Khoivudang1209/abte-restaurants-sentiment-conv1d

abte-restaurants-setiment-conv1d

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

• Loss: 0.7528
• Accuracy: 0.6649

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: 256
• eval_batch_size: 256
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0185	1.0	15	0.8908	0.6497
0.9677	2.0	30	0.8715	0.6497
0.9579	3.0	45	0.8620	0.6497
0.9201	4.0	60	0.8513	0.6506
0.898	5.0	75	0.8398	0.6506
0.8778	6.0	90	0.8292	0.6497
0.8539	7.0	105	0.8232	0.6515
0.8362	8.0	120	0.8178	0.6515
0.8267	9.0	135	0.8129	0.6524
0.811	10.0	150	0.8089	0.6524
0.8196	11.0	165	0.8044	0.6533
0.7855	12.0	180	0.8008	0.6524
0.8072	13.0	195	0.7963	0.6524
0.7657	14.0	210	0.7941	0.6524
0.7483	15.0	225	0.7896	0.6533
0.7403	16.0	240	0.7883	0.6559
0.753	17.0	255	0.7850	0.6559
0.7162	18.0	270	0.7830	0.6559
0.7131	19.0	285	0.7804	0.6559
0.7158	20.0	300	0.7782	0.6559
0.7017	21.0	315	0.7765	0.6542
0.7061	22.0	330	0.7747	0.6533
0.6816	23.0	345	0.7740	0.6568
0.6864	24.0	360	0.7715	0.6559
0.6941	25.0	375	0.7695	0.6559
0.6894	26.0	390	0.7679	0.6559
0.6661	27.0	405	0.7671	0.6568
0.6587	28.0	420	0.7661	0.6568
0.663	29.0	435	0.7642	0.6586
0.6674	30.0	450	0.7627	0.6613
0.6645	31.0	465	0.7617	0.6622
0.6491	32.0	480	0.7607	0.6622
0.6607	33.0	495	0.7603	0.6649
0.6461	34.0	510	0.7594	0.6640
0.6373	35.0	525	0.7587	0.6649
0.6469	36.0	540	0.7583	0.6640
0.6321	37.0	555	0.7578	0.6622
0.6255	38.0	570	0.7567	0.6640
0.6306	39.0	585	0.7559	0.6649
0.6249	40.0	600	0.7553	0.6649
0.6345	41.0	615	0.7548	0.6649
0.6255	42.0	630	0.7545	0.6649
0.6248	43.0	645	0.7542	0.6649
0.6232	44.0	660	0.7536	0.6649
0.6113	45.0	675	0.7534	0.6649
0.6273	46.0	690	0.7532	0.6649
0.6217	47.0	705	0.7530	0.6649
0.6385	48.0	720	0.7528	0.6649
0.6211	49.0	735	0.7528	0.6649
0.6125	50.0	750	0.7528	0.6649

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0