README.md

metadata

language:
  - en
license: null
tags:
  - generated_from_trainer
datasets:
  - jigsaw
model_index:
  - name: bert-base-uncased
    results:
      - {}

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