absa

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: 1.0417
• Accuracy: 0.8779
• F1: 0.7493

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.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	F1
0.4543	1.0	298	0.3298	0.8707	0.6817
0.2805	2.0	596	0.3210	0.8798	0.7341
0.208	3.0	894	0.3442	0.8841	0.7757
0.167	4.0	1192	0.3991	0.8841	0.7528
0.131	5.0	1490	0.4410	0.8798	0.7546
0.1039	6.0	1788	0.5180	0.8784	0.7626
0.0863	7.0	2086	0.5390	0.8793	0.7588
0.073	8.0	2384	0.5781	0.8702	0.7600
0.0651	9.0	2682	0.5738	0.875	0.7503
0.0535	10.0	2980	0.5999	0.8755	0.7501
0.0389	11.0	3278	0.6803	0.8760	0.7553
0.0385	12.0	3576	0.7202	0.8836	0.7301
0.0388	13.0	3874	0.7378	0.8774	0.7323
0.0324	14.0	4172	0.8089	0.8740	0.7334
0.0278	15.0	4470	0.7891	0.8769	0.7540
0.0224	16.0	4768	0.8061	0.8740	0.7448
0.022	17.0	5066	0.8205	0.8716	0.7492
0.0208	18.0	5364	0.7715	0.8716	0.7271
0.0184	19.0	5662	0.8142	0.8803	0.7440
0.0173	20.0	5960	0.8908	0.8764	0.7480
0.0149	21.0	6258	0.8814	0.8731	0.7427
0.0145	22.0	6556	0.8972	0.8784	0.7416
0.0161	23.0	6854	0.8861	0.8736	0.7395
0.0164	24.0	7152	0.9344	0.8736	0.7498
0.0168	25.0	7450	0.9008	0.8740	0.7466
0.0147	26.0	7748	0.9498	0.8769	0.7456
0.0124	27.0	8046	0.9168	0.8712	0.7254
0.0128	28.0	8344	0.9192	0.8774	0.7362
0.0138	29.0	8642	0.9745	0.8788	0.7612
0.0125	30.0	8940	0.9276	0.8784	0.7455
0.0118	31.0	9238	1.0205	0.8707	0.7552
0.0123	32.0	9536	0.9628	0.8764	0.7486
0.0139	33.0	9834	1.0042	0.8745	0.7541
0.0126	34.0	10132	0.9834	0.8760	0.7461
0.013	35.0	10430	0.9986	0.8769	0.7450
0.0134	36.0	10728	0.9907	0.8788	0.7490
0.0135	37.0	11026	1.0038	0.8736	0.7458
0.0121	38.0	11324	1.0175	0.8740	0.7476
0.0122	39.0	11622	1.0053	0.8755	0.7499
0.0112	40.0	11920	1.0120	0.8784	0.7467
0.0115	41.0	12218	1.0084	0.8764	0.7448
0.0129	42.0	12516	1.0021	0.8798	0.7491
0.0107	43.0	12814	1.0105	0.8784	0.7476
0.0108	44.0	13112	1.0131	0.8774	0.7454
0.0114	45.0	13410	1.0363	0.875	0.7504
0.0115	46.0	13708	1.0333	0.8798	0.7553
0.0106	47.0	14006	1.0297	0.8788	0.7500
0.0102	48.0	14304	1.0378	0.8779	0.7494
0.01	49.0	14602	1.0414	0.8769	0.7469
0.0107	50.0	14900	1.0417	0.8779	0.7493

Framework versions

• Transformers 4.50.2
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1