scenario-normal-finetune-clf-data-smsa-model-xlm-roberta-base

This model is a fine-tuned version of xlm-roberta-base on the smsa dataset. It achieves the following results on the evaluation set:

• Loss: 0.3511
• Accuracy: 0.9222
• F1: 0.9011

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 6969

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.29	100	0.4204	0.8397	0.6487
No log	0.58	200	0.3298	0.9095	0.8696
No log	0.87	300	0.2664	0.9214	0.8843
No log	1.16	400	0.2882	0.9151	0.8849
0.3642	1.45	500	0.2531	0.9175	0.8808
0.3642	1.74	600	0.2847	0.9175	0.8820
0.3642	2.03	700	0.2889	0.9294	0.9060
0.3642	2.33	800	0.3066	0.9270	0.8996
0.3642	2.62	900	0.3736	0.9190	0.8914
0.2064	2.91	1000	0.2706	0.9214	0.8853
0.2064	3.2	1100	0.3201	0.9190	0.8878
0.2064	3.49	1200	0.2372	0.9254	0.9007
0.2064	3.78	1300	0.2534	0.9190	0.8904
0.2064	4.07	1400	0.3266	0.9214	0.8939
0.1543	4.36	1500	0.3405	0.9135	0.8815
0.1543	4.65	1600	0.3485	0.9238	0.8988
0.1543	4.94	1700	0.3287	0.9270	0.9011
0.1543	5.23	1800	0.3631	0.9167	0.8866
0.1543	5.52	1900	0.3714	0.9167	0.8922
0.1227	5.81	2000	0.3030	0.9119	0.8794
0.1227	6.1	2100	0.3363	0.9286	0.9046
0.1227	6.4	2200	0.3511	0.9222	0.9011

Framework versions

• Transformers 4.33.3
• Pytorch 2.0.1
• Datasets 2.14.5
• Tokenizers 0.13.3