TwHIN-BERT-Misinformation-Classifier

roupenminassian/TwHIN-BERT-Misinformation-Classifier

TwHIN-BERT-Misinformation-Classifier is a text classification model from roupenminassian. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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Base models

Twitter/twhin-bert-large

Datasets

roupenminassian/twitter-misinformation

Model card

From the Hugging Face model README

TwHIN-BERT-Misinformation-Classifier

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

Loss: 0.0632
F1: 0.9829

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.3092	0.04	250	0.0873	0.9683
0.095	0.09	500	0.0973	0.9752
0.0937	0.13	750	0.0969	0.9669
0.0859	0.17	1000	0.0762	0.9772
0.0828	0.22	1250	0.1208	0.9669
0.0685	0.26	1500	0.0745	0.9803
0.0801	0.3	1750	0.0678	0.9798
0.0717	0.35	2000	0.1027	0.9783
0.1132	0.39	2250	0.0968	0.9771
0.1088	0.43	2500	0.0838	0.9794
0.0976	0.48	2750	0.0915	0.9787
0.0863	0.52	3000	0.0828	0.9797
0.0967	0.56	3250	0.1350	0.9722
0.1225	0.61	3500	0.1098	0.9753
0.1017	0.65	3750	0.0807	0.9754
0.091	0.69	4000	0.1083	0.9757
0.0882	0.74	4250	0.0768	0.9811
0.0688	0.78	4500	0.0819	0.9788
0.0728	0.82	4750	0.0758	0.9802
0.0967	0.87	5000	0.1157	0.9743
0.0862	0.91	5250	0.0677	0.9816
0.0815	0.95	5500	0.0709	0.9768
0.0776	1.0	5750	0.0737	0.9799
0.0635	1.04	6000	0.0941	0.9761
0.0805	1.08	6250	0.0993	0.9778
0.0887	1.13	6500	0.0916	0.9770
0.0824	1.17	6750	0.0859	0.9790
0.0885	1.21	7000	0.0893	0.9795
0.0868	1.26	7250	0.0686	0.9801
0.09	1.3	7500	0.0940	0.9767
0.0907	1.34	7750	0.0878	0.9796
0.0835	1.39	8000	0.0736	0.9790
0.0747	1.43	8250	0.0878	0.9817
0.0745	1.47	8500	0.0675	0.9809
0.0754	1.52	8750	0.0708	0.9813
0.0781	1.56	9000	0.0816	0.9822
0.069	1.6	9250	0.0761	0.9820
0.0708	1.65	9500	0.0887	0.9814
0.1062	1.69	9750	0.0887	0.9803
0.0643	1.73	10000	0.0771	0.9821
0.0675	1.77	10250	0.0870	0.9809
0.0705	1.82	10500	0.0791	0.9817
0.0737	1.86	10750	0.0780	0.9815
0.0665	1.9	11000	0.0779	0.9828
0.0919	1.95	11250	0.0905	0.9768
0.0687	1.99	11500	0.0647	0.9806
0.0675	2.03	11750	0.0634	0.9814
0.0549	2.08	12000	0.0670	0.9812
0.0593	2.12	12250	0.0676	0.9815
0.0603	2.16	12500	0.0624	0.9828
0.0553	2.21	12750	0.0762	0.9806
0.0502	2.25	13000	0.0798	0.9835
0.0431	2.29	13250	0.1644	0.9227
0.0514	2.34	13500	0.0831	0.9803
0.0536	2.38	13750	0.0585	0.9833
0.0472	2.42	14000	0.0570	0.9839
0.0581	2.47	14250	0.0561	0.9828
0.0465	2.51	14500	0.0585	0.9842
0.0542	2.55	14750	0.1652	0.9827
0.0595	2.6	15000	0.1015	0.9821
0.051	2.64	15250	0.2099	0.8379
0.0447	2.68	15500	0.0633	0.9826
0.0389	2.73	15750	0.0647	0.9830
0.0533	2.77	16000	0.0623	0.9829
0.0524	2.81	16250	0.0586	0.9831
0.0576	2.86	16500	0.0597	0.9830
0.0483	2.9	16750	0.0689	0.9836
0.0469	2.94	17000	0.0569	0.9837
0.0447	2.99	17250	0.0632	0.9829

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Apr 20, 2023
Updated: Jun 13, 2025