bert-base-multilingual-cased-FakeNews-Dravidian-mBert – AI Model by mdosama39 | AlphaNeural AI | AlphaNeural AI

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mdosama39/
bert-base-multilingual-cased-FakeNews-Dravidian-mBert

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text-classification

generated_from_trainer

google-bert/bert-base-multilingual-cased

autotrain_compatible

endpoints_compatible

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bert-base-multilingual-cased-FakeNews-Dravidian-mBert

This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4452
Accuracy: 0.8307
Weighted f1 score: 0.8305
Macro f1 score: 0.8305

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-06
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Weighted f1 score	Macro f1 score
0.7901	1.0	204	0.6155	0.7067	0.6999	0.7000
0.5829	2.0	408	0.4932	0.8037	0.8036	0.8036
0.4813	3.0	612	0.4437	0.8135	0.8130	0.8129
0.4298	4.0	816	0.4182	0.8123	0.8123	0.8123
0.3896	5.0	1020	0.4068	0.8221	0.8219	0.8219
0.3411	6.0	1224	0.3999	0.8209	0.8209	0.8209
0.314	7.0	1428	0.4030	0.8307	0.8305	0.8305
0.2982	8.0	1632	0.4084	0.8270	0.8269	0.8269
0.279	9.0	1836	0.4129	0.8319	0.8316	0.8315
0.2502	10.0	2040	0.4120	0.8307	0.8306	0.8305
0.2328	11.0	2244	0.4256	0.8368	0.8364	0.8364
0.2168	12.0	2448	0.4480	0.8393	0.8388	0.8388
0.2246	13.0	2652	0.4463	0.8294	0.8292	0.8292
0.2149	14.0	2856	0.4411	0.8307	0.8306	0.8305
0.2077	15.0	3060	0.4452	0.8307	0.8305	0.8305

Framework versions

Transformers 4.35.0
Pytorch 2.0.0
Datasets 2.11.0
Tokenizers 0.14.1