bert-base-german-cased-defakts-fake-binary

kugler/bert-base-german-cased-defakts-fake-binary

bert-base-german-cased-defakts-fake-binary is a text classification model from kugler. Use it when you need a label for a piece of text. It is set up for transformers.

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

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At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Jan 14, 2025
Updated: Jan 14, 2025
SHA: ce5d6878

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Model card

From the Hugging Face model README

bert-base-german-cased-defakts-fake-binary

This Model is finetuned for sequence classification (binary fake-news classification task) on the german DeFaktS-Dataset. It achieves the following results on the evaluation set:

Loss: 0.3608
Accuracy: 0.8531
F1: 0.8392
Precision: 0.8636
Recall: 0.8283

Model description

This Model is finetuned for sequence classification

Dataset

Trained on the DeFactS dataset https://github.com/caisa-lab/DeFaktS-Dataset-Disinformaton-Detection, feature catposfake/catneutral to detect fake news

Intended uses & limitations

Fake news classification

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-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
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy@de	F1@de	Precision@de	Recall@de	Loss@de
0.5531	0.0888	50	0.4191	0.8071	0.7958	0.7993	0.7931	0.4192
0.4341	0.1776	100	0.3987	0.8186	0.8132	0.8100	0.8198	0.3988
0.3854	0.2664	150	0.3816	0.8206	0.8100	0.8138	0.8071	0.3817
0.4028	0.3552	200	0.3703	0.8301	0.8221	0.8218	0.8223	0.3704
0.3784	0.4440	250	0.3750	0.8276	0.8065	0.8491	0.7933	0.3752
0.3622	0.5329	300	0.3465	0.8461	0.8389	0.8384	0.8393	0.3465
0.3945	0.6217	350	0.4596	0.7706	0.7704	0.7934	0.8006	0.4595
0.4073	0.7105	400	0.3360	0.8531	0.8419	0.8549	0.8343	0.3361
0.3779	0.7993	450	0.3440	0.8451	0.8399	0.8366	0.8455	0.3441
0.3596	0.8881	500	0.3608	0.8531	0.8392	0.8636	0.8283	0.3610
0.3588	0.9769	550	0.3468	0.8516	0.8375	0.8620	0.8266	0.3468
0.287	1.0657	600	0.3416	0.8591	0.8527	0.8517	0.8539	0.3416
0.2395	1.1545	650	0.3976	0.8531	0.8419	0.8547	0.8345	0.3977
0.2278	1.2433	700	0.3635	0.8441	0.8387	0.8355	0.8438	0.3635
0.2495	1.3321	750	0.3294	0.8581	0.8518	0.8506	0.8530	0.3294
0.2455	1.4210	800	0.3448	0.8581	0.8516	0.8507	0.8526	0.3448
0.2472	1.5098	850	0.3743	0.8626	0.8527	0.8635	0.8460	0.3745

Framework versions

Transformers 4.45.2
Pytorch 2.3.1+cu121
Tokenizers 0.20.3

Task: Text Classification
Library: transformers
Type: bert
Created: Jan 14, 2025
Updated: Jan 14, 2025