app_prova2 – AI Model by AleRams | AlphaNeural AI

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AleRams/
app_prova2

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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app_prova2

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

Loss: 0.1559
Accuracy: 0.935

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: 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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.4056	0.53	100	1.3185	0.4267
1.3194	1.06	200	1.1961	0.4933
1.2148	1.6	300	1.0185	0.6167
0.9494	2.13	400	0.8830	0.6567
0.9645	2.66	500	0.7196	0.7433
0.6089	3.19	600	0.5523	0.8017
0.7564	3.72	700	0.4789	0.83
0.5319	4.26	800	0.3553	0.8683
0.3567	4.79	900	0.2926	0.88
0.2969	5.32	1000	0.2558	0.89
0.2578	5.85	1100	0.2054	0.9217
0.3002	6.38	1200	0.1744	0.9333
0.293	6.91	1300	0.1620	0.9483
0.132	7.45	1400	0.1646	0.92
0.1836	7.98	1500	0.1559	0.935

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0