CS221-bert-base-uncased-finetuned-semeval-NTdeu

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

Model description

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The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 20

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.3253	1.0	261	0.3549	0.3597	0.6101	0.4012
0.3116	2.0	522	0.3043	0.5547	0.7009	0.5144
0.2182	3.0	783	0.3106	0.5885	0.7273	0.5163
0.1216	4.0	1044	0.3249	0.5851	0.7324	0.5048
0.077	5.0	1305	0.3578	0.5931	0.7298	0.5202
0.0335	6.0	1566	0.3857	0.6011	0.7338	0.5298
0.0214	7.0	1827	0.4070	0.61	0.7469	0.5278
0.0092	8.0	2088	0.4319	0.6198	0.7540	0.5374
0.0081	9.0	2349	0.4407	0.6239	0.7534	0.5393
0.006	10.0	2610	0.4525	0.6236	0.7521	0.5470
0.004	11.0	2871	0.4609	0.6266	0.7524	0.5470
0.0036	12.0	3132	0.4741	0.6237	0.7498	0.5451
0.0026	13.0	3393	0.4845	0.6223	0.7500	0.5432
0.0033	14.0	3654	0.4889	0.6314	0.7575	0.5528
0.003	15.0	3915	0.4953	0.6264	0.7518	0.5528
0.0034	16.0	4176	0.4986	0.6288	0.7555	0.5528
0.0023	17.0	4437	0.5016	0.6321	0.7571	0.5566
0.0027	18.0	4698	0.5027	0.6232	0.7508	0.5470
0.0032	19.0	4959	0.5029	0.6302	0.7553	0.5528
0.0029	20.0	5220	0.5029	0.6294	0.7551	0.5509