sewd-classifier-aug-ref

This model is a fine-tuned version of asapp/sew-d-tiny-100k-ft-ls100h on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.2210
• Accuracy: 0.6402
• Precision: 0.6291
• Recall: 0.6402
• F1: 0.6137
• Binary: 0.7478

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: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• 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	Binary
No log	0.13	50	4.3839	0.0337	0.0029	0.0337	0.0052	0.2004
No log	0.27	100	4.1179	0.0687	0.0353	0.0687	0.0281	0.3295
No log	0.4	150	3.8682	0.0889	0.0335	0.0889	0.0338	0.3523
No log	0.54	200	3.6594	0.0997	0.0311	0.0997	0.0366	0.3635
No log	0.67	250	3.5084	0.1280	0.0534	0.1280	0.0494	0.3838
No log	0.81	300	3.3946	0.1469	0.0476	0.1469	0.0628	0.3964
No log	0.94	350	3.2570	0.1604	0.0699	0.1604	0.0830	0.4082
3.9151	1.08	400	3.1540	0.1806	0.0844	0.1806	0.0987	0.4224
3.9151	1.21	450	3.0449	0.1846	0.1000	0.1846	0.1005	0.4260
3.9151	1.35	500	2.9543	0.2237	0.1403	0.2237	0.1376	0.4534
3.9151	1.48	550	2.8691	0.2507	0.1621	0.2507	0.1606	0.4706
3.9151	1.62	600	2.7812	0.2493	0.1520	0.2493	0.1592	0.4718
3.9151	1.75	650	2.6598	0.2871	0.1855	0.2871	0.1856	0.4981
3.9151	1.89	700	2.6099	0.2951	0.2123	0.2951	0.2019	0.5047
3.1406	2.02	750	2.5039	0.3235	0.2106	0.3235	0.2230	0.5236
3.1406	2.16	800	2.4359	0.3383	0.2454	0.3383	0.2501	0.5358
3.1406	2.29	850	2.3869	0.3154	0.2329	0.3154	0.2324	0.5179
3.1406	2.43	900	2.3144	0.3612	0.2937	0.3612	0.2798	0.5513
3.1406	2.56	950	2.2470	0.3720	0.3122	0.3720	0.2908	0.5584
3.1406	2.7	1000	2.1944	0.3774	0.3099	0.3774	0.2992	0.5632
3.1406	2.83	1050	2.1421	0.4030	0.3250	0.4030	0.3226	0.5819
3.1406	2.97	1100	2.0630	0.4137	0.3442	0.4137	0.3336	0.5899
2.6974	3.1	1150	2.0115	0.4245	0.3679	0.4245	0.3510	0.5974
2.6974	3.24	1200	1.9716	0.4434	0.3964	0.4434	0.3729	0.6093
2.6974	3.37	1250	1.9255	0.4488	0.3972	0.4488	0.3883	0.6150
2.6974	3.51	1300	1.8715	0.4623	0.4112	0.4623	0.3969	0.6228
2.6974	3.64	1350	1.8223	0.4825	0.4534	0.4825	0.4222	0.6369
2.6974	3.78	1400	1.7951	0.5013	0.4728	0.5013	0.4500	0.6511
2.6974	3.91	1450	1.7427	0.5270	0.4855	0.5270	0.4804	0.6686
2.3963	4.05	1500	1.7319	0.5	0.4618	0.5	0.4452	0.6493
2.3963	4.18	1550	1.7098	0.4960	0.4588	0.4960	0.4454	0.6473
2.3963	4.32	1600	1.6518	0.5310	0.5051	0.5310	0.4855	0.6709
2.3963	4.45	1650	1.6535	0.5067	0.4838	0.5067	0.4552	0.6539
2.3963	4.59	1700	1.6011	0.5539	0.5106	0.5539	0.5061	0.6865
2.3963	4.72	1750	1.5894	0.5404	0.4940	0.5404	0.4923	0.6767
2.3963	4.86	1800	1.5580	0.5660	0.5371	0.5660	0.5285	0.6964
2.3963	4.99	1850	1.5375	0.5431	0.5032	0.5431	0.4968	0.6803
2.1926	5.12	1900	1.5166	0.5620	0.5237	0.5620	0.5193	0.6941
2.1926	5.26	1950	1.5168	0.5526	0.5198	0.5526	0.5085	0.6860
2.1926	5.39	2000	1.4773	0.5836	0.5615	0.5836	0.5455	0.7073
2.1926	5.53	2050	1.4488	0.5782	0.5564	0.5782	0.5396	0.7054
2.1926	5.66	2100	1.4335	0.5916	0.5691	0.5916	0.5560	0.7143
2.1926	5.8	2150	1.4078	0.5957	0.5782	0.5957	0.5641	0.7177
2.1926	5.93	2200	1.4092	0.5863	0.5691	0.5863	0.5506	0.7105
2.0446	6.07	2250	1.3942	0.5755	0.5405	0.5755	0.5334	0.7026
2.0446	6.2	2300	1.3828	0.5930	0.5776	0.5930	0.5613	0.7148
2.0446	6.34	2350	1.3625	0.6065	0.5886	0.6065	0.5688	0.7247
2.0446	6.47	2400	1.3444	0.6119	0.6008	0.6119	0.5754	0.7284
2.0446	6.61	2450	1.3088	0.6267	0.6134	0.6267	0.5914	0.7388
2.0446	6.74	2500	1.3183	0.6038	0.5869	0.6038	0.5729	0.7228
2.0446	6.88	2550	1.3000	0.6173	0.5886	0.6173	0.5810	0.7322
1.9441	7.01	2600	1.2930	0.6213	0.6048	0.6213	0.5900	0.7341
1.9441	7.15	2650	1.2757	0.6226	0.6097	0.6226	0.5959	0.7361
1.9441	7.28	2700	1.2787	0.6226	0.6091	0.6226	0.5963	0.7361
1.9441	7.42	2750	1.2566	0.6240	0.6204	0.6240	0.5983	0.7375
1.9441	7.55	2800	1.2549	0.6253	0.6055	0.6253	0.5970	0.7380
1.9441	7.69	2850	1.2396	0.6240	0.6255	0.6240	0.5954	0.7371
1.9441	7.82	2900	1.2400	0.6388	0.6361	0.6388	0.6128	0.7478
1.9441	7.96	2950	1.2369	0.6294	0.6188	0.6294	0.5996	0.7407
1.8636	8.09	3000	1.2235	0.6375	0.6363	0.6375	0.6151	0.7460
1.8636	8.23	3050	1.2178	0.6456	0.6415	0.6456	0.6196	0.7520
1.8636	8.36	3100	1.2093	0.6402	0.6346	0.6402	0.6149	0.7482
1.8636	8.5	3150	1.2210	0.6402	0.6291	0.6402	0.6137	0.7478

Framework versions

• Transformers 4.38.2
• Pytorch 2.3.0
• Datasets 2.19.1
• Tokenizers 0.15.1