You can deploy this model and start earning money today!

danush99/
checkPoints

Name: checkPoints
Rating: 2.5 (7 reviews)

transformers

safetensors

vision-encoder-decoder

image-text-to-text

generated_from_trainer

danush99/Model_TrOCR-Sin-Printed-Text

finetune

endpoints_compatible

Views

No views yet

checkPoints

This model is a fine-tuned version of danush99/Model_TrOCR-Sin-Printed-Text on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.2322
Cer: 0.5629

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: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
2.2319	1.7544	100	1.7853	0.7027
0.6266	3.5088	200	1.8948	0.7267
0.2727	5.2632	300	1.9236	0.6485
0.1861	7.0175	400	2.0634	0.6453
0.1811	8.7719	500	2.0956	0.6463
0.1337	10.5263	600	2.2578	0.6644
0.0759	12.2807	700	2.5696	0.7128
0.0797	14.0351	800	2.1449	0.6458
0.0942	15.7895	900	2.1767	0.6299
0.0425	17.5439	1000	2.5660	0.6639
0.0699	19.2982	1100	2.4545	0.6781
0.0707	21.0526	1200	2.7097	0.6925
0.0577	22.8070	1300	2.8215	0.7074
0.0281	24.5614	1400	2.4110	0.7004
0.0336	26.3158	1500	2.3586	0.6528
0.0359	28.0702	1600	2.2111	0.6103
0.0128	29.8246	1700	2.3535	0.6307
0.0560	31.5789	1800	2.3196	0.6399
0.0211	33.3333	1900	2.5897	0.6570
0.0136	35.0877	2000	2.5756	0.7019
0.0039	36.8421	2100	2.9723	0.6602
0.0123	38.5965	2200	2.9204	0.6374
0.0573	40.3509	2300	2.4419	0.6508
0.0178	42.1053	2400	2.3078	0.6138
0.0341	43.8596	2500	2.6973	0.6691
0.0075	45.6140	2600	2.4838	0.6530
0.0176	47.3684	2700	3.2690	0.6649
0.0008	49.1228	2800	3.2363	0.6612
0.0043	50.8772	2900	2.6300	0.6441
0.0031	52.6316	3000	2.7526	0.6505
0.0026	54.3860	3100	2.5666	0.6247
0.0005	56.1404	3200	2.7527	0.6369
0.0009	57.8947	3300	2.6842	0.6329
0.0007	59.6491	3400	2.6928	0.6240
0.0138	61.4035	3500	3.2250	0.6513
0.0009	63.1579	3600	2.4138	0.6451
0.0008	64.9123	3700	2.2832	0.6019
0.0010	66.6667	3800	2.2619	0.5974
0.0003	68.4211	3900	3.0282	0.6054
0.0014	70.1754	4000	2.6130	0.6215
0.0003	71.9298	4100	2.4099	0.5805
0.0004	73.6842	4200	2.5573	0.6086
0.0150	75.4386	4300	2.8885	0.6210
0.0016	77.1930	4400	2.4898	0.5994
0.0002	78.9474	4500	2.7552	0.6399
0.0004	80.7018	4600	2.4722	0.5967
0.0004	82.4561	4700	2.3909	0.6006
0.0003	84.2105	4800	2.5311	0.6029
0.0002	85.9649	4900	2.6945	0.5947
0.0002	87.7193	5000	2.2324	0.5644
0.0002	89.4737	5100	2.2411	0.5862
0.0002	91.2281	5200	2.5429	0.6237
0.0002	92.9825	5300	2.3281	0.6059
0.0001	94.7368	5400	2.3460	0.5902
0.0002	96.4912	5500	2.2796	0.5825
0.0001	98.2456	5600	2.2379	0.5830
0.0011	100.0	5700	2.2377	0.5825

Framework versions

Transformers 5.2.0
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.2

danush99/checkPoints

checkPoints

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

danush99/
checkPoints