sewd-classifier-aug

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: 1.3679
• Accuracy: 0.6092
• Precision: 0.6385
• Recall: 0.6092
• F1: 0.5780
• Binary: 0.7259

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	0.19	50	4.3617	0.0431	0.0054	0.0431	0.0091	0.2267
No log	0.38	100	4.1063	0.0593	0.0134	0.0593	0.0187	0.3046
No log	0.58	150	3.8795	0.1105	0.0541	0.1105	0.0502	0.3553
No log	0.77	200	3.6851	0.1294	0.0368	0.1294	0.0511	0.3768
No log	0.96	250	3.5025	0.2022	0.1342	0.2022	0.1289	0.4337
4.0829	1.15	300	3.3027	0.2049	0.1255	0.2049	0.1208	0.4394
4.0829	1.34	350	3.1674	0.2291	0.1067	0.2291	0.1305	0.4571
4.0829	1.53	400	3.0183	0.2453	0.1576	0.2453	0.1515	0.4677
4.0829	1.73	450	2.9047	0.2830	0.1807	0.2830	0.1898	0.4949
4.0829	1.92	500	2.7836	0.3181	0.2598	0.3181	0.2400	0.5194
3.3139	2.11	550	2.6784	0.3396	0.2432	0.3396	0.2484	0.5345
3.3139	2.3	600	2.5843	0.3261	0.2363	0.3261	0.2332	0.5259
3.3139	2.49	650	2.5050	0.3288	0.2625	0.3288	0.2465	0.5286
3.3139	2.68	700	2.3782	0.3531	0.3019	0.3531	0.2844	0.5456
3.3139	2.88	750	2.2826	0.4124	0.3926	0.4124	0.3473	0.5871
2.8692	3.07	800	2.2188	0.4151	0.3627	0.4151	0.3390	0.5889
2.8692	3.26	850	2.1541	0.4124	0.3370	0.4124	0.3348	0.5871
2.8692	3.45	900	2.0925	0.4016	0.3462	0.4016	0.3324	0.5787
2.8692	3.64	950	2.0181	0.4286	0.3550	0.4286	0.3612	0.5984
2.8692	3.84	1000	1.9575	0.4663	0.4514	0.4663	0.4074	0.6248
2.5712	4.03	1050	1.9088	0.4771	0.4544	0.4771	0.4229	0.6323
2.5712	4.22	1100	1.8500	0.4906	0.4284	0.4906	0.4235	0.6418
2.5712	4.41	1150	1.8270	0.4852	0.4312	0.4852	0.4222	0.6380
2.5712	4.6	1200	1.7758	0.4852	0.4556	0.4852	0.4306	0.6380
2.5712	4.79	1250	1.7430	0.4933	0.4552	0.4933	0.4388	0.6437
2.5712	4.99	1300	1.7188	0.5067	0.4941	0.5067	0.4624	0.6531
2.3754	5.18	1350	1.6813	0.5148	0.4820	0.5148	0.4668	0.6580
2.3754	5.37	1400	1.6463	0.5472	0.5302	0.5472	0.5029	0.6806
2.3754	5.56	1450	1.6446	0.5256	0.5247	0.5256	0.4801	0.6655
2.3754	5.75	1500	1.6005	0.5660	0.5579	0.5660	0.5235	0.6930
2.3754	5.94	1550	1.5667	0.5795	0.5561	0.5795	0.5363	0.7032
2.234	6.14	1600	1.5397	0.5741	0.5389	0.5741	0.5291	0.6995
2.234	6.33	1650	1.5235	0.5687	0.5444	0.5687	0.5219	0.6957
2.234	6.52	1700	1.5045	0.5849	0.5699	0.5849	0.5376	0.7070
2.234	6.71	1750	1.4936	0.5822	0.5635	0.5822	0.5383	0.7059
2.234	6.9	1800	1.4688	0.5822	0.5736	0.5822	0.5401	0.7059
2.1257	7.09	1850	1.4533	0.5957	0.6042	0.5957	0.5536	0.7154
2.1257	7.29	1900	1.4233	0.6038	0.6097	0.6038	0.5675	0.7202
2.1257	7.48	1950	1.4267	0.6038	0.6224	0.6038	0.5721	0.7210
2.1257	7.67	2000	1.4075	0.6146	0.6310	0.6146	0.5804	0.7278
2.1257	7.86	2050	1.3887	0.5984	0.6178	0.5984	0.5610	0.7173
2.0636	8.05	2100	1.3679	0.6092	0.6385	0.6092	0.5780	0.7259

Framework versions

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