Modelpublic

JeswinMS4/scam-alert-mobile-bert

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

0likes42downloads

Model Card

scam-alert-mobile-bert

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

—Loss: 0.7097
—Accuracy: 0.9880
—F1: 0.9880

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
—trainbatchsize: 8
—evalbatchsize: 2
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.1577	100	0.4729	0.9223	0.9145
No log	0.3155	200	2.1621	0.9801	0.9803
No log	0.4732	300	0.8327	0.9900	0.9900
No log	0.6309	400	3.3648	0.9900	0.9900
No log	0.7886	500	0.8376	0.9861	0.9861
No log	0.9464	600	0.7630	0.9861	0.9861
No log	1.1041	700	0.6559	0.9861	0.9861
No log	1.2618	800	2.2440	0.9880	0.9880
No log	1.4196	900	2.4358	0.9900	0.9900
No log	1.5773	1000	1.9655	0.9861	0.9859
No log	1.7350	1100	1.8927	0.9880	0.9880
No log	1.8927	1200	1.3919	0.9880	0.9880
No log	2.0505	1300	0.9143	0.9861	0.9860
No log	2.2082	1400	0.1891	0.9861	0.9859
No log	2.3659	1500	0.0815	0.9861	0.9861
No log	2.5237	1600	0.0853	0.9880	0.9880
No log	2.6814	1700	0.2719	0.9861	0.9860
No log	2.8391	1800	0.2175	0.9900	0.9900
No log	2.9968	1900	0.5407	0.9880	0.9880
No log	3.1546	2000	0.8695	0.9880	0.9880
No log	3.3123	2100	0.1031	0.9880	0.9880
No log	3.4700	2200	1.1922	0.9900	0.9900
No log	3.6278	2300	0.4830	0.9880	0.9880
No log	3.7855	2400	1.4562	0.9880	0.9880
No log	3.9432	2500	1.8929	0.9900	0.9900
2789.4062	4.1009	2600	0.6560	0.9880	0.9880
2789.4062	4.2587	2700	0.1473	0.9841	0.9842
2789.4062	4.4164	2800	0.3488	0.9880	0.9880
2789.4062	4.5741	2900	0.2347	0.9880	0.9880
2789.4062	4.7319	3000	0.7488	0.9900	0.9900
2789.4062	4.8896	3100	0.5055	0.9880	0.9880
2789.4062	5.0473	3200	0.8339	0.9900	0.9900
2789.4062	5.2050	3300	0.5382	0.9880	0.9880
2789.4062	5.3628	3400	0.6095	0.9880	0.9880
2789.4062	5.5205	3500	0.7142	0.9880	0.9880
2789.4062	5.6782	3600	0.6855	0.9880	0.9880
2789.4062	5.8360	3700	0.7152	0.9880	0.9880
2789.4062	5.9937	3800	0.7097	0.9880	0.9880

Framework versions

—Transformers 4.41.1
—Pytorch 2.3.0+cu121
—Datasets 2.19.2
—Tokenizers 0.19.1