bert-base-uncased-finetuned-stationary-update – AI Model by MKS3099 | AlphaNeural AI

You can deploy this model and start earning money today!

MKS3099/
bert-base-uncased-finetuned-stationary-update

like

0

text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

Views

No views yet

bert-base-uncased-finetuned-stationary-update

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: 0.8082
Accuracy: 0.7967
F1: 0.7872

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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.5673	1.0	38	0.5049	0.7667	0.7453
0.4018	2.0	76	0.4605	0.79	0.7853
0.3074	3.0	114	0.4991	0.7967	0.7941
0.2065	4.0	152	0.5517	0.7967	0.7914
0.1347	5.0	190	0.7082	0.7833	0.7655
0.1008	6.0	228	0.7469	0.7967	0.7811
0.0799	7.0	266	0.7609	0.7933	0.7823
0.0558	8.0	304	0.8108	0.7967	0.7853
0.0526	9.0	342	0.7988	0.79	0.7821
0.0426	10.0	380	0.8082	0.7967	0.7872

Framework versions

Transformers 4.36.0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0