pranaydeeps/lettuce_pos_en_xlm

pos_final_xlm_en

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: 0.0719
• Precision: 0.9686
• Recall: 0.9705
• F1: 0.9695
• Accuracy: 0.9790

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	0.99	60	3.0062	0.2412	0.1720	0.2008	0.3036
No log	1.99	120	0.5353	0.8699	0.8553	0.8625	0.8970
No log	2.99	180	0.1312	0.9578	0.9553	0.9566	0.9691
No log	3.99	240	0.0981	0.9621	0.9628	0.9625	0.9737
No log	4.99	300	0.0853	0.9652	0.9659	0.9655	0.9760
No log	5.99	360	0.0788	0.9656	0.9676	0.9666	0.9769
No log	6.99	420	0.0745	0.9664	0.9689	0.9677	0.9775
No log	7.99	480	0.0718	0.9675	0.9689	0.9682	0.9780
0.7956	8.99	540	0.0707	0.9679	0.9683	0.9681	0.9779
0.7956	9.99	600	0.0686	0.9682	0.9698	0.9690	0.9786
0.7956	10.99	660	0.0686	0.9689	0.9694	0.9692	0.9787
0.7956	11.99	720	0.0680	0.9679	0.9707	0.9693	0.9787
0.7956	12.99	780	0.0685	0.9683	0.9706	0.9694	0.9789
0.7956	13.99	840	0.0695	0.9689	0.9700	0.9694	0.9788
0.7956	14.99	900	0.0703	0.9682	0.9699	0.9690	0.9786
0.7956	15.99	960	0.0719	0.9686	0.9705	0.9695	0.9790
0.051	16.99	1020	0.0735	0.9687	0.9701	0.9694	0.9788
0.051	17.99	1080	0.0747	0.9684	0.9701	0.9692	0.9787
0.051	18.99	1140	0.0761	0.9685	0.9697	0.9691	0.9786
0.051	19.99	1200	0.0774	0.9678	0.9698	0.9688	0.9784
0.051	20.99	1260	0.0796	0.9685	0.9694	0.9690	0.9785
0.051	21.99	1320	0.0796	0.9681	0.9701	0.9691	0.9786
0.051	22.99	1380	0.0820	0.9684	0.9690	0.9687	0.9784
0.051	23.99	1440	0.0829	0.9679	0.9688	0.9683	0.9781
0.0318	24.99	1500	0.0854	0.9681	0.9690	0.9686	0.9782
0.0318	25.99	1560	0.0881	0.9677	0.9692	0.9684	0.9782
0.0318	26.99	1620	0.0893	0.9679	0.9690	0.9685	0.9783
0.0318	27.99	1680	0.0910	0.9676	0.9691	0.9683	0.9781
0.0318	28.99	1740	0.0919	0.9684	0.9686	0.9685	0.9783
0.0318	29.99	1800	0.0933	0.9678	0.9686	0.9682	0.9781
0.0318	30.99	1860	0.0947	0.9677	0.9688	0.9683	0.9781
0.0318	31.99	1920	0.0966	0.9678	0.9694	0.9686	0.9783
0.0318	32.99	1980	0.0974	0.9677	0.9689	0.9683	0.9781
0.0211	33.99	2040	0.0981	0.9684	0.9693	0.9688	0.9784
0.0211	34.99	2100	0.0989	0.9681	0.9690	0.9686	0.9783
0.0211	35.99	2160	0.1008	0.9679	0.9695	0.9687	0.9784
0.0211	36.99	2220	0.1015	0.9681	0.9689	0.9685	0.9782
0.0211	37.99	2280	0.1015	0.9677	0.9689	0.9683	0.9781
0.0211	38.99	2340	0.1024	0.9679	0.9690	0.9684	0.9782
0.0211	39.99	2400	0.1022	0.9680	0.9690	0.9685	0.9782

Framework versions

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