TTC4900Model – AI Model by AmirlyPhd | AlphaNeural AI

You can deploy this model and start earning money today!

AmirlyPhd/
TTC4900Model

like

0

text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

Views

No views yet

TTC4900Model

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: 3.1884
Accuracy: 0.6272
F1: 0.7392
Precision: 0.7048
Recall: 0.8129

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: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.5316	0.56	50	1.1986	0.6262	0.4825	0.5074	0.5748
0.5421	1.12	100	0.2282	0.9464	0.9318	0.9579	0.9159
0.1327	1.69	150	0.2318	0.9499	0.9542	0.9479	0.9637
0.1214	2.25	200	0.1772	0.9669	0.9688	0.9652	0.9730
0.0632	2.81	250	0.2155	0.9669	0.9688	0.9681	0.9696

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2