bert-base-uncased-jigsaw

作者 imvladikon · 下载 0 · 点赞 0 · 更新 2023-10-31

generated_from_trainer

bert-base-uncased

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

Loss: 0.0393
Precision Micro: 0.7758
Recall Micro: 0.7858
F1 Micro: 0.7808
F2 Micro: 0.7838
Precision Macro: 0.6349
Recall Macro: 0.5972
F1 Macro: 0.6105
F2 Macro: 0.6015
Overall Precision: 0.9841
Overall Recall: 0.9841
Overall F1: 0.9841
Overall F2: 0.9841
Overall Accuracy: 0.9841
Matthews Corrcoef: 0.7725
Aucroc Macro: 0.9897
Aucroc Micro: 0.9920
Accuracy Toxic: 0.9678
F1 Toxic: 0.8295
Accuracy Severe Toxic: 0.9899
F1 Severe Toxic: 0.3313
Accuracy Obscene: 0.9816
F1 Obscene: 0.8338
Accuracy Threat: 0.9974
F1 Threat: 0.4545
Accuracy Insult: 0.9763
F1 Insult: 0.7662
Accuracy Identity Hate: 0.9914
F1 Identity Hate: 0.4480

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: 3e-05
train_batch_size: 24
eval_batch_size: 12
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 48
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Micro	Recall Micro	F1 Micro	F2 Micro	Precision Macro	Recall Macro	F1 Macro	F2 Macro	Overall Precision	Overall Recall	Overall F1	Overall F2	Overall Accuracy	Matthews Corrcoef	Aucroc Macro	Aucroc Micro	Accuracy Toxic	F1 Toxic	Accuracy Severe Toxic	F1 Severe Toxic	Accuracy Obscene	F1 Obscene	Accuracy Threat	F1 Threat	Accuracy Insult	F1 Insult	Accuracy Identity Hate	F1 Identity Hate
0.0433	1.0	2659	0.0423	0.7607	0.7798	0.7702	0.7759	0.6398	0.5561	0.5585	0.5535	0.9832	0.9832	0.9832	0.9832	0.9832	0.7615	0.9887	0.9908	0.9671	0.8211	0.9878	0.4354	0.9805	0.8265	0.9974	0.2243	0.9746	0.7602	0.9918	0.2834
0.0366	2.0	5318	0.0393	0.7758	0.7858	0.7808	0.7838	0.6349	0.5972	0.6105	0.6015	0.9841	0.9841	0.9841	0.9841	0.9841	0.7725	0.9897	0.9920	0.9678	0.8295	0.9899	0.3313	0.9816	0.8338	0.9974	0.4545	0.9763	0.7662	0.9914	0.4480
0.0305	3.0	7977	0.0399	0.7608	0.8186	0.7887	0.8064	0.6621	0.6856	0.6715	0.6794	0.9842	0.9842	0.9842	0.9842	0.9842	0.7810	0.9897	0.9919	0.9662	0.8272	0.9892	0.4772	0.9815	0.8347	0.9977	0.5629	0.9772	0.7740	0.9931	0.5528
0.0263	4.0	10636	0.0435	0.7333	0.8336	0.7803	0.8114	0.6395	0.7039	0.6687	0.6890	0.9830	0.9830	0.9830	0.9830	0.9830	0.7732	0.9897	0.9912	0.9608	0.8083	0.9898	0.4791	0.9812	0.8319	0.9972	0.5368	0.9756	0.7700	0.9935	0.5861
0.0218	5.0	13295	0.0456	0.7480	0.8108	0.7781	0.7974	0.6661	0.6720	0.6662	0.6691	0.9833	0.9833	0.9833	0.9833	0.9833	0.7701	0.9890	0.9907	0.9612	0.8071	0.9894	0.4642	0.9823	0.8354	0.9977	0.5325	0.9754	0.7613	0.9936	0.5968

Framework versions

Transformers 4.8.2
Pytorch 1.9.0+cu102
Datasets 1.9.0
Tokenizers 0.10.3