pranaydeeps/lettuce_pos_fr_xlm

pos_final_xlm_fr

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1022
• Precision: 0.9744
• Recall: 0.9746
• F1: 0.9745
• Accuracy: 0.9769

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: 5e-05
• train_batch_size: 256
• eval_batch_size: 256
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 1024
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 40.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.95	14	3.5537	0.0	0.0	0.0	0.0026
No log	1.95	28	3.4536	0.0153	0.0024	0.0042	0.0049
No log	2.95	42	3.1247	0.2395	0.1816	0.2066	0.2843
No log	3.95	56	2.5988	0.4342	0.3539	0.3900	0.4543
No log	4.95	70	2.0168	0.5125	0.4086	0.4547	0.5148
No log	5.95	84	1.4838	0.5959	0.5180	0.5543	0.6086
No log	6.95	98	0.9300	0.7905	0.7619	0.7759	0.7981
No log	7.95	112	0.4874	0.9111	0.9078	0.9094	0.9147
No log	8.95	126	0.2940	0.9372	0.9368	0.9370	0.9396
No log	9.95	140	0.2086	0.9471	0.9482	0.9476	0.9490
No log	10.95	154	0.1688	0.9594	0.9610	0.9602	0.9627
No log	11.95	168	0.1450	0.9624	0.9641	0.9632	0.9659
No log	12.95	182	0.1334	0.9651	0.9669	0.9660	0.9686
No log	13.95	196	0.1213	0.9674	0.9685	0.9679	0.9702
No log	14.95	210	0.1155	0.9684	0.9696	0.9690	0.9718
No log	15.95	224	0.1093	0.9707	0.9712	0.9709	0.9734
No log	16.95	238	0.1059	0.9710	0.9716	0.9713	0.9739
No log	17.95	252	0.1046	0.9711	0.9716	0.9714	0.9740
No log	18.95	266	0.1014	0.9719	0.9724	0.9722	0.9745
No log	19.95	280	0.1003	0.9715	0.9722	0.9718	0.9742
No log	20.95	294	0.0987	0.9724	0.9730	0.9727	0.9751
No log	21.95	308	0.0971	0.9722	0.9728	0.9725	0.9750
No log	22.95	322	0.0968	0.9724	0.9735	0.9730	0.9754
No log	23.95	336	0.0954	0.9728	0.9736	0.9732	0.9756
No log	24.95	350	0.0967	0.9722	0.9731	0.9727	0.9752
No log	25.95	364	0.0965	0.9735	0.9744	0.9739	0.9763
No log	26.95	378	0.0963	0.9725	0.9735	0.9730	0.9757
No log	27.95	392	0.0972	0.9728	0.9738	0.9733	0.9759
No log	28.95	406	0.0987	0.9736	0.9745	0.9740	0.9766
No log	29.95	420	0.0994	0.9737	0.9742	0.9740	0.9764
No log	30.95	434	0.0985	0.9737	0.9741	0.9739	0.9764
No log	31.95	448	0.1022	0.9744	0.9746	0.9745	0.9769
No log	32.95	462	0.1020	0.9740	0.9744	0.9742	0.9767
No log	33.95	476	0.1055	0.9730	0.9738	0.9734	0.9758
No log	34.95	490	0.1068	0.9732	0.9742	0.9737	0.9760
0.6768	35.95	504	0.1085	0.9737	0.9740	0.9739	0.9764
0.6768	36.95	518	0.1088	0.9735	0.9743	0.9739	0.9764
0.6768	37.95	532	0.1100	0.9739	0.9744	0.9742	0.9768
0.6768	38.95	546	0.1107	0.9739	0.9745	0.9742	0.9767
0.6768	39.95	560	0.1115	0.9740	0.9747	0.9744	0.9769

Framework versions

• Transformers 4.25.1
• Pytorch 1.12.0
• Datasets 2.18.0
• Tokenizers 0.13.2