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Ahmed235/roberta-base-topic_classification_simple

sourceHugging Facemitupdated 3y agoView on Hugging Face

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roberta-base-topicclassificationsimple

This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:

—Loss: 1.3253
—Accuracy: {'accuracy': 0.8445839874411303}
—F1: {'f1': 0.8435559601445874}

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: 32
—evalbatchsize: 32
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	353	0.6772	{'accuracy': 0.7905359946176272}	{'f1': 0.7881026657042776}
0.8304	2.0	706	0.6028	{'accuracy': 0.8187934514465127}	{'f1': 0.8207294945978928}
0.3839	3.0	1059	0.5942	{'accuracy': 0.8344920385736713}	{'f1': 0.8333019225828988}
0.3839	4.0	1412	0.6904	{'accuracy': 0.8340435075128952}	{'f1': 0.8330992428789376}
0.2015	5.0	1765	0.8314	{'accuracy': 0.8264184794797039}	{'f1': 0.82429813311833}
0.118	6.0	2118	0.8572	{'accuracy': 0.8356133662256111}	{'f1': 0.8349736274018552}
0.118	7.0	2471	0.9742	{'accuracy': 0.8383045525902669}	{'f1': 0.8376600364979794}
0.0804	8.0	2824	1.0628	{'accuracy': 0.8333707109217313}	{'f1': 0.8313400577604307}
0.0508	9.0	3177	1.0866	{'accuracy': 0.8333707109217313}	{'f1': 0.832415418717587}
0.0406	10.0	3530	1.1633	{'accuracy': 0.8432383942588024}	{'f1': 0.8425868379595812}
0.0406	11.0	3883	1.2132	{'accuracy': 0.8400986768333707}	{'f1': 0.8388873470699977}
0.0245	12.0	4236	1.2799	{'accuracy': 0.836958959407939}	{'f1': 0.8378019487138132}
0.0139	13.0	4589	1.2379	{'accuracy': 0.8434626597891904}	{'f1': 0.8429633731503271}
0.0139	14.0	4942	1.2578	{'accuracy': 0.8445839874411303}	{'f1': 0.8439974594663667}
0.014	15.0	5295	1.3392	{'accuracy': 0.8407714734245346}	{'f1': 0.8405188286141088}
0.0111	16.0	5648	1.2977	{'accuracy': 0.8443597219107423}	{'f1': 0.8438293082262649}
0.0099	17.0	6001	1.3405	{'accuracy': 0.8412200044853106}	{'f1': 0.8400992068548403}
0.0099	18.0	6354	1.3433	{'accuracy': 0.8405472078941467}	{'f1': 0.839917724407298}
0.0041	19.0	6707	1.3269	{'accuracy': 0.8445839874411303}	{'f1': 0.8434224071770644}
0.0041	20.0	7060	1.3253	{'accuracy': 0.8445839874411303}	{'f1': 0.8435559601445874}

Framework versions

—Transformers 4.35.2
—Pytorch 2.1.0+cu121
—Datasets 2.16.1
—Tokenizers 0.15.1