bert-base-uncased_bert-base-uncased – AI Model by avinasht | AlphaNeural AI

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avinasht/
bert-base-uncased_bert-base-uncased

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-base-uncased_bert-base-uncased

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: 0.4173
Accuracy: 0.8627
F1: 0.8635
Precision: 0.8672
Recall: 0.8627

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: 0.0001
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: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.894	1.0	91	0.8435	0.6568	0.6583	0.6624	0.6568
0.6269	2.0	182	0.6009	0.7738	0.7765	0.7942	0.7738
0.4109	3.0	273	0.4567	0.8253	0.8256	0.8262	0.8253
0.2831	4.0	364	0.4173	0.8627	0.8635	0.8672	0.8627
0.2121	5.0	455	0.4562	0.8424	0.8396	0.8457	0.8424
0.1212	6.0	546	0.5369	0.8502	0.8491	0.8502	0.8502
0.116	7.0	637	0.5242	0.8502	0.8487	0.8528	0.8502
0.0976	8.0	728	0.5847	0.8487	0.8482	0.8486	0.8487
0.1313	9.0	819	0.5216	0.8440	0.8435	0.8442	0.8440

Framework versions

Transformers 4.37.0
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.1