LiLT-SER-EN

This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.3960
• Precision: 0.7248
• Recall: 0.7458
• F1: 0.7351
• Accuracy: 0.7438

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: 8
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0832	6.67	500	1.0009	0.6741	0.6923	0.6831	0.7292
0.052	13.33	1000	1.4186	0.7225	0.7320	0.7272	0.7441
0.0027	20.0	1500	1.5508	0.7218	0.7376	0.7297	0.7464
0.0034	26.67	2000	1.7198	0.7051	0.7382	0.7213	0.7422
0.002	33.33	2500	1.8116	0.7106	0.7392	0.7246	0.7424
0.0002	40.0	3000	1.8843	0.6769	0.7514	0.7122	0.7435
0.0009	46.67	3500	1.9528	0.7401	0.7514	0.7457	0.7518
0.0224	53.33	4000	2.0602	0.7178	0.7529	0.7350	0.7476
0.0002	60.0	4500	2.2901	0.7283	0.7509	0.7394	0.7287
0.0001	66.67	5000	2.1746	0.7198	0.7433	0.7313	0.7371
0.0001	73.33	5500	1.9452	0.7214	0.7387	0.7299	0.7641
0.0	80.0	6000	2.0976	0.7350	0.7560	0.7454	0.7442
0.0021	86.67	6500	2.3034	0.7200	0.7387	0.7292	0.7365
0.0	93.33	7000	2.2409	0.7348	0.7636	0.7489	0.7499
0.0	100.0	7500	2.2742	0.7362	0.7193	0.7276	0.7472
0.0	106.67	8000	2.4953	0.7312	0.7509	0.7409	0.7363
0.0	113.33	8500	2.4936	0.7340	0.7438	0.7389	0.7396
0.0	120.0	9000	2.3976	0.7239	0.7453	0.7344	0.7440
0.0001	126.67	9500	2.3723	0.7282	0.7478	0.7379	0.7441
0.0	133.33	10000	2.3960	0.7248	0.7458	0.7351	0.7438

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.1