newsdata-bert – AI Model by tiagoblima | AlphaNeural AI

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tiagoblima/
newsdata-bert

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text-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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newsdata-bert

This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4835
Accuracy: 0.8617
Precision: 0.8494
F1: 0.8533
Recall: 0.8617

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: 4
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	F1	Recall
1.2095	0.1024	1000	1.0182	0.7335	0.6811	0.6915	0.7335
0.8995	0.2048	2000	0.8102	0.7798	0.7622	0.7586	0.7798
0.7554	0.3071	3000	0.6720	0.8165	0.7938	0.8023	0.8165
0.6805	0.4095	4000	0.6185	0.828	0.8107	0.8157	0.828
0.6192	0.5119	5000	0.5865	0.8322	0.8233	0.8226	0.8322
0.5963	0.6143	6000	0.5462	0.8475	0.8333	0.8356	0.8475
0.5466	0.7166	7000	0.5384	0.849	0.8386	0.8398	0.849
0.5447	0.8190	8000	0.4923	0.8582	0.8440	0.8491	0.8582
0.5288	0.9214	9000	0.4835	0.8617	0.8494	0.8533	0.8617

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1