pranaydeeps/lettuce_pos_nl_xlm

pos_final_xlm_nl

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: 0.1066
• Precision: 0.9780
• Recall: 0.9783
• F1: 0.9782
• Accuracy: 0.9789

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	1.0	69	3.4837	0.2936	0.1709	0.2161	0.3200
No log	2.0	138	0.8299	0.8501	0.8416	0.8459	0.8497
No log	3.0	207	0.2765	0.9419	0.9408	0.9414	0.9429
No log	4.0	276	0.1704	0.9601	0.9596	0.9599	0.9611
No log	5.0	345	0.1259	0.9685	0.9686	0.9686	0.9693
No log	6.0	414	0.1085	0.9711	0.9713	0.9712	0.9719
No log	7.0	483	0.0984	0.9728	0.9731	0.9729	0.9738
1.1448	8.0	552	0.0906	0.9742	0.9745	0.9743	0.9752
1.1448	9.0	621	0.0888	0.9749	0.9752	0.9751	0.9758
1.1448	10.0	690	0.0864	0.9757	0.9759	0.9758	0.9765
1.1448	11.0	759	0.0842	0.9764	0.9767	0.9765	0.9772
1.1448	12.0	828	0.0840	0.9764	0.9768	0.9766	0.9773
1.1448	13.0	897	0.0846	0.9766	0.9769	0.9768	0.9775
1.1448	14.0	966	0.0854	0.9768	0.9771	0.9769	0.9776
0.0668	15.0	1035	0.0867	0.9767	0.9770	0.9768	0.9776
0.0668	16.0	1104	0.0859	0.9769	0.9772	0.9771	0.9778
0.0668	17.0	1173	0.0858	0.9772	0.9775	0.9773	0.9781
0.0668	18.0	1242	0.0878	0.9776	0.9779	0.9778	0.9785
0.0668	19.0	1311	0.0887	0.9775	0.9779	0.9777	0.9785
0.0668	20.0	1380	0.0902	0.9774	0.9777	0.9775	0.9783
0.0668	21.0	1449	0.0910	0.9772	0.9775	0.9774	0.9782
0.0375	22.0	1518	0.0926	0.9774	0.9777	0.9775	0.9783
0.0375	23.0	1587	0.0930	0.9777	0.9780	0.9779	0.9787
0.0375	24.0	1656	0.0955	0.9777	0.9781	0.9779	0.9787
0.0375	25.0	1725	0.0955	0.9778	0.9781	0.9780	0.9787
0.0375	26.0	1794	0.0978	0.9776	0.9779	0.9777	0.9785
0.0375	27.0	1863	0.0997	0.9772	0.9775	0.9774	0.9782
0.0375	28.0	1932	0.1000	0.9776	0.9779	0.9778	0.9786
0.0238	29.0	2001	0.1022	0.9775	0.9778	0.9776	0.9785
0.0238	30.0	2070	0.1030	0.9777	0.9780	0.9779	0.9787
0.0238	31.0	2139	0.1041	0.9778	0.9780	0.9779	0.9787
0.0238	32.0	2208	0.1054	0.9778	0.9781	0.9779	0.9787
0.0238	33.0	2277	0.1055	0.9777	0.9779	0.9778	0.9786
0.0238	34.0	2346	0.1063	0.9778	0.9780	0.9779	0.9787
0.0238	35.0	2415	0.1066	0.9780	0.9783	0.9782	0.9789
0.0238	36.0	2484	0.1075	0.9779	0.9781	0.9780	0.9788
0.0167	37.0	2553	0.1083	0.9780	0.9783	0.9781	0.9789
0.0167	38.0	2622	0.1083	0.9780	0.9783	0.9781	0.9789
0.0167	39.0	2691	0.1087	0.9779	0.9782	0.9781	0.9789
0.0167	40.0	2760	0.1088	0.9780	0.9782	0.9781	0.9789

Framework versions

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