lotto – AI Model by gilson0156 | AlphaNeural AI

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gilson0156/
lotto

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text-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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lotto

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

Loss: 0.3929
Accuracy: 0.1383
Precision: 0.1383
Recall: 0.1383
F1: 0.1383

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.4559	1.0	18	0.4474	0.1583	0.1583	0.1583	0.1583
0.4029	2.0	36	0.3972	0.1333	0.1333	0.1333	0.1333
0.3953	3.0	54	0.3924	0.135	0.135	0.135	0.135
0.3956	4.0	72	0.3926	0.1483	0.1483	0.1483	0.1483
0.3983	5.0	90	0.3933	0.1417	0.1417	0.1417	0.1417
0.3924	6.0	108	0.3926	0.1367	0.1367	0.1367	0.1367
0.3917	7.0	126	0.3926	0.1417	0.1417	0.1417	0.1417
0.3923	8.0	144	0.3924	0.1483	0.1483	0.1483	0.1483
0.3965	9.0	162	0.3929	0.1350	0.135	0.135	0.135
0.3939	10.0	180	0.3929	0.1383	0.1383	0.1383	0.1383

Framework versions

Transformers 4.41.2
Pytorch 2.2.2
Datasets 2.20.0
Tokenizers 0.19.1