Jasoncpit/Roberta_finetuned_PMV

results

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

• Loss: 2.8299
• Accuracy: 0.6038
• F1: 0.5980

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: 2e-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
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	24	1.1029	0.1772	0.0627
No log	2.0	48	1.0290	0.5063	0.3404
No log	3.0	72	0.9006	0.5949	0.5268
No log	4.0	96	0.8745	0.6013	0.6014
No log	5.0	120	0.8370	0.5696	0.5730
No log	6.0	144	0.8020	0.6709	0.6623
No log	7.0	168	0.8105	0.6835	0.6759
No log	8.0	192	0.9875	0.6329	0.6251
No log	9.0	216	1.1282	0.6266	0.6317
No log	10.0	240	1.2444	0.5949	0.5950
No log	11.0	264	1.1916	0.6456	0.6394
No log	12.0	288	1.5230	0.5886	0.5905
No log	13.0	312	1.4544	0.6456	0.6381
No log	14.0	336	1.6109	0.6076	0.6093
No log	15.0	360	1.6181	0.6203	0.6213
No log	16.0	384	1.6836	0.6392	0.6382
No log	17.0	408	1.7056	0.6709	0.6648
No log	18.0	432	1.9027	0.5949	0.5968
No log	19.0	456	1.7156	0.6835	0.6695
No log	20.0	480	1.8976	0.6392	0.6376
0.3619	21.0	504	1.8731	0.6139	0.6172
0.3619	22.0	528	1.8723	0.6709	0.6570
0.3619	23.0	552	2.1482	0.5886	0.5921
0.3619	24.0	576	1.8633	0.6203	0.6198
0.3619	25.0	600	1.7921	0.6392	0.6373
0.3619	26.0	624	1.8867	0.6203	0.6229
0.3619	27.0	648	1.8571	0.6646	0.6535
0.3619	28.0	672	1.9876	0.6266	0.6295
0.3619	29.0	696	1.8853	0.6519	0.6452
0.3619	30.0	720	2.0321	0.6266	0.6315
0.3619	31.0	744	1.8590	0.6646	0.6553
0.3619	32.0	768	2.2514	0.6266	0.6297
0.3619	33.0	792	1.8813	0.6646	0.6647
0.3619	34.0	816	2.1837	0.6139	0.6158
0.3619	35.0	840	1.8851	0.6709	0.6682
0.3619	36.0	864	2.0150	0.6329	0.6346
0.3619	37.0	888	1.9542	0.6709	0.6703
0.3619	38.0	912	2.0234	0.6582	0.6551
0.3619	39.0	936	2.1399	0.6329	0.6350
0.3619	40.0	960	2.1121	0.6329	0.6357
0.3619	41.0	984	2.0931	0.6266	0.6291
0.0321	42.0	1008	1.9945	0.6772	0.6757
0.0321	43.0	1032	2.0745	0.6646	0.6652
0.0321	44.0	1056	2.0226	0.6835	0.6795
0.0321	45.0	1080	2.1174	0.6582	0.6589
0.0321	46.0	1104	2.1243	0.6456	0.6467
0.0321	47.0	1128	2.1506	0.6203	0.6226
0.0321	48.0	1152	2.1542	0.6329	0.6350
0.0321	49.0	1176	2.1295	0.6582	0.6580
0.0321	50.0	1200	2.1290	0.6582	0.6580

Framework versions

• Transformers 4.35.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.14.1