trocr-base-handwritten

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

• Loss: 1.4825
• Cer: 0.1647
• Wer: 0.3687
• Penalized Wer: 0.3822
• Combined: 0.2735

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 2026
• optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.05
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer	Penalized Wer	Combined
8.3684	1.0	116	7.7234	0.5314	0.8450	0.8862	0.7088
6.1191	2.0	232	4.0425	0.4222	0.7276	0.7603	0.5912
3.5055	3.0	348	3.0009	0.3198	0.6157	0.6434	0.4816
2.7962	4.0	464	2.6273	0.2876	0.5739	0.6002	0.4439
2.5346	5.0	580	2.4117	0.2671	0.5368	0.5581	0.4126
2.3641	6.0	696	2.2578	0.2518	0.5180	0.5396	0.3957
2.2352	7.0	812	2.1378	0.2388	0.4889	0.5075	0.3731
2.1021	8.0	928	2.0344	0.2263	0.4705	0.4882	0.3572
1.9916	9.0	1044	1.9547	0.2186	0.4551	0.4724	0.3455
1.8868	10.0	1160	1.8899	0.2096	0.4447	0.4627	0.3362
1.8350	11.0	1276	1.8264	0.2030	0.4330	0.4504	0.3267
1.7901	12.0	1392	1.7776	0.1966	0.4228	0.4386	0.3176
1.7575	13.0	1508	1.7373	0.1918	0.4122	0.4282	0.3100
1.6409	14.0	1624	1.6968	0.1866	0.4094	0.4261	0.3064
1.6370	15.0	1740	1.6660	0.1835	0.4066	0.4223	0.3029
1.5953	16.0	1856	1.6408	0.1806	0.4003	0.4156	0.2981
1.5737	17.0	1972	1.6150	0.1787	0.3960	0.4111	0.2949
1.5381	18.0	2088	1.5917	0.1768	0.3925	0.4078	0.2923
1.5058	19.0	2204	1.5737	0.1740	0.3840	0.3977	0.2859
1.4760	20.0	2320	1.5564	0.1707	0.3819	0.3954	0.2831
1.4345	21.0	2436	1.5408	0.1718	0.3786	0.3924	0.2821
1.4309	22.0	2552	1.5288	0.1694	0.3793	0.3930	0.2812
1.4147	23.0	2668	1.5168	0.1681	0.3773	0.3922	0.2801
1.3955	24.0	2784	1.5072	0.1686	0.3743	0.3885	0.2785
1.4017	25.0	2900	1.5017	0.1661	0.3736	0.3876	0.2769
1.4114	26.0	3016	1.4926	0.1658	0.3708	0.3847	0.2753
1.4293	27.0	3132	1.4886	0.1639	0.3702	0.3838	0.2738
1.3707	28.0	3248	1.4852	0.1657	0.3713	0.3850	0.2753
1.3794	29.0	3364	1.4832	0.1638	0.3663	0.3797	0.2718
1.3586	30.0	3480	1.4825	0.1647	0.3687	0.3822	0.2735

Framework versions

• PEFT 0.19.1
• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 5.0.0
• Tokenizers 0.22.2