CancerTextV1 – AI Model by Dinithi | AlphaNeural AI

You can deploy this model and start earning money today!

Dinithi/
CancerTextV1

like

0

text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

Views

No views yet

CancerTextV1

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5476
Accuracy: 0.8683
Precision: 0.8558
Recall: 0.8870
F1: 0.8711

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: 1e-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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.3268	1.0	600	0.3939	0.8475	0.8268	0.8804	0.8528
0.3132	2.0	1200	0.3510	0.8475	0.8509	0.8439	0.8474
0.2595	3.0	1800	0.3631	0.8617	0.8505	0.8787	0.8644
0.2256	4.0	2400	0.4303	0.8625	0.8507	0.8804	0.8653
0.1944	5.0	3000	0.4551	0.8642	0.8592	0.8721	0.8656
0.1734	6.0	3600	0.4673	0.86	0.8434	0.8854	0.8639
0.1446	7.0	4200	0.4960	0.87	0.8562	0.8904	0.8730
0.1371	8.0	4800	0.5162	0.8708	0.8646	0.8804	0.8724
0.123	9.0	5400	0.5396	0.8642	0.8604	0.8704	0.8654
0.1174	10.0	6000	0.5476	0.8683	0.8558	0.8870	0.8711

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1