results – AI Model by erfansalima | AlphaNeural AI

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erfansalima/
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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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results

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

Loss: 1.4638
Accuracy: 0.6007
F1: 0.6006
Precision: 0.6110
Recall: 0.6007

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: 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
lr_scheduler_warmup_steps: 500
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.1309	1.0	210	1.1230	0.5352	0.5141	0.5661	0.5352
0.9674	2.0	420	1.0355	0.5936	0.5845	0.6239	0.5936
0.8132	3.0	630	1.0338	0.5959	0.5956	0.6162	0.5959
0.4738	4.0	840	1.2041	0.5983	0.5985	0.6091	0.5983
0.1973	5.0	1050	1.4638	0.6007	0.6006	0.6110	0.6007

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1