9825	0.0444	0.5	0.0233
0.058	20.0	5000	0.0952	0.9820	0.0435	0.3333	0.0233
0.058	21.0	5250	0.0950	0.9820	0.0435	0.3333	0.0233
0.0547	22.0	5500	0.0954	0.9816	0.0426	0.25	0.0233
0.0547	23.0	5750	0.0963	0.9816	0.0426	0.25	0.0233
0.0525	24.0	6000	0.0946	0.9820	0.0435	0.3333	0.0233
0.0525	25.0	6250	0.0942	0.9820	0.0435	0.3333	0.0233
0.0502	26.0	6500	0.0909	0.9825	0.0444	0.5	0.0233
0.0502	27.0	6750	0.0958	0.9816	0.0426	0.25	0.0233
0.048	28.0	7000	0.0934	0.9808	0.0408	0.1667	0.0233
0.048	29.0	7250	0.0946	0.9804	0.04	0.1429	0.0233
0.0458	30.0	7500	0.0938	0.9808	0.0408	0.1667	0.0233
0.0458	31.0	7750	0.0913	0.9829	0.0870	0.6667	0.0465
0.044	32.0	8000	0.0913	0.9816	0.0426	0.25	0.0233
0.044	33.0	8250	0.0915	0.9808	0.0408	0.1667	0.0233
0.0427	34.0	8500	0.0924	0.9808	0.0408	0.1667	0.0233
0.0427	35.0	8750	0.0912	0.9812	0.0417	0.2	0.0233
0.041	36.0	9000	0.0922	0.9808	0.0408	0.1667	0.0233
0.041	37.0	9250	0.0933	0.9812	0.0417	0.2	0.0233
0.0404	38.0	9500	0.0929	0.9812	0.0417	0.2	0.0233
0.0404	39.0	9750	0.0922	0.9816	0.0426	0.25	0.0233
0.0401	40.0	10000	0.0921	0.9816	0.0426	0.25	0.0233

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.1.0
• Tokenizers 0.19.1